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Office of Scientific and Technical Information
High-Dose Dosimetry Uncertainty Tables
Science Conference Proceedings (OSTI)
... 4.0. Alanine Transfer - E-Beam, Uncertainty Source, Type A (%), Type B (%). ... Calibration Curve, 0.50, 0.10. Electron Energy Dependence, 0.10. ...
Uncertainties in Gapped Graphene
E-Print Network (OSTI)
Motivated by graphene-based quantum computer we examine the time-dependence of the position-momentum and position-velocity uncertainties in the monolayer gapped graphene. The effect of the energy gap to the uncertainties is shown to appear via the Compton-like wavelength $\\lambda_c$. The uncertainties in the graphene are mainly contributed by two phenomena, spreading and zitterbewegung. While the former determines the uncertainties in the long-range of time, the latter gives the highly oscillation to the uncertainties in the short-range of time. The uncertainties in the graphene are compared with the corresponding values for the usual free Hamiltonian $\\hat{H}_{free} = (p_1^2 + p_2^2) / 2 M$. It is shown that the uncertainties can be under control within the quantum mechanical law if one can choose the gap parameter $\\lambda_c$ freely.
Eylee Jung; Kwang S. Kim; DaeKil Park
Uncertainty analysis
SciTech Connect
An evaluation is made of the suitability of analytical and statistical sampling methods for making uncertainty analyses. The adjoint method is found to be well-suited for obtaining sensitivity coefficients for computer programs involving large numbers of equations and input parameters. For this purpose the Latin Hypercube Sampling method is found to be inferior to conventional experimental designs. The Latin hypercube method can be used to estimate output probability density functions, but requires supplementary rank transformations followed by stepwise regression to obtain uncertainty information on individual input parameters. A simple Cork and Bottle problem is used to illustrate the efficiency of the adjoint method relative to certain statistical sampling methods. For linear models of the form Ax=b it is shown that a complete adjoint sensitivity analysis can be made without formulating and solving the adjoint problem. This can be done either by using a special type of statistical sampling or by reformulating the primal problem and using suitable linear programming software.
Thomas, R.E.
Uncertainty of calorimeter measurements at NREL's high flux solar furnace
DOE Green Energy (OSTI)
The uncertainties of the calorimeter and concentration measurements at the High Flux Solar Furnace (HFSF) at the National Renewable Energy Laboratory (NREL) are discussed. Two calorimeter types have been used to date. One is an array of seven commercially available circular foil calorimeters (gardon or heat flux gages) for primary concentrator peak flux (up to 250 W/cm{sup 2}). The second is a cold-water calorimeter designed and built by the University of Chicago to measure the average exit power of the reflective compound parabolic secondary concentrator used at the HFSF (over 3.3 kW across a 1.6cm{sup {minus}2} exit aperture, corresponding to a flux of about 2 kW/cm{sup 2}). This paper discussed the uncertainties of the calorimeter and pyrheliometer measurements and resulting concentration calculations. The measurement uncertainty analysis is performed according to the ASME/ANSI standard PTC 19.1 (1985). Random and bias errors for each portion of the measurement are analyzed. The results show that as either the power or the flux is reduced, the uncertainties increase. Another calorimeter is being designed for a new, refractive secondary which will use a refractive material to produce a higher average flux (5 kW/cm{sup 2}) than the reflective secondary. The new calorimeter will use a time derivative of the fluid temperature as a key measurement of the average power out of the secondary. A description of this calorimeter and test procedure is also presented, along with a pre-test estimate of major sources of uncertainty. 8 refs., 4 figs., 3 tabs.
Bingham, C.E.
The uncertainties in estimating measurement uncertainties
All measurements include some error. Whether measurements are used for accountability, environmental programs or process support, they are of little value unless accompanied by an estimate of the measurements uncertainty. This fact is often overlooked by the individuals who need measurements to make decisions. This paper will discuss the concepts of measurement, measurements errors (accuracy or bias and precision or random error), physical and error models, measurement control programs, examples of measurement uncertainty, and uncertainty as related to measurement quality. Measurements are comparisons of unknowns to knowns, estimates of some true value plus uncertainty; and are no better than the standards to which they are compared. Direct comparisons of unknowns that match the composition of known standards will normally have small uncertainties. In the real world, measurements usually involve indirect comparisons of significantly different materials (e.g., measuring a physical property of a chemical element in a sample having a matrix that is significantly different from calibration standards matrix). Consequently, there are many sources of error involved in measurement processes that can affect the quality of a measurement and its associated uncertainty. How the uncertainty estimates are determined and what they mean is as important as the measurement. The process of calculating the uncertainty of a measurement itself has uncertainties that must be handled correctly. Examples of chemistry laboratory measurement will be reviewed in this report and recommendations made for improving measurement uncertainties.
Clark, J.P.; Shull, A.H.
Sensitivity and Uncertainty Analysis
Energy.gov (U.S. Department of Energy (DOE))
Summary Notes from 15 November 2007 Generic Technical Issue Discussion on Sensitivity and Uncertainty Analysis and Model Support
Basic definitions of uncertainty
... Glossary. The following definitions are given in the ISO Guide to the Expression of Uncertainty in Measurement. Many additional ...
Uncertainty Machine — User's Manual
... Figure 8: Resistance — Densities. ... Supplement 1 to the "Guide to the expression of uncertainty in measurement" — Propagation of distributions ...
Impacts of Sea Surface Temperature Uncertainty on the Western North Pacific Subtropical High (WNPSH) and Rainfall
This paper examines the sensitivity of short-term forecasts of the western North Pacific subtropical high (WNPSH) and rainfall to sea surface temperature (SST) uncertainty using the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS). A ...
Xiaodong Hong; Craig H. Bishop; Teddy Holt; Larry O'Neill
Characteristic uncertainty relations
New uncertainty relations for n observables are established. The relations take the invariant form of inequalities between the characteristic coefficients of order r, r = 1,2,...,n, of the uncertainty matrix and the matrix of mean commutators of the observables. It is shown that the second and the third order characteristic inequalities for the three generators of SU(1,1) and SU(2) are minimized in the corresponding group-related coherent states with maximal symmetry.
D. A. Trifonov; S. G. Donev
Uncertainty and calibration analysis
All measurements contain some deviation from the true value which is being measured. In the common vernacular this deviation between the true value and the measured value is called an inaccuracy, an error, or a mistake. Since all measurements contain errors, it is necessary to accept that there is a limit to how accurate a measurement can be. The undertainty interval combined with the confidence level, is one measure of the accuracy for a measurement or value. Without a statement of uncertainty (or a similar parameter) it is not possible to evaluate if the accuracy of the measurement, or data, is appropriate. The preparation of technical reports, calibration evaluations, and design calculations should consider the accuracy of measurements and data being used. There are many methods to accomplish this. This report provides a consistent method for the handling of measurement tolerances, calibration evaluations and uncertainty calculations. The SRS Quality Assurance (QA) Program requires that the uncertainty of technical data and instrument calibrations be acknowledged and estimated. The QA Program makes some specific technical requirements related to the subject but does not provide a philosophy or method on how uncertainty should be estimated. This report was prepared to provide a technical basis to support the calculation of uncertainties and the calibration of measurement and test equipment for any activity within the Experimental Thermal-Hydraulics (ETH) Group. The methods proposed in this report provide a graded approach for estimating the uncertainty of measurements, data, and calibrations. The method is based on the national consensus standard, ANSI/ASME PTC 19.1.
Coutts, D.A.
Numerical approach for quantification of epistemic uncertainty
In the field of uncertainty quantification, uncertainty in the governing equations may assume two forms: aleatory uncertainty and epistemic uncertainty. Aleatory uncertainty can be characterised by known probability distributions whilst epistemic uncertainty ... Keywords: Encapsulation problem, Epistemic uncertainty, Generalized polynomial chaos, Stochastic collocation, Uncertainty quantification
John Jakeman; Michael Eldred; Dongbin Xiu
It is difficult to overestimate the importance that physical measurements performed with nondestructive assay instruments play throughout the nuclear fuel cycle. They underpin decision making in many areas and support: criticality safety, radiation protection, process control, safeguards, facility compliance, and waste measurements. No physical measurement is complete or indeed meaningful, without a defensible and appropriate accompanying statement of uncertainties and how they combine to define the confidence in the results. The uncertainty budget should also be broken down in sufficient detail suitable for subsequent uses to which the nondestructive assay (NDA) results will be applied. Creating an uncertainty budget and estimating the total measurement uncertainty can often be an involved process, especially for non routine situations. This is because data interpretation often involves complex algorithms and logic combined in a highly intertwined way. The methods often call on a multitude of input data subject to human oversight. These characteristics can be confusing and pose a barrier to developing and understanding between experts and data consumers. ASTM subcommittee C26-10 recognized this problem in the context of how to summarize and express precision and bias performance across the range of standards and guides it maintains. In order to create a unified approach consistent with modern practice and embracing the continuous improvement philosophy a consensus arose to prepare a procedure covering the estimation and reporting of uncertainties in non destructive assay of nuclear materials. This paper outlines the needs analysis, objectives and on-going development efforts. In addition to emphasizing some of the unique challenges and opportunities facing the NDA community we hope this article will encourage dialog and sharing of best practice and furthermore motivate developers to revisit the treatment of measurement uncertainty.
Salaymeh, S.; Ashley, W.; Jeffcoat, R.
Electoral Competition, Political Uncertainty and Policy Insulation
Uncertainty and Policy Insulation Horn, Murray. 1995. TheUncertainty and Policy Insulation United States Congress.UNCERTAINTY AND POLICY INSULATION Rui J. P. de Figueiredo,
de Figueiredo, Rui J. P. Jr.
Bayesian uncertainty quantification and propagation in molecular dynamics simulations: A high performance computing framework
We present a Bayesian probabilistic framework for quantifying and propagating the uncertainties in the parameters of force fields employed in molecular dynamics (MD) simulations. We propose a highly parallel implementation of the transitional Markov chain Monte Carlo for populating the posterior probability distribution of the MD force-field parameters. Efficient scheduling algorithms are proposed to handle the MD model runs and to distribute the computations in clusters with heterogeneous architectures. Furthermore
Panagiotis Angelikopoulos; Costas Papadimitriou; Petros Koumoutsakos
Demand Uncertainty and Price Dispersion.
??Demand uncertainty has been recognized as one factor that may cause price dispersion in perfectly competitive markets with costly and perishable capacity. With the persistence… (more)
Li, Suxi
Uncertainty in Integrated Assessment Scenarios
The determination of climate policy is a decision under uncertainty. The uncertainty in future climate change impacts is large, as is the uncertainty in the costs of potential policies. Rational and economically efficient policy choices will therefore seek to balance the expected marginal costs with the expected marginal benefits. This approach requires that the risks of future climate change be assessed. The decision process need not be formal or quantitative for descriptions of the risks to be useful. Whatever the decision procedure, a useful starting point is to have as accurate a description of climate risks as possible. Given the goal of describing uncertainty in future climate change, we need to characterize the uncertainty in the main causes of uncertainty in climate impacts. One of the major drivers of uncertainty in future climate change is the uncertainty in future emissions, both of greenhouse gases and other radiatively important species such as sulfur dioxide. In turn, the drivers of uncertainty in emissions are uncertainties in the determinants of the rate of economic growth and in the technologies of production and how those technologies will change over time. This project uses historical experience and observations from a large number of countries to construct statistical descriptions of variability and correlation in labor productivity growth and in AEEI. The observed variability then provides a basis for constructing probability distributions for these drivers. The variance of uncertainty in growth rates can be further modified by expert judgment if it is believed that future variability will differ from the past. But often, expert judgment is more readily applied to projected median or expected paths through time. Analysis of past variance and covariance provides initial assumptions about future uncertainty for quantities that are less intuitive and difficult for experts to estimate, and these variances can be normalized and then applied to mean trends from a model for uncertainty projections. The probability distributions of these critical model drivers, and the resulting uncertainty in projections from a range of models, can provide the basis of future emission scenario set designs.
Mort Webster
Quantifying uncertainty from material inhomogeneity.
Most engineering materials are inherently inhomogeneous in their processing, internal structure, properties, and performance. Their properties are therefore statistical rather than deterministic. These inhomogeneities manifest across multiple length and time scales, leading to variabilities, i.e. statistical distributions, that are necessary to accurately describe each stage in the process-structure-properties hierarchy, and are ultimately the primary source of uncertainty in performance of the material and component. When localized events are responsible for component failure, or when component dimensions are on the order of microstructural features, this uncertainty is particularly important. For ultra-high reliability applications, the uncertainty is compounded by a lack of data describing the extremely rare events. Hands-on testing alone cannot supply sufficient data for this purpose. To date, there is no robust or coherent method to quantify this uncertainty so that it can be used in a predictive manner at the component length scale. The research presented in this report begins to address this lack of capability through a systematic study of the effects of microstructure on the strain concentration at a hole. To achieve the strain concentration, small circular holes (approximately 100 {micro}m in diameter) were machined into brass tensile specimens using a femto-second laser. The brass was annealed at 450 C, 600 C, and 800 C to produce three hole-to-grain size ratios of approximately 7, 1, and 1/7. Electron backscatter diffraction experiments were used to guide the construction of digital microstructures for finite element simulations of uniaxial tension. Digital image correlation experiments were used to qualitatively validate the numerical simulations. The simulations were performed iteratively to generate statistics describing the distribution of plastic strain at the hole in varying microstructural environments. In both the experiments and simulations, the deformation behavior was found to depend strongly on the character of the nearby microstructure.
Battaile, Corbett Chandler; Emery, John M.; Brewer, Luke N.; Boyce, Brad Lee
Uncertainty in emissions projections for climate models
Future global climate projections are subject to large uncertainties. Major sources of this uncertainty are projections of anthropogenic emissions. We evaluate the uncertainty in future anthropogenic emissions using a ...
Webster, Mort David.; Babiker, Mustafa H.M.; Mayer, Monika.; Reilly, John M.; Harnisch, Jochen.; Hyman, Robert C.; Sarofim, Marcus C.; Wang, Chien.
Autonomous Exploration: Driven by Uncertainty
Autonomous Exploration: Driven by Uncertainty Peter Whaite and Frank P. Ferrie TR-CIM-93-17 1993-6319 Telex: 05 268510 FAX: 514 398-7348 Email: firstname.lastname@example.com #12;Autonomous Exploration: Driven
Dudek, Gregory
Bayesian calibration Uncertainty Sensitivity analysis
available at www.sciencedirect.com journal homepage: www.elsevier.com/locate/ecolmodel Sensitivity and uncertainty analysis from a coupled 3-PG and soil organic matter decomposition model
Monte Carlo; Markov Chain
Uncertainty in Air Quality Modeling
Under the direction of the AMS Steering Committee for the EPA Cooperative Agreement on Air Quality Modeling, a small group of scientists convened to consider the question of uncertainty in air quality modeling. Because the group was particularly ...
Douglas G. Fox
Harvesting a renewable resource under uncertainty
Consider a valuable renewable resource whose biomass X2003. "Harvesting a renewable resource under uncertainty,"Harvesting a Renewable Resource under Uncertainty 1 (with
Saphores, Jean-Daniel M
Essays on pricing under uncertainty
This dissertation analyzes pricing under uncertainty focusing on the U.S. airline industry. It sets to test theories of price dispersion driven by uncertainty in the demand by taking advantage of very detailed information about the dynamics of airline prices and inventory levels as the flight date approaches. Such detailed information about inventories at a ticket level to analyze airline pricing has been used previously by the author to show the importance of capacity constraints in airline pricing. This dissertation proposes and implements many new ideas to analyze airline pricing. Among the most important are: (1) It uses information about inventories at a ticket level. (2) It is the first to note that fare changes can be explained by adding dummy variables representing ticket characteristics. Therefore, the load factor at a ticket level will lose its explanatory power on fares if all ticket characteristics are included in a pricing equation. (3) It is the first to propose and implement a measure of Expected Load Factor as a tool to identify which flights are peak and which ones are not. (4) It introduces a novel idea of comparing actual sales with average sales at various points prior departure. Using these deviations of actual sales from sales under average conditions, it presents is the first study to show empirical evidence of peak load pricing in airlines. (5) It controls for potential endogeneity of sales using dynamic panels. The first essay tests the empirical importance of theories that explain price dispersion under costly capacity and demand uncertainty. The essay calculates a measure of an Expected Load Factor, that is used to calibrate the distribution of demand uncertainty and to identify which flights are peak and which ones are off-peak. It shows that different prices can be explained by the different selling probabilities. The second essay is the first study to provide formal evidence of stochastic peak-load pricing in airlines. It shows that airlines learn about the demand and respond to early sales setting higher prices when expected demand is high and more likely to exceed capacity.
Escobari Urday, Diego Alfonso
Uncertainty quantification approaches for advanced reactor analyses.
The original approach to nuclear reactor design or safety analyses was to make very conservative modeling assumptions so as to ensure meeting the required safety margins. Traditional regulation, as established by the U. S. Nuclear Regulatory Commission required conservatisms which have subsequently been shown to be excessive. The commission has therefore moved away from excessively conservative evaluations and has determined best-estimate calculations to be an acceptable alternative to conservative models, provided the best-estimate results are accompanied by an uncertainty evaluation which can demonstrate that, when a set of analysis cases which statistically account for uncertainties of all types are generated, there is a 95% probability that at least 95% of the cases meet the safety margins. To date, nearly all published work addressing uncertainty evaluations of nuclear power plant calculations has focused on light water reactors and on large-break loss-of-coolant accident (LBLOCA) analyses. However, there is nothing in the uncertainty evaluation methodologies that is limited to a specific type of reactor or to specific types of plant scenarios. These same methodologies can be equally well applied to analyses for high-temperature gas-cooled reactors and to liquid metal reactors, and they can be applied to steady-state calculations, operational transients, or severe accident scenarios. This report reviews and compares both statistical and deterministic uncertainty evaluation approaches. Recommendations are given for selection of an uncertainty methodology and for considerations to be factored into the process of evaluating uncertainties for advanced reactor best-estimate analyses.
Briggs, L. L.; Nuclear Engineering Division
Estimating uncertainty of inference for validation
We present a validation process based upon the concept that validation is an inference-making activity. This has always been true, but the association has not been as important before as it is now. Previously, theory had been confirmed by more data, and predictions were possible based on data. The process today is to infer from theory to code and from code to prediction, making the role of prediction somewhat automatic, and a machine function. Validation is defined as determining the degree to which a model and code is an accurate representation of experimental test data. Imbedded in validation is the intention to use the computer code to predict. To predict is to accept the conclusion that an observable final state will manifest; therefore, prediction is an inference whose goodness relies on the validity of the code. Quantifying the uncertainty of a prediction amounts to quantifying the uncertainty of validation, and this involves the characterization of uncertainties inherent in theory/models/codes and the corresponding data. An introduction to inference making and its associated uncertainty is provided as a foundation for the validation problem. A mathematical construction for estimating the uncertainty in the validation inference is then presented, including a possibility distribution constructed to represent the inference uncertainty for validation under uncertainty. The estimation of inference uncertainty for validation is illustrated using data and calculations from Inertial Confinement Fusion (ICF). The ICF measurements of neutron yield and ion temperature were obtained for direct-drive inertial fusion capsules at the Omega laser facility. The glass capsules, containing the fusion gas, were systematically selected with the intent of establishing a reproducible baseline of high-yield 10{sup 13}-10{sup 14} neutron output. The deuterium-tritium ratio in these experiments was varied to study its influence upon yield. This paper on validation inference is the first in a series of inference uncertainty estimations. While the methods demonstrated are primarily statistical, these do not preclude the use of nonprobabilistic methods for uncertainty characterization. The methods presented permit accurate determinations for validation and eventual prediction. It is a goal that these methods establish a standard against which best practice may evolve for determining degree of validation.
Booker, Jane M [Los Alamos National Laboratory; Langenbrunner, James R [Los Alamos National Laboratory; Hemez, Francois M [Los Alamos National Laboratory; Ross, Timothy J [UNM
Experimental uncertainty estimation and statistics for data having interval uncertainty.
This report addresses the characterization of measurements that include epistemic uncertainties in the form of intervals. It reviews the application of basic descriptive statistics to data sets which contain intervals rather than exclusively point estimates. It describes algorithms to compute various means, the median and other percentiles, variance, interquartile range, moments, confidence limits, and other important statistics and summarizes the computability of these statistics as a function of sample size and characteristics of the intervals in the data (degree of overlap, size and regularity of widths, etc.). It also reviews the prospects for analyzing such data sets with the methods of inferential statistics such as outlier detection and regressions. The report explores the tradeoff between measurement precision and sample size in statistical results that are sensitive to both. It also argues that an approach based on interval statistics could be a reasonable alternative to current standard methods for evaluating, expressing and propagating measurement uncertainties.
Kreinovich, Vladik (Applied Biomathematics, Setauket, New York); Oberkampf, William Louis (Applied Biomathematics, Setauket, New York); Ginzburg, Lev (Applied Biomathematics, Setauket, New York); Ferson, Scott (Applied Biomathematics, Setauket, New York); Hajagos, Janos (Applied Biomathematics, Setauket, New York)
Multidelity approaches for design under uncertainty
Uncertainties are present in many engineering applications and it is important to account for their effects during engineering design to achieve robust and reliable systems. One approach is to represent uncertainties as ...
Ng, Leo Wai-Tsun
Optimization under uncertainty in radiation therapy
In the context of patient care for life-threatening illnesses, the presence of uncertainty may compromise the quality of a treatment. In this thesis, we investigate robust approaches to managing uncertainty in radiation ...
Chan, Timothy Ching-Yee
Quantifying Uncertainty in Epidemiological Models
Modern epidemiology has made use of a number of mathematical models, including ordinary differential equation (ODE) based models and agent based models (ABMs) to describe the dynamics of how a disease may spread within a population and enable the rational design of strategies for intervention that effectively contain the spread of the disease. Although such predictions are of fundamental importance in preventing the next global pandemic, there is a significant gap in trusting the outcomes/predictions solely based on such models. Hence, there is a need to develop approaches such that mathematical models can be calibrated against historical data. In addition, there is a need to develop rigorous uncertainty quantification approaches that can provide insights into when a model will fail and characterize the confidence in the (possibly multiple) model outcomes/predictions, when such retrospective analysis cannot be performed. In this paper, we outline an approach to develop uncertainty quantification approaches for epidemiological models using formal methods and model checking. By specifying the outcomes expected from a model in a suitable spatio-temporal logic, we use probabilistic model checking methods to quantify the probability with which the epidemiological model satisfies the specification. We argue that statistical model checking methods can solve the uncertainty quantification problem for complex epidemiological models.
Ramanathan, Arvind [ORNL; Jha, Sumit Kumar [University of Central Florida
Uncertainty Assessments in Severe Nuclear Accident Scenarios
Managing uncertainties in industrial systems is a daily challenge to ensure improved design, robust operation, accountable performance and responsive risk control. This paper aims to illustrate the different depth analyses that the uncertainty software ... Keywords: Monte Carlo simulation, nuclear power plant, sensitivity analysis, severe accident, uncertainty
Bertrand Iooss; Fabrice Gaudier; Michel Marques; Bertrand Spindler; Bruno Tourniaire
Reliable water supply system design under uncertainty
Given the natural variability and uncertainties in long-term predictions, reliability is a critical design factor for water supply systems. However, the large scale of the problem and the correlated nature of the involved uncertainties result in models ... Keywords: Data uncertainty, Robust optimization, Spatially correlated data, Water supply system
G. Chung; K. Lansey; G. Bayraksan
Quantum Mechanics and the Generalized Uncertainty Principle
The generalized uncertainty principle has been described as a general consequence of incorporating a minimal length from a theory of quantum gravity. We consider a simple quantum mechanical model where the operator corresponding to position has discrete eigenvalues and show how the generalized uncertainty principle results for minimum uncertainty wave packets.
Jang Young Bang; Micheal S. Berger
Uncertainties in forecasting future climate
The increasing atmospheric concentrations of carbon dioxide, methane, chlorofluorocarbons, and other trace gases (collectively, greenhouse gases) pose a three-part challenge: (1) What the changes to atmospheric composition and the climate system will be; (2) What impacts (both detrimental and beneficial) these changes will induce on the biosphere and natural and societal resources; and (3) What the appropriate response, if any, might be when considering the changes themselves, the resulting impacts, and the benefits and other impacts of the activities generating the emissions. This brief summary will address only areas of agreement and areas of uncertainty related to the first challenge.
MacCracken, M.C.
A flexible numerical approach for quantification of epistemic uncertainty
In the field of uncertainty quantification (UQ), epistemic uncertainty often refers to the kind of uncertainty whose complete probabilistic description is not available, largely due to our lack of knowledge about the uncertainty. Quantification of the ... Keywords: Encapsulation problem, Epistemic uncertainty, Generalized polynomial chaos, Uncertainty quantification
Xiaoxiao Chen; Eun-Jae Park; Dongbin Xiu
ARM - PI Product - Direct Aerosol Forcing Uncertainty
NLE Websites -- All DOE Office Websites (Extended Search)
ProductsDirect Aerosol Forcing Uncertainty ProductsDirect Aerosol Forcing Uncertainty Comments? We would love to hear from you! Send us a note below or call us at 1-888-ARM-DATA. Send PI Product : Direct Aerosol Forcing Uncertainty Site(s) NSA SGP TWP General Description Understanding sources of uncertainty in aerosol direct radiative forcing (DRF), the difference in a given radiative flux component with and without aerosol, is essential to quantifying changes in Earth's radiation budget. We examine the uncertainty in DRF due to measurement uncertainty in the quantities on which it depends: aerosol optical depth, single scattering albedo, asymmetry parameter, solar geometry, and surface albedo. Direct radiative forcing at the top of the atmosphere and at the surface as well as sensitivities, the changes in DRF in response to unit changes in
Holographic Indeterminacy, Uncertainty and Noise
A theory is developed to describe the nonlocal effect of spacetime quantization on position measurements transverse to macroscopic separations. Spacetime quantum states close to a classical null trajectory are approximated by plane wavefunctions of Planck wavelength (l_P) reference beams; these are used to connect transverse position operators at macroscopically separated events. Transverse positions of events with null spacetime separation, but separated by macroscopic spatial distance $L$, are shown to be quantum conjugate observables, leading to holographic indeterminacy and a new uncertainty principle, a lower bound on the standard deviation of relative transverse position \\Delta x_\\perp > \\sqrt{l_PL} or angular orientation \\Delta\\theta > \\sqrt{l_P/L}. The resulting limit on the number of independent degrees of freedom is shown to agree quantitatively with holographic covariant entropy bounds derived from black hole physics and string theory. The theory predicts a universal ``holographic noise'' of spacet...
Hogan, Craig J
Joint quantum measurements with minimum uncertainty
Quantum physics constrains the accuracy of joint measurements of incompatible observables. Here we test tight measurement-uncertainty relations using single photons. We implement two independent, idealized uncertainty-estimation methods, the 3-state method and the weak-measurement method, and adapt them to realistic experimental conditions. Exceptional quantum state fidelities of up to 0.99998(6) allow us to verge upon the fundamental limits of measurement uncertainty.
Martin Ringbauer; Devon N. Biggerstaff; Matthew A. Broome; Alessandro Fedrizzi; Cyril Branciard; Andrew G. White
Design Feasibility Analysis and Optimization under Uncertainty...
Feasibility Analysis and Optimization under Uncertainty - A Bayesian Optimal Decision Framework Speaker(s): Jose M. Ortega Date: October 7, 2003 - 12:00pm Location: Bldg. 90...
Essays in inventory decisions under uncertainty .
??Uncertainty is a norm in business decisions. In this research, we focus on the inventory decisions for companies with uncertain customer demands. We first investigate… (more)
Manikas, Andrew Steven
A Partial Order on Uncertainty and Information
Information and uncertainty are closely related and extensively studied concepts in a number of scientific disciplines such as communication theory, probability theory, and statistics. Increasing the information arguably reduces the uncertainty on a given random subject. Consider the uncertainty measure as the variance of a random variable. Given the information that its outcome is in an interval, the uncertainty is expected to reduce when the interval shrinks. This proposition is not generally true. In this paper, we provide a necessary and sufficient condition for this proposition when the random variable is absolutely continuous or integer valued. We also give a similar result on Shannon information.
Chen, Jiahua
Long Term Power Generation Planning Under Uncertainty.
??Generation expansion planning concerns investment and operation decisions for different types of power plants over a multi-decade horizon under various uncertainties. The goal of this… (more)
Jin, Shan
The impact of uncertainty and risk measures
peak, and finds that this nonlinear transformation of the oiland oil price growth rates. As seen in the above illustration, uncertainty is at its peak
Jo, Soojin; Jo, Soojin
Dynamic Linear Production Games under Uncertainty
Oct 2, 2013 ... Abstract: In situations where uncertain costs are shared over time, static ... under uncertainty, generalizing classical linear production games to ...
Including uncertainty in hazard analysis through fuzzy measures
This paper presents a method for capturing the uncertainty expressed by an Hazard Analysis (HA) expert team when estimating the frequencies and consequences of accident sequences and provides a sound mathematical framework for propagating this uncertainty to the risk estimates for these accident sequences. The uncertainty is readily expressed as distributions that can visually aid the analyst in determining the extent and source of risk uncertainty in HA accident sequences. The results also can be expressed as single statistics of the distribution in a manner analogous to expressing a probabilistic distribution as a point-value statistic such as a mean or median. The study discussed here used data collected during the elicitation portion of an HA on a high-level waste transfer process to demonstrate the techniques for capturing uncertainty. These data came from observations of the uncertainty that HA team members expressed in assigning frequencies and consequences to accident sequences during an actual HA. This uncertainty was captured and manipulated using ideas from possibility theory. The result of this study is a practical method for displaying and assessing the uncertainty in the HA team estimates of the frequency and consequences for accident sequences. This uncertainty provides potentially valuable information about accident sequences that typically is lost in the HA process.
Bott, T.F.; Eisenhawer, S.W.
Microsoft Word - Price Uncertainty Supplement.doc
Gasoline and Diesel Fuel Update (EIA)
0 0 1 July 2010 Short-Term Energy Outlook Energy Price Volatility and Forecast Uncertainty 1 July 7, 2010 Release Crude Oil Prices. WTI crude oil spot prices averaged $75.34 per barrel in June 2010 ($1.60 per barrel above the prior month's average), close to the $76 per barrel projected in the forecast in last month's Outlook. EIA projects WTI prices will average about $79 per barrel over the second half of this year and rise to $84 by the end of next year (West Texas Intermediate Crude Oil Price Chart). Energy price forecasts are highly uncertain, as history has shown (Energy Price Volatility and Forecast Uncertainty). WTI futures for September 2010 delivery for the
Uncertainty of the Solar Neutrino Energy Spectrum
The solar neutrino spectrum measured by the Super-Kamiokande shows an excess in high energy bins, which may be explained by vacuum oscillation solution or $hep$ neutrino effect. Here we reconsider an uncertainty of the data caused by the tail of the energy resolution function. Events observed at energy higher than 13.5 MeV are induced by the tail of the resolution. At Super-Kamiokande precision level this uncertainty is no more than few percent within a Gaussian tail. But a power-law decay tail at 3 $\\sigma$ results considerable excesses in these bins, which may be another possible explanation of the anomaly in 708d(825d) data.
Q. Y. Liu
Stochastic Variational Approach to Minimum Uncertainty States
We introduce a new variational characterization of Gaussian diffusion processes as minimum uncertainty states. We then define a variational method constrained by kinematics of diffusions and Schr\\"{o}dinger dynamics to seek states of local minimum uncertainty for general non-harmonic potentials.
F. Illuminati; L. Viola
Uncertainty and Inference for Verification Measures
When a forecast is assessed, a single value for a verification measure is often quoted. This is of limited use, as it needs to be complemented by some idea of the uncertainty associated with the value. If this uncertainty can be quantified, it is ...
Ian T. Jolliffe
Constructing Uncertainty Sets for Robust Linear Optimization
In this paper, we propose a methodology for constructing uncertainty sets within the framework of robust optimization for linear optimization problems with uncertain parameters. Our approach relies on decision maker risk preferences. Specifically, we ... Keywords: coherent risk measures, distortion risk measures, robust optimization, uncertainty sets
Dimitris Bertsimas; David B. Brown
A theory is developed to describe the nonlocal effect of spacetime quantization on position measurements transverse to macroscopic separations. Spacetime quantum states close to a classical null trajectory are approximated by plane wavefunctions of Planck wavelength (l_P) reference beams; these are used to connect transverse position operators at macroscopically separated events. Transverse positions of events with null spacetime separation, but separated by macroscopic spatial distance $L$, are shown to be quantum conjugate observables, leading to holographic indeterminacy and a new uncertainty principle, a lower bound on the standard deviation of relative transverse position \\Delta x_\\perp > \\sqrt{l_PL} or angular orientation \\Delta\\theta > \\sqrt{l_P/L}. The resulting limit on the number of independent degrees of freedom is shown to agree quantitatively with holographic covariant entropy bounds derived from black hole physics and string theory. The theory predicts a universal ``holographic noise'' of spacetime, appearing as shear perturbations with a frequency-independent power spectral density S_H=l_P/c, or in equivalent metric perturbation units, h_{H,rms} \\sqrt{l_P/c} = 2.3 \\times 10^{-22} /\\sqrt{Hz}. If this description of holographic phenomenology is valid, interferometers with current technology could undertake direct quantitative studies of quantum gravity.
Craig J. Hogan
Statistical Uncertainty Analysis Applied to Criticality Calculation
In this paper, we present an uncertainty methodology based on a statistical approach, for assessing uncertainties in criticality prediction using monte carlo method due to uncertainties in the isotopic composition of the fuel. The methodology has been applied to criticality calculations with MCNP5 with additional stochastic input of the isotopic fuel composition. The stochastic input were generated using the latin hypercube sampling method based one the probability density function of each nuclide composition. The automatic passing of the stochastic input to the MCNP and the repeated criticality calculation is made possible by using a python script to link the MCNP and our latin hypercube sampling code.
Hartini, Entin; Andiwijayakusuma, Dinan; Susmikanti, Mike; Nursinta, A. W. [Centre for Nuclear Informatics Development, National Nuclear Energy Agency of Indonesia (Indonesia)
Meteorological Data Needs for Modeling Air Quality Uncertainties
A probabilistic framework for incorporating uncertainty in air quality models is described. The quantitative dependence of the uncertainty in calculated air quality concentrations on the uncertainty in the input meteorological data is illustrated ...
W. S. Lewellen; R. I. Sykes
Operational Forecaster Uncertainty Needs and Future Roles
Key results of a comprehensive survey of U.S. National Weather Service operational forecast managers concerning the assessment and communication of forecast uncertainty are presented and discussed. The survey results revealed that forecasters are ...
David R. Novak; David R. Bright; Michael J. Brennan
Parameterizing Mesoscale Wind Uncertainty for Dispersion Modeling
A parameterization of numerical weather prediction uncertainty is presented for use by atmospheric transport and dispersion models. The theoretical development applies Taylor dispersion concepts to diagnose dispersion metrics from numerical wind ...
Leonard J. Peltier; Sue Ellen Haupt; John C. Wyngaard; David R. Stauffer; Aijun Deng; Jared A. Lee; Kerrie J. Long; Andrew J. Annunzio
Uncertainty in climate change policy analysis
Achieving agreement about whether and how to control greenhouse gas emissions would be difficult enough even if the consequences were fully known. Unfortunately, choices must be made in the face of great uncertainty, about ...
Jacoby, Henry D.; Prinn, Ronald G.
Multifidelity methods for multidisciplinary design under uncertainty
For computational design and analysis tasks, scientists and engineers often have available many different simulation models. The output of each model has an associated uncertainty that is a result of the modeling process. ...
Christensen, Daniel Erik
Classification of Unseen Examples under Uncertainty
Very frequently machine learning from real-life data is affected by uncertainty. There are three main reasons for imperfection in data: incompleteness, imprecision (also called vagueness), and errors. In this paper the main emphasis is on classification ...
Jerzy W. Grzymala-Busse
MOS Uncertainty Estimates in an Ensemble Framework
It is being increasingly recognized that the uncertainty in weather forecasts should be quantified and furnished to users along with the single-value forecasts usually provided. Probabilistic forecasts of "events" have been made in special cases; ...
Bob Glahn; Matthew Peroutka; Jerry Wiedenfeld; John Wagner; Greg Zylstra; Bryan Schuknecht; Bryan Jackson
Climate Science and the Uncertainty Monster
How to understand and reason about uncertainty in climate science is a topic that is receiving increasing attention in both the scientific and philosophical literature. This paper provides a perspective on exploring ways to understand, assess, and reason ...
J. A. Curry; P. J. Webster
Annual Energy Outlook 2012 (EIA)
0 1 August 2010 Short-Term Energy Outlook Energy Price Volatility and Forecast Uncertainty 1 August 10, 2010 Release WTI crude oil spot prices averaged 76.32 per barrel in July...
Quantifying reliability uncertainty : a proof of concept.
This paper develops Classical and Bayesian methods for quantifying the uncertainty in reliability for a system of mixed series and parallel components for which both go/no-go and variables data are available. Classical methods focus on uncertainty due to sampling error. Bayesian methods can explore both sampling error and other knowledge-based uncertainties. To date, the reliability community has focused on qualitative statements about uncertainty because there was no consensus on how to quantify them. This paper provides a proof of concept that workable, meaningful quantification methods can be constructed. In addition, the application of the methods demonstrated that the results from the two fundamentally different approaches can be quite comparable. In both approaches, results are sensitive to the details of how one handles components for which no failures have been seen in relatively few tests.
Diegert, Kathleen V.; Dvorack, Michael A.; Ringland, James T.; Mundt, Michael Joseph; Huzurbazar, Aparna (Los Alamos National Laboratory, Los Alamos, NM); Lorio, John F.; Fatherley, Quinn (Los Alamos National Laboratory, Los Alamos, NM); Anderson-Cook, Christine (Los Alamos National Laboratory, Los Alamos, NM); Wilson, Alyson G. (Los Alamos National Laboratory, Los Alamos, NM); Zurn, Rena M.
Nuclear Data Uncertainties in 2004: A Perspective
Interest in nuclear data uncertainties is growing robustly after having languished for several years. Renewed attention to this topic is being motivated by the practical need for assuring that nuclear systems will be safe
Donald L. Smith
Uncertainty in Electroweak Symmetry Breaking in Models With High Scale Supersymmetry Breaking and its Impact on Interpretations of Searches For Supersymmetric Particles
Some regions of parameter space of the minimal supersymmetric standard model (MSSM) with high scale supersymmetry breaking have extreme sensitivity of electroweak symmetry breaking (EWSB) to the top quark mass through renormalisation group evolution effects. This leads to uncertainties in the predictions which need to be taken into account in the interpretation of searches for supersymmetric particles in these regions. As an example, we provide estimates of the current uncertainties on the position in parameter space of the region which does not break electroweak symmetry in the constrained MSSM (CMSSM). The position of the boundary of EWSB can vary by up to 2 TeV in m_0 due to the uncertainties coming from the current measurement errors on the top quark mass and from higher order corrections. In this dangerous region, for fixed CMSSM parameters the neutralino lightest supersymmetric particle mass has an associated large uncertainty of order 100%. These uncertainties therefore have a profound effect on the interpretation of LHC supersymmetric particle searches in terms of the CMSSM. We also show how to ameliorate poor convergence of the iterative numerical algorithm that calculates the MSSM spectrum near the boundary of EWSB.
B. C. Allanach; M. A. Parker
Environmental issues: still clouded in uncertainty
The key environmental obstacle affecting oil shale development is uncertainty. Little is known with much degree of certainty about potential environmental consequences of oil shale mining or processing. Primary uncertainties include: environmental compliance costs and their effects on investment decisions/ reliability and acceptability of anticipated pollution control technologies/ and confusing, time consuming environmental regulatory framework. The availability of water supplies, protection of air and water quality, impacts on boom-towns, and spent shale disposal are other related issues. (2 photos)
Stanwood, R.M.
Distributed Generation Investment by a Microgrid Under Uncertainty
operating strategy of the microgrid is not known in advance,Generation Investment by a Microgrid Under Uncertainty AfzalGeneration Investment by a Microgrid Under Uncertainty Afzal
Siddiqui, Afzal; Marnay, Chris
Distributed Generation Investment by a Microgrid under Uncertainty
Effects of carbon tax on microgrid combined heat and powerGeneration Investment by a Microgrid under Uncertainty AfzalGeneration Investment by a Microgrid under Uncertainty Afzal
Siddiqui, Afzal
Uncertainty Study of INEEL EST Laboratory Battery Testing Systems
INEELEXT-01-00505 December 2001 Uncertainty Study of INEEL EST Laboratory Battery Testing Systems Volume 1 Background and Derivation of Uncertainty Relationships John L. Morrison...
Strategies for Application of Isotopic Uncertainties in Burnup Credit
Uncertainties in the predicted isotopic concentrations in spent nuclear fuel represent one of the largest sources of overall uncertainty in criticality calculations that use burnup credit. The methods used to propagate the uncertainties in the calculated nuclide concentrations to the uncertainty in the predicted neutron multiplication factor (k{sub eff}) of the system can have a significant effect on the uncertainty in the safety margin in criticality calculations and ultimately affect the potential capacity of spent fuel transport and storage casks employing burnup credit. Methods that can provide a more accurate and realistic estimate of the uncertainty may enable increased spent fuel cask capacity and fewer casks needing to be transported, thereby reducing regulatory burden on licensee while maintaining safety for transporting spent fuel. This report surveys several different best-estimate strategies for considering the effects of nuclide uncertainties in burnup-credit analyses. The potential benefits of these strategies are illustrated for a prototypical burnup-credit cask design. The subcritical margin estimated using best-estimate methods is discussed in comparison to the margin estimated using conventional bounding methods of uncertainty propagation. To quantify the comparison, each of the strategies for estimating uncertainty has been performed using a common database of spent fuel isotopic assay measurements for pressurized-light-water reactor fuels and predicted nuclide concentrations obtained using the current version of the SCALE code system. The experimental database applied in this study has been significantly expanded to include new high-enrichment and high-burnup spent fuel assay data recently published for a wide range of important burnup-credit actinides and fission products. Expanded rare earth fission-product measurements performed at the Khlopin Radium Institute in Russia that contain the only known publicly-available measurement for {sup 103}Rh have also been included.
Gauld, I.C.
Quantification of Uncertainty in High-Resolution Temperature Scenarios for North America
A framework for the construction of probabilistic projections of high-resolution monthly temperature over North America using available outputs of opportunity from ensembles of multiple general circulation models (GCMs) and multiple regional ...
Guilong Li; Xuebin Zhang; Francis Zwiers; Qiuzi H. Wen
Risk management & organizational uncertainty implications for the assessment of high consequence organizations
Post hoc analyses have demonstrated clearly that macro-system, organizational processes have played important roles in such major catastrophes as Three Mile Island, Bhopal, Exxon Valdez, Chernobyl, and Piper Alpha. How can managers of such high-consequence organizations as nuclear power plants and nuclear explosives handling facilities be sure that similar macro-system processes are not operating in their plants? To date, macro-system effects have not been integrated into risk assessments. Part of the reason for not using macro-system analyses to assess risk may be the impression that standard organizational measurement tools do not provide hard data that can be managed effectively. In this paper, I argue that organizational dimensions, like those in ISO 9000, can be quantified and integrated into standard risk assessments.
Bennett, C.T.
Quantifying uncertainty in LCA-modelling of waste management systems
Highlights: Black-Right-Pointing-Pointer Uncertainty in LCA-modelling of waste management is significant. Black-Right-Pointing-Pointer Model, scenario and parameter uncertainties contribute. Black-Right-Pointing-Pointer Sequential procedure for quantifying uncertainty is proposed. Black-Right-Pointing-Pointer Application of procedure is illustrated by a case-study. - Abstract: Uncertainty analysis in LCA studies has been subject to major progress over the last years. In the context of waste management, various methods have been implemented but a systematic method for uncertainty analysis of waste-LCA studies is lacking. The objective of this paper is (1) to present the sources of uncertainty specifically inherent to waste-LCA studies, (2) to select and apply several methods for uncertainty analysis and (3) to develop a general framework for quantitative uncertainty assessment of LCA of waste management systems. The suggested method is a sequence of four steps combining the selected methods: (Step 1) a sensitivity analysis evaluating the sensitivities of the results with respect to the input uncertainties, (Step 2) an uncertainty propagation providing appropriate tools for representing uncertainties and calculating the overall uncertainty of the model results, (Step 3) an uncertainty contribution analysis quantifying the contribution of each parameter uncertainty to the final uncertainty and (Step 4) as a new approach, a combined sensitivity analysis providing a visualisation of the shift in the ranking of different options due to variations of selected key parameters. This tiered approach optimises the resources available to LCA practitioners by only propagating the most influential uncertainties.
Clavreul, Julie, E-mail: email@example.com [Department of Environmental Engineering, Technical University of Denmark, Miljoevej, Building 113, DK-2800 Kongens Lyngby (Denmark); Guyonnet, Dominique [BRGM, ENAG BRGM-School, BP 6009, 3 Avenue C. Guillemin, 45060 Orleans Cedex (France); Christensen, Thomas H. [Department of Environmental Engineering, Technical University of Denmark, Miljoevej, Building 113, DK-2800 Kongens Lyngby (Denmark)
Evaluation of Uncertainties in Cost Estimations
This paper examines uncertainty in several cost estimation methods for large infrastructure projects. In particular the impact and consequences of different unexpected events that have occurred during the construction of various tunnels and aqueducts will be treated. Combined with the cost estimation development, several hypotheses concerning uncertainty prediction and correlation will be verified. First, the basis for researching this topic is described and the methods of estimation and uncertainty prediction are presented. Based upon the results of an extensive investigation into the occurrence of unexpected events, several hypotheses concerning unexpected events and correlations are verified. Other related topics that have been researched are also briefly mentioned. Finally the findings are contemplated in a larger context based upon which the conclusions are presented
Meint Boschloo; Meint Boschloo; Pieter Van Gelder; Han Vrijling; Han Vrijling
Report: Technical Uncertainty and Risk Reduction
Energy.gov (U.S. Department of Energy (DOE)) Indexed Site
TECHNICAL UNCERTAINTY AND RISK REDUCTION TECHNICAL UNCERTAINTY AND RISK REDUCTION Background In FY 2007 EMAB was tasked to assess EM's ability to reduce risk and technical uncertainty. Board members explored this topic throughout the year as a component of their focus on the previously discussed topic of Discretionary Budgeting. Discussion Understanding the risks and variability associated with EM's projects is a challenging task that has the potential to significantly impact the program's established baselines. According to budget personnel, EM has established a database of baseline variables and possibilities; however, this tool is project-specific and does not apply to the greater complex. The Board believes that EM could benefit from incorporating an additional and more comprehensive data point into the baseline development process that budgets
Outlook Price Uncertainty-January 2010 Outlook Price Uncertainty-January 2010 1 January 2010 Short-Term Energy Outlook Energy Price Volatility and Forecast Uncertainty 1 January 12, 2010 Release Crude Oil Prices. West Texas Intermediate (WTI) crude oil spot prices averaged $74.50 per barrel in December 2009, about $3.50 per barrel lower than the prior month's average. The WTI spot price fell from $78 to $70 during the first 2 weeks of December, but colder-than-normal weather and U.S. crude oil and product inventory draws that exceeded the December 5-year averages helped push it back up to $79 per barrel by the end of the month. EIA forecasts that WTI spot prices will weaken over
May 2010 May 2010 Short-Term Energy Outlook Energy Price Volatility and Forecast Uncertainty 1 May 11, 2010 Release Crude Oil Prices. WTI crude oil spot prices averaged $84 per barrel in April 2010, about $3 per barrel above the prior month's average and $2 per barrel higher than forecast in last month's Outlook. EIA projects WTI prices will average about $84 per barrel over the second half of this year and rise to $87 by the end of next year, an increase of about $2 per barrel from the previous Outlook (West Texas Intermediate Crude Oil Price Chart). Energy price forecasts are highly uncertain, as history has shown. Prices for near-term futures options contracts suggest that the market attaches
0 0 1 June 2010 Short-Term Energy Outlook Energy Price Volatility and Forecast Uncertainty 1 June 8, 2010 Release Crude Oil Prices. WTI crude oil spot prices averaged less than $74 per barrel in May 2010, almost $11 per barrel below the prior month's average and $7 per barrel lower than forecast in last month's Outlook. EIA projects WTI prices will average about $79 per barrel over the second half of this year and rise to $84 by the end of next year, a decrease of about $3 per barrel from the previous Outlook (West Texas Intermediate Crude Oil Price Chart). Energy price forecasts are highly uncertain, as history has shown. Prices for near-term futures options contracts suggest that the market attaches
Uncertainty in in-place filter test results
Some benefits of accounting for uncertainty in in-place filter test results are explored. Information the test results provide relative to system performance acceptance limits is evaluated in terms of test result uncertainty. An expression for test result uncertainty is used to estimate uncertainty in in-place filter tests on an example air cleaning system. Modifications to the system test geometry are evaluated in terms of effects on test result uncertainty.
Scripsick, R.C.; Beckman, R.J.; Mokler, B.V.
Generalized Uncertainty Principle and Dark Matter
There have been proposals that primordial black hole remnants (BHRs) are the dark matter, but the idea is somewhat vague. Recently we argued that the generalized uncertainty principle (GUP) may prevent black holes from evaporating completely, in a similar way that the standard uncertainty principle prevents the hydrogen atom from collapsing. We further noted that the hybrid inflation model provides a plausible mechanism for production of large numbers of small black holes. Combining these we suggested that the dark matter might be composed of Planck-size BHRs. In this paper we briefly review these arguments, and discuss the reheating temperature as a result of black hole evaporation.
Chen, P
We underestimate uncertainties in our predictions.
Prediction is defined in the American Heritage Dictionary as follows: 'To state, tell about, or make known in advance, especially on the basis of special knowledge.' What special knowledge do we demand of modeling and simulation to assert that we have a predictive capability for high consequence applications? The 'special knowledge' question can be answered in two dimensions: the process and rigor by which modeling and simulation is executed and assessment results for the specific application. Here we focus on the process and rigor dimension and address predictive capability in terms of six attributes: (1) geometric and representational fidelity, (2) physics and material model fidelity, (3) code verification, (4) solution verification, (5) validation, and (6) uncertainty quantification. This presentation will demonstrate through mini-tutorials, simple examples, and numerous case studies how each attribute creates opportunities for errors, biases, or uncertainties to enter into simulation results. The demonstrations will motivate a set of practices that minimize the risk in using modeling and simulation for high-consequence applications while defining important research directions. It is recognized that there are cultural, technical, infrastructure, and resource barriers that prevent analysts from performing all analyses at the highest levels of rigor. Consequently, the audience for this talk is (1) analysts, so they can know what is expected of them, (2) decision makers, so they can know what to expect from modeling and simulation, and (3) the R&D community, so they can address the technical and infrastructure issues that prevent analysts from executing analyses in a practical, timely, and quality manner.
Pilch, Martin M.
Requirements Uncertainty in a Software Product Line
A complex system's requirements almost always remain uncertain late into its software development. In gas turbine engine control systems at Rolls-Royce, for a traditional project (non-product line) typically 50% of requirements will change between Critical ... Keywords: Requirements, Uncertainty, Software Product Lines
Andy J. Nolan; Silvia Abrahao; Paul C. Clements; Andy Pickard
Uncertainty analysis of well test data
During a well test a transient pressure response is created by a temporary change in production rate. The well response is usually monitored during a relatively short period of time, depending upon the test objectives. Reservoir properties are determined from well test data via an inverse problem approach. Uncertainty is inherent in any nonlinear inverse problem. Unfortunately, well test interpretation suffers particularly from a variety of uncertainties that, when combined, reduce the confidence that can be associated with the estimated reservoir properties. The specific factors that have been analyzed in this work are: 1. Pressure noise (random noise) 2. Pressure drift (systematic variation) 3. Rate history effects Our work is based on the analysis of the effects of random pressure noise, the drift error, and the rate history on the estimation of typical reservoir parameters for two common reservoir models: A vertical well with a constant wellbore storage and skin in a homogeneous reservoir. A vertical well with a finite conductivity vertical fracture including wellbore effects in a homogeneous reservoir. This work represents a sensitivity study of the impact of pressure and rate uncertainty on parameter estimation and the confidence intervals associated with these results. In this work we statistically analyze the calculated reservoir parameters to quantify the impact of pressure and rate uncertainty on them.
Merad, Mohamed Belgacem
UAV mission planning under uncertainty
With the continued development of high endurance Unmanned Aerial Vehicles (UAV) and Unmanned Combat Aerial Vehicles (UCAV) that are capable of performing autonomous fiunctions across the spectrum of military operations, ...
Sakamoto, Philemon
Quantum Gravitational Uncertainty of Transverse Position
It is argued that holographic bounds on the information content of spacetime might be directly measurable. A new uncertainty principle is conjectured to arise from quantum indeterminacy of nearly flat spacetime: Angular orientations of null trajectories of spatial length L are uncertain, with standard deviation in each transverse direction \\Delta \\theta> \\sqrt{l_P/L}, where l_p denotes the Planck length. It is shown that this angular uncertainty corresponds to the information loss and nonlocality that occur if 3+1-D spacetime has a holographic dual description in terms of Planck-scale waves on a 2+1D screen with encoding close to the Planck diffraction limit, and agrees with covariant holographic entropy bounds on total number of degrees of freedom. The spectrum and spatial structure of predicted quantum-gravitational ``holographic noise'' are estimated to be directly measurable over a broad range of frequencies using interferometers with current technology.
Global Warming Mitigation Investments Optimized under Uncertainty
Global Warming Mitigation Investments Optimized under Uncertainty Global Warming Mitigation Investments Optimized under Uncertainty Speaker(s): Hermann Held Date: July 9, 2010 - 12:00pm Location: 90-3122 Seminar Host/Point of Contact: Thomas McKone The Copenhagen Accord (2009) recognizes that 'the increase in global temperature should be below 2 degrees Celsius' (compared to pre-industrial levels, '2° target'). In recent years, energy economics have derived welfare-optimal investment streams into low-emission energy mixes and associated costs. According to our analyses, auxiliary targets that are in line with the 2° target could be achieved at relatively low costs if energy investments were triggered rather swiftly. While such analyses assume 'perfect foresight' of a benevolent 'social planner', an accompanying suite of experiments explicitly
Interpolation Uncertainties Across the ARM SGP Area
Interpolation Uncertainties Across the ARM SGP Area Interpolation Uncertainties Across the ARM SGP Area J. E. Christy, C. N. Long, and T. R. Shippert Pacific Northwest National Laboratory Richland, Washington Interpolation Grids Across the SGP Network Area The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Program operates a network of surface radiation measurement sites across north central Oklahoma and south central Kansas. This Southern Great Plains (SGP) network consists of 21 sites unevenly spaced from 95.5 to 99.5 degrees west longitude, and from 34.5 to 38.5 degrees north latitude. We use the technique outlined by Long and Ackerman (2000) and Long et al. (1999) to infer continuous estimates of clear-sky downwelling shortwave (SW) irradiance, SW cloud effect, and daylight fractional sky cover for each
Information-Disturbance theorem and Uncertainty Relation
It has been shown that Information-Disturbance theorem can play an important role in security proof of quantum cryptography. The theorem is by itself interesting since it can be regarded as an information theoretic version of uncertainty principle. It, however, has been able to treat restricted situations. In this paper, the restriction on the source is abandoned, and a general information-disturbance theorem is obtained. The theorem relates information gain by Eve with information gain by Bob.
Takayuki Miyadera; Hideki Imai
Evaluating Uncertainties in the Projection of Future Drought
The uncertainty in the projection of future drought occurrence was explored for four different drought indices using two model ensembles. The first ensemble expresses uncertainty in the parameter space of the third Hadley Centre climate model, ...
Eleanor J. Burke; Simon J. Brown
Treatment of measurement uncertainties at the power burst facility
The treatment of measurement uncertainty at the Power Burst Facility provides a means of improving data integrity as well as meeting standard practice reporting requirements. This is accomplished by performing the uncertainty analysis in two parts, test independent uncertainty analysis and test dependent uncertainty analysis. The test independent uncertainty analysis is performed on instrumentation used repeatedly from one test to the next, and does not have to be repeated for each test except for improved or new types of instruments. A test dependent uncertainty analysis is performed on each test based on the test independent uncertainties modified as required by test specifications, experiment fixture design, and historical performance of instruments on similar tests. The methodology for performing uncertainty analysis based on the National Bureau of Standards method is reviewed with examples applied to nuclear instrumentation.
Meyer, L.C.
Estimates of Uncertainty in Predictions of Global Mean Surface Temperature
A method for estimating uncertainty in future climate change is discussed in detail and applied to predictions of global mean temperature change. The method uses optimal fingerprinting to make estimates of uncertainty in model simulations of ...
J. A. Kettleborough; B. B. B. Booth; P. A. Stott; M. R. Allen
Eigenvalue sensitivity studies for the Fort St. Vrain high temperature gas-cooled reactor to account for fabrication and modeling uncertainties
Uncertainties in the composition and fabrication of fuel compacts for the Fort St. Vrain (FSV) high temperature gas reactor have been studied by performing eigenvalue sensitivity studies that represent the key uncertainties for the FSV neutronic analysis. The uncertainties for the TRISO fuel kernels were addressed by developing a suite of models for an 'average' FSV fuel compact that models the fuel as (1) a mixture of two different TRISO fuel particles representing fissile and fertile kernels, (2) a mixture of four different TRISO fuel particles representing small and large fissile kernels and small and large fertile kernels and (3) a stochastic mixture of the four types of fuel particles where every kernel has its diameter sampled from a continuous probability density function. All of the discrete diameter and continuous diameter fuel models were constrained to have the same fuel loadings and packing fractions. For the non-stochastic discrete diameter cases, the MCNP compact model arranged the TRISO fuel particles on a hexagonal honeycomb lattice. This lattice-based fuel compact was compared to a stochastic compact where the locations (and kernel diameters for the continuous diameter cases) of the fuel particles were randomly sampled. Partial core configurations were modeled by stacking compacts into fuel columns containing graphite. The differences in eigenvalues between the lattice-based and stochastic models were small but the runtime of the lattice-based fuel model was roughly 20 times shorter than with the stochastic-based fuel model. (authors)
Pavlou, A. T.; Betzler, B. R.; Burke, T. P.; Lee, J. C.; Martin, W. R.; Pappo, W. N.; Sunny, E. E. [Univ. of Michigan, Dept. of Nuclear Engineering and Radiological Sciences, 2355 Bonisteel Boulevard, Ann Arbor, MI 48109 (United States)
Uncertainties in risk assessment at USDOE facilities
The United States Department of Energy (USDOE) has embarked on an ambitious program to remediate environmental contamination at its facilities. Decisions concerning cleanup goals, choices among cleanup technologies, and funding prioritization should be largely risk-based. Risk assessments will be used more extensively by the USDOE in the future. USDOE needs to develop and refine risk assessment methods and fund research to reduce major sources of uncertainty in risk assessments at USDOE facilities. The terms{open_quote} risk assessment{close_quote} and{open_quote} risk management{close_quote} are frequently confused. The National Research Council (1983) and the United States Environmental Protection Agency (USEPA, 1991a) described risk assessment as a scientific process that contributes to risk management. Risk assessment is the process of collecting, analyzing and integrating data and information to identify hazards, assess exposures and dose responses, and characterize risks. Risk characterization must include a clear presentation of {open_quotes}... the most significant data and uncertainties...{close_quotes} in an assessment. Significant data and uncertainties are {open_quotes}...those that define and explain the main risk conclusions{close_quotes}. Risk management integrates risk assessment information with other considerations, such as risk perceptions, socioeconomic and political factors, and statutes, to make and justify decisions. Risk assessments, as scientific processes, should be made independently of the other aspects of risk management (USEPA, 1991a), but current methods for assessing health risks are based on conservative regulatory principles, causing unnecessary public concern and misallocation of funds for remediation.
Hamilton, L.D.; Holtzman, S.; Meinhold, A.F.; Morris, S.C.; Rowe, M.D.
Investment and Upgrade in Distributed Generation under Uncertainty
AS, Marnay, C. Distributed generation investment by aand Upgrade in Distributed Generation under Uncertaintyand Upgrade in Distributed Generation under Uncertainty ?
Making Life and Death Decisions in Conditions of Uncertainty ...
Making Life and Death Decisions in Conditions of Uncertainty THE 2008 RUSSIAN-GEORGIAN WAR. Andrei Illarionov ...
The NIST Reference on Constants, Units, and Uncertainty
The NIST Reference on Constants, Units and Uncertainty, Information at the foundation of modern science and technology from the Physical ...
Uncertainty Budgets for SOP 5: 2013-06-19
... DEVELOP an uncertainty budget based on concepts ... for participation using the Office of Weights and Measures Contact Management System. ...
Uncertainty Budgets for SOP 4: 2013-06-17
Group decision making under uncertainty Type="Italic ...
Resource allocation using quantification of margins and uncertainty.
There is an increasing need to assess the performance of high consequence systems using a modeling and simulation based approach. Central to this approach are the need to quantify the uncertainties present in the system and to compare the system response to an expected performance measure. At Sandia National Laboratories, this process is referred to as quantification of margins and uncertainties or QMU. Depending on the outcome of the assessment, there might be a need to increase the confidence in the predicted response of a system model; thus a need to understand where resources need to be allocated to increase this confidence. This paper examines the problem of resource allocation done within the context of QMU. An optimization based approach to solving the resource allocation is considered and sources of aleatoric and epistemic uncertainty are included in the calculations.
Mahadevan, Sankaran (Vanderbilt University, Nashville, TN); Urbina, Angel; Paez, Thomas Lee (Thomas Paez Consulting, Durango, CO)
Investment and Upgrade in Distributed Generation under Uncertainty Investment and Upgrade in Distributed Generation under Uncertainty Speaker(s): Afzal Siddiqui Karl Maribu Date: September 4, 2008 - 12:00pm Location: 90-3122 Seminar Host/Point of Contact: Galen Barbose The ongoing deregulation of electricity industries worldwide is providing incentives for microgrids to use small-scale distributed generation (DG) and combined heat and power (CHP) applications via heat exchangers (HXs) to meet local energy loads. Although the electric-only effciency of DG is lower than that of central-station production, relatively high tariff rates and the potential for CHP applications increase the attractiveness of on-site generation. Nevertheless, a microgrid contemplating the installation of gas-fired DG has to be aware of the uncertainty in the
State of the Science FACT SHEET Weather Forecast Uncertainty
State of the Science FACT SHEET Weather Forecast Uncertainty December, 2009 This document represents the state of the science of quantifying and communicating weather forecast uncertainty. Decisions can be improved by better understanding the uncertainties in the weather forecasts. NOAA seeks to help
Probabilistic surfaces: point based primitives to show surface uncertainty
Efficient and informative visualization of surfaces with uncertainties is an important topic with many applications in science and engineering. Examples include environmental pollution borderline identification, identification of the limits of an oil ... Keywords: points as display primitives, uncertainty, visualizing surface uncertainty
Gevorg Grigoryan; Penny Rheingans
Visualization of gridded scalar data with uncertainty in geosciences
Characterization of the earth's subsurface involves the construction of 3D models from sparse data and so leads to simulation results that involve some degree of uncertainty. This uncertainty is often neglected in the subsequent visualization, due to ... Keywords: 3D, Monte carlo simulation, Scalar fields, Uncertainty, Visualisation, Visualization
Björn Zehner; Norihiro Watanabe; Olaf Kolditz
Probabilistic accident consequence uncertainty analysis: Food chain uncertainty assessment. Volume 1: Main report
This volume is the first of a two-volume document that summarizes a joint project conducted by the US Nuclear Regulatory Commission and the European Commission to assess uncertainties in the MACCS and COSYMA probabilistic accident consequence codes. These codes were developed primarily for estimating the risks presented by nuclear reactors based on postulated frequencies and magnitudes of potential accidents. This document reports on an ongoing project to assess uncertainty in the MACCS and COSYMA calculations for the offsite consequences of radionuclide releases by hypothetical nuclear power plant accidents. A panel of sixteen experts was formed to compile credible and traceable uncertainty distributions for food chain variables that affect calculations of offsite consequences. The expert judgment elicitation procedure and its outcomes are described in these volumes. Other panels were formed to consider uncertainty in other aspects of the codes. Their results are described in companion reports. Volume 1 contains background information and a complete description of the joint consequence uncertainty study. Volume 2 contains appendices that include (1) a summary of the MACCS and COSYMA consequence codes, (2) the elicitation questionnaires and case structures for both panels, (3) the rationales and results for the panels on soil and plant transfer and animal transfer, (4) short biographies of the experts, and (5) the aggregated results of their responses.
Brown, J. [National Radiological Protection Board (United Kingdom); Goossens, L.H.J.; Kraan, B.C.P. [Delft Univ. of Technology (Netherlands)] [and others
Methodology for characterizing modeling and discretization uncertainties in computational simulation
This research effort focuses on methodology for quantifying the effects of model uncertainty and discretization error on computational modeling and simulation. The work is directed towards developing methodologies which treat model form assumptions within an overall framework for uncertainty quantification, for the purpose of developing estimates of total prediction uncertainty. The present effort consists of work in three areas: framework development for sources of uncertainty and error in the modeling and simulation process which impact model structure; model uncertainty assessment and propagation through Bayesian inference methods; and discretization error estimation within the context of non-deterministic analysis.
A Bayesian approach to simultaneously quantify assignments and linguistic uncertainty
Subject matter expert assessments can include both assignment and linguistic uncertainty. This paper examines assessments containing linguistic uncertainty associated with a qualitative description of a specific state of interest and the assignment uncertainty associated with assigning a qualitative value to that state. A Bayesian approach is examined to simultaneously quantify both assignment and linguistic uncertainty in the posterior probability. The approach is applied to a simplified damage assessment model involving both assignment and linguistic uncertainty. The utility of the approach and the conditions under which the approach is feasible are examined and identified.
Chavez, Gregory M [Los Alamos National Laboratory; Booker, Jane M [BOOKER SCIENTIFIC FREDERICKSBURG; Ross, Timothy J [UNM
Towards The Removal Of Uncertainty In Sustainable Building Design Through Full Scale Optimization.
??The lack of whole-building design optimization resources available to building designers has led to uncertainty in design decisions involved with building highly sustainable or 'Green'… (more)
Fix, Stuart C.
Uncertainty Budget Analysis for Dimensional Inspection Processes (U)
This paper is intended to provide guidance and describe how to prepare an uncertainty analysis of a dimensional inspection process through the utilization of an uncertainty budget analysis. The uncertainty analysis is stated in the same methodology as that of the ISO GUM standard for calibration and testing. There is a specific distinction between how Type A and Type B uncertainty analysis is used in a general and specific process. All theory and applications are utilized to represent both a generalized approach to estimating measurement uncertainty and how to report and present these estimations for dimensional measurements in a dimensional inspection process. The analysis of this uncertainty budget shows that a well-controlled dimensional inspection process produces a conservative process uncertainty, which can be attributed to the necessary assumptions in place for best possible results.
Valdez, Lucas M. [Los Alamos National Laboratory
Risk communication: Uncertainties and the numbers game
The science of risk assessment seeks to characterize the potential risk in situations that may pose hazards to human health or the environment. However, the conclusions reached by the scientists and engineers are not an end in themselves - they are passed on to the involved companies, government agencies, legislators, and the public. All interested parties must then decide what to do with the information. Risk communication is a type of technical communication that involves some unique challenges. This paper first defines the relationships between risk assessment, risk management, and risk communication and then explores two issues in risk communication: addressing uncertainty and putting risk number into perspective.
Ortigara, M. [ed.
Air pollution uncertainty exists in radon measurements
This report discusses radon which is a colorless, odorless gas formed by the decay of radium and uranium that has been shown to cause lung cancer. Progress has been made in ensuring the accuracy of home radon measurements. According to this report, however, radon measurements are uncertain because the ability of the devices that measure radon and the companies analyzing the devices' readings varies and homeowners may not be following EPA's recommended testing procedures. Several possible causes for the uncertainty in radon measurements are cited.
It is argued that holographic bounds on the information content of spacetime might be directly measurable. A new holographic uncertainty principle is conjectured as a quantum property of nearly flat spacetime: The spatial wavefunction of a body at rest at position L relative to any observer has a width in directions transverse to L greater than Delta x_H=C(Ll_P)^{1/2}, where l_p denotes the Planck length and C is of the order of unity. It is shown that this angular uncertainty Delta theta > C (l_P/L)^{1/2} corresponds to the information loss and nonlocality that occur if 3D space has a holographic dual description in terms of Planck-scale waves on a 2D screen with encoding close to the diffraction limit, and agrees with covariant holographic entropy bounds on total number of degrees of freedom. It is estimated that this effect can be precisely tested by interferometers capable of transverse position measurements, such as the Laser Interferometer Space Antenna.
Dealing with Uncertainties During Heat Exchanger Design
Over the last thirty years much progress has been made in heat exchanger design methodology. Even so, the design engineer still has to deal with a great deal of uncertainty. Whilst the methods used to predict heat transfer coefficients are now quite sophisticated and take account of many physical factors, the results they yield are still inaccurate. Physical property information is required for the estimation of heat transfer coefficients. Available information is often of dubious accuracy. Even given accurate properties modern methods for the predictions of tube-side heat transfer coefficient can be expected to have an accuracy of only ± 10%. For the shell-side, higher errors (say, around ±15%) can be expected. Perhaps worst of all, comes the specification of fouling resistance (the allowance made for the thermal resistance presented by dirt layers deposited on the heat exchanger tubes). In most instances there is little science or understanding behind the specification of these resistances. Traditionally there have been two approaches to dealing with these uncertainties: over-specification of fouling resistance; and, addition of 'design margin' (i.e. addition of extra surface area). There are cases in which both approaches are adopted. The engineer specifying the required duty provides a higher than necessary fouling resistance whilst the exchanger designer adds design margin! Both approaches result in 'over-design'.
Polley, G. T.; Pugh, S. J.
Uncertainty quantification for large-scale ocean circulation predictions.
Uncertainty quantificatio in climate models is challenged by the sparsity of the available climate data due to the high computational cost of the model runs. Another feature that prevents classical uncertainty analyses from being easily applicable is the bifurcative behavior in the climate data with respect to certain parameters. A typical example is the Meridional Overturning Circulation in the Atlantic Ocean. The maximum overturning stream function exhibits discontinuity across a curve in the space of two uncertain parameters, namely climate sensitivity and CO{sub 2} forcing. We develop a methodology that performs uncertainty quantificatio in the presence of limited data that have discontinuous character. Our approach is two-fold. First we detect the discontinuity location with a Bayesian inference, thus obtaining a probabilistic representation of the discontinuity curve location in presence of arbitrarily distributed input parameter values. Furthermore, we developed a spectral approach that relies on Polynomial Chaos (PC) expansions on each sides of the discontinuity curve leading to an averaged-PC representation of the forward model that allows efficient uncertainty quantification and propagation. The methodology is tested on synthetic examples of discontinuous data with adjustable sharpness and structure.
Safta, Cosmin; Debusschere, Bert J.; Najm, Habib N.; Sargsyan, Khachik
Quantifying the uncertainty of synchrotron-based lattice strain measurements
Crystallographic lattice strains - measured using diffraction techniques - are the same magnitude as typical macroscopic elastic strains. From a research perspective, the main interest is in measuring changes in lattice strains induced during in-situ loading: either from one macroscopic stress level to another or from one cycle to the next. The hope is to link these measurements to deformation-induced changes in the internal structure of crystals, possibly related to inelastic deformation and damage. These measurements are relatively new - little experimental intuition exists and it is difficult to discern whether observed differences are due to actual micromechanical evolution or to random experimental fluctuations. If the measurements are linked to material evolution on the size scale of the individual crystal, they have the potential to change the ideas about grain scale deformation partitioning processes and can be used to validate crystal-based simulation frameworks. Therefore, understanding the uncertainty associated with the lattice strain experiments is a crucial step in their continued development. If the measured lattice strains are of the same order as the random fluctuations that are part of the measurement process, documenting the strains can create more confusion than understanding. Often lattice strain error is quoted as {+-}1 x 10{sup -4}. This simple value fails to account for the range of factors that contribute to the experimental uncertainty - which, if not properly accounted for, may lead to a false confidence in the measurements. The focus of this paper is the development of a lattice strain uncertainty expression that delineates the contributing factors into terms that vary independently: (i) the contribution from the instrument and (ii) the contribution from the material under investigation. These aspects of uncertainty are described, and it is then possible to employ a calibrant powder method (diffraction from an unstrained material with high-precision lattice constants) to quantify the instrument portion of the lattice strain uncertainty. In these experiments, the instrument contribution to the uncertainty has been found to be a function of the Bragg angle and the intensity of the diffracted peaks. To develop a model for the instrument portion of the lattice strain uncertainty two datasets obtained using a MAR345 online image plate at the Cornell High Energy Synchrotron Source and a GE 41RT amorphous silicon detector at the Advanced Photon Source have been examined.
Schuren, J.C.; Miller, M.P. (Cornell)
Survey and Evaluate Uncertainty Quantification Methodologies
The Carbon Capture Simulation Initiative (CCSI) is a partnership among national laboratories, industry and academic institutions that will develop and deploy state-of-the-art computational modeling and simulation tools to accelerate the commercialization of carbon capture technologies from discovery to development, demonstration, and ultimately the widespread deployment to hundreds of power plants. The CCSI Toolset will provide end users in industry with a comprehensive, integrated suite of scientifically validated models with uncertainty quantification, optimization, risk analysis and decision making capabilities. The CCSI Toolset will incorporate commercial and open-source software currently in use by industry and will also develop new software tools as necessary to fill technology gaps identified during execution of the project. The CCSI Toolset will (1) enable promising concepts to be more quickly identified through rapid computational screening of devices and processes; (2) reduce the time to design and troubleshoot new devices and processes; (3) quantify the technical risk in taking technology from laboratory-scale to commercial-scale; and (4) stabilize deployment costs more quickly by replacing some of the physical operational tests with virtual power plant simulations. The goal of CCSI is to deliver a toolset that can simulate the scale-up of a broad set of new carbon capture technologies from laboratory scale to full commercial scale. To provide a framework around which the toolset can be developed and demonstrated, we will focus on three Industrial Challenge Problems (ICPs) related to carbon capture technologies relevant to U.S. pulverized coal (PC) power plants. Post combustion capture by solid sorbents is the technology focus of the initial ICP (referred to as ICP A). The goal of the uncertainty quantification (UQ) task (Task 6) is to provide a set of capabilities to the user community for the quantification of uncertainties associated with the carbon capture processes. As such, we will develop, as needed and beyond existing capabilities, a suite of robust and efficient computational tools for UQ to be integrated into a CCSI UQ software framework.
Lin, Guang; Engel, David W.; Eslinger, Paul W.
Incorporating uncertainty in RADTRAN 6.0 input files.
Uncertainty may be introduced into RADTRAN analyses by distributing input parameters. The MELCOR Uncertainty Engine (Gauntt and Erickson, 2004) has been adapted for use in RADTRAN to determine the parameter shape and minimum and maximum of the distribution, to sample on the distribution, and to create an appropriate RADTRAN batch file. Coupling input parameters is not possible in this initial application. It is recommended that the analyst be very familiar with RADTRAN and able to edit or create a RADTRAN input file using a text editor before implementing the RADTRAN Uncertainty Analysis Module. Installation of the MELCOR Uncertainty Engine is required for incorporation of uncertainty into RADTRAN. Gauntt and Erickson (2004) provides installation instructions as well as a description and user guide for the uncertainty engine.
Dennis, Matthew L.; Weiner, Ruth F.; Heames, Terence John (Alion Science and Technology)
December 2010 Short-Term Energy Outlook Energy Price Volatility and Forecast Uncertainty 1 December 7, 2010 Release Crude Oil Prices. West Texas Intermediate (WTI) crude oil spot prices averaged over $84 per barrel in November, more than $2 per barrel higher than the October average. EIA has raised the average winter 2010-2011 period WTI spot price forecast by $1 per barrel from the last monthĘąs Outlook to $84 per barrel. WTI spot prices rise to $89 per barrel by the end of next year, $2 per barrel higher than in the last Outlook. Projected WTI prices average $79 per barrel in 2010 and $86 per barrel in 2011. WTI futures for February 2011 delivery during the 5-day period ending December 2
Reducing long-term reservoir performance uncertainty
Reservoir performance is one of the key issues that have to be addressed before going ahead with the development of a geothermal field. In order to select the type and size of the power plant and design other surface installations, it is necessary to know the characteristics of the production wells and of the produced fluids, and to predict the changes over a 10--30 year period. This is not a straightforward task, as in most cases the calculations have to be made on the basis of data collected before significant fluid volumes have been extracted from the reservoir. The paper describes the methodology used in predicting the long-term performance of hydrothermal systems, as well as DOE/GTD-sponsored research aimed at reducing the uncertainties associated with these predictions. 27 refs., 1 fig.
Lippmann, M.J.
Uncertainty quantification in kinematic wave models
We developed a probabilistic approach to quantify parametric uncertainty in first-order hyperbolic conservation laws (kinematic wave equations). The approach relies on the derivation of a deterministic equation for the cumulative density function (CDF) of the system state, in which probabilistic descriptions (probability density functions or PDFs) of the system parameters and/or initial and boundary conditions serve as inputs. In contrast to PDF equations, which are often used in other contexts, CDF equations allow for straightforward and unambiguous determination of boundary conditions with respect to sample variables.The accuracy and robustness of solutions of the CDF equation for one such system, the Saint-Venant equations of river flows, were investigated via comparison with Monte Carlo simulations.
Wang, Peng; Tartakovsky, Daniel M.
Radiotherapy Dose Fractionation under Parameter Uncertainty
In radiotherapy, radiation is directed to damage a tumor while avoiding surrounding healthy tissue. Tradeoffs ensue because dose cannot be exactly shaped to the tumor. It is particularly important to ensure that sensitive biological structures near the tumor are not damaged more than a certain amount. Biological tissue is known to have a nonlinear response to incident radiation. The linear quadratic dose response model, which requires the specification of two clinically and experimentally observed response coefficients, is commonly used to model this effect. This model yields an optimization problem giving two different types of optimal dose sequences (fractionation schedules). Which fractionation schedule is preferred depends on the response coefficients. These coefficients are uncertainly known and may differ from patient to patient. Because of this not only the expected outcomes but also the uncertainty around these outcomes are important, and it might not be prudent to select the strategy with the best expected outcome.
Davison, Matt [Department of Applied Mathematics, University of Western Ontario, London, Ontario (Canada); Department of Statistical and Actuarial Science, University of Western Ontario, London, Ontario (Canada); Ivey School of Business, University of Western Ontario, London, Ontario (Canada); Kim, Daero [Department of Applied Mathematics, University of Western Ontario, London, Ontario (Canada); Keller, Harald [Department Radiation Oncology, University of Toronto, Toronto, Ontario (Canada); Radiation Medicine Program, Princess Margaret Hospital, Toronto, Ontario (Canada)
April 2010 April 2010 Short-Term Energy Outlook Energy Price Volatility and Forecast Uncertainty 1 April 6, 2010 Release Crude Oil Prices. WTI crude oil spot prices averaged $81 per barrel in March 2010, almost $5 per barrel above the prior month's average and $3 per barrel higher than forecast in last month's Outlook. Oil prices rose from a low this year of $71.15 per barrel on February 5 to $80 per barrel by the end of February, generally on news of robust economic and energy demand growth in non-OECD Asia and the Middle East, and held near $81 until rising to $85 at the start of April. EIA expects WTI prices to average above $81 per barrel this summer, slightly less that $81 for 2010 as a whole,
0 0 1 September 2010 Short-Term Energy Outlook Energy Price Volatility and Forecast Uncertainty 1 September 8, 2010 Release Crude Oil Prices. West Texas Intermediate (WTI) crude oil spot prices averaged about $77 per barrel in August 2010, very close to the July average, but $3 per barrel lower than projected in last month's Outlook. WTI spot prices averaged almost $82 per barrel over the first 10 days of August but then fell by $9 per barrel over the next 2 weeks as the market reacted to a series of reports of a stumbling economic recovery. EIA has lowered its average fourth quarter 2010 WTI spot price forecast to $77 per barrel, compared with $81 in last month's Outlook. WTI spot prices are projected to
October 2010 October 2010 Short-Term Energy Outlook Energy Price Volatility and Forecast Uncertainty 1 October 13, 2010 Release Crude Oil Prices. WTI oil prices averaged $75 per barrel in September but rose above $80 at the end of the month and into early October. EIA has raised the average fourth- quarter 2010 forecasted WTI spot price to $79 per barrel compared with $77 per barrel in last monthĘąs Outlook. WTI spot prices are projected to rise to $85 per barrel by the fourth quarter of next year. As has been the case for most of 2010, WTI futures traded with a notable lack of volatility during the third quarter of 2010 (Figure 1). However, prices did bounce in
March 2010 March 2010 Short-Term Energy Outlook Energy Price Volatility and Forecast Uncertainty 1 March 9, 2010 Release Crude Oil Prices. WTI crude oil spot prices averaged $76.39 per barrel in February 2010, almost $2 per barrel lower than the prior month's average and very near the $76 per barrel forecast in last month's Outlook. Last month, the WTI spot price reached a low of $71.15 on February 5 and peaked at $80.04 on February 22. EIA expects WTI prices to average above $80 per barrel this spring, rising to an average of about $82 per barrel by the end of the year and to $85 per barrel by the end of 2011 (West Texas Intermediate Crude Oil Price Chart).
November 2010 November 2010 Short-Term Energy Outlook Energy Price Volatility and Forecast Uncertainty 1 November 9, 2010 Release Crude Oil Prices. WTI crude oil spot prices averaged almost $82 per barrel in October, about $7 per barrel higher than the September average, as expectations of higher oil demand pushed up prices. EIA has raised the average fourth quarter 2010 WTI spot price forecast to about $83 per barrel compared with $79 per barrel in last monthĘąs Outlook. WTI spot prices rise to $87 per barrel by the fourth quarter of next year. Projected WTI prices average $79 per barrel in 2010 and $85 per barrel in 2011. WTI futures for January 2011 delivery (for the 5-day period ending November 4)
Microsoft Word - Price Uncertainty Supplement .docx
1 1 1 January 2011 Short-Term Energy Outlook Energy Price Volatility and Forecast Uncertainty 1 January 11, 2011 Release Crude Oil Prices. West Texas Intermediate (WTI) crude oil spot prices averaged over $89 per barrel in December, about $5 per barrel higher than the November average. Expectations of higher oil demand, combined with unusually cold weather in both Europe and the U.S. Northeast, contributed to prices. EIA has raised the first quarter 2011 WTI spot price forecast by $8 per barrel from last monthĘąs Outlook to $92 per barrel with a continuing rise to an average $99 per barrel in the fourth quarter of 2012. The projected annual average WTI price is $93 per barrel in 2011 and $98 per barrel in
Effects of uncertainties and errors on Lyapunov control
Lyapunov control (open-loop) is often confronted with uncertainties and errors in practical applications. In this paper, we analyze the robustness of Lyapunov control against the uncertainties and errors in quantum control systems. The analysis is carried out through examinations of uncertainties and errors, calculations of the control fidelity under influences of the certainties and errors, as well as discussions on the caused effects. Two examples, a closed control system and an open control system, are presented to illustrate the general formulism.
Yi, X X; Wu, Chunfeng; Oh, C H
generation investment by a microgrid under uncertainty.M. E?ects of carbon tax on microgrid combined heat and powersite generation. Nevertheless, a microgrid contemplating the
Relating confidence to measured information uncertainty in qualitative reasoning
Qualitative reasoning makes use of qualitative assessments provided by subject matter experts to model factors such as security risk. Confidence in a result is important and useful when comparing competing results. Quantifying the confidence in an evidential reasoning result must be consistent and based on the available information. A novel method is proposed to relate confidence to the available information uncertainty in the result using fuzzy sets. Information uncertainty can be quantified through measures of non-specificity and conflict. Fuzzy values for confidence are established from information uncertainty values that lie between the measured minimum and maximum information uncertainty values.
Chavez, Gregory M [Los Alamos National Laboratory; Zerkle, David K [Los Alamos National Laboratory; Key, Brian P [Los Alamos National Laboratory; Shevitz, Daniel W [Los Alamos National Laboratory
The effects of incorporating dynamic data on estimates of uncertainty
Petroleum exploration and development are capital intensive and smart economic decisions that need to be made to profitably extract oil and gas from the reservoirs. Accurate quantification of uncertainty in production forecasts will help in assessing risk and making good economic decisions. This study investigates the effect of combining dynamic data with the uncertainty in static data to see the effect on estimates of uncertainty in production forecasting. Fifty permeability realizations were generated for a reservoir in west Texas from available petrophysical data. We quantified the uncertainty in the production forecasts using a likelihood weighting method and an automatic history matching technique combined with linear uncertainty analysis. The results were compared with the uncertainty predicted using only static data. We also investigated approaches for best selecting a smaller number of models from a larger set of realizations to be history matched for quantification of uncertainty. We found that incorporating dynamic data in a reservoir model will result in lower estimates of uncertainty than considering only static data. However, incorporation of dynamic data does not guarantee that the forecasted ranges will encompass the true value. Reliability of the forecasted ranges depends on the method employed. When sampling multiple realizations of static data for history matching to quantify uncertainty, a sampling over the entire range of realization likelihoods shows larger confidence intervals and is more likely to encompass the true value for predicted fluid recoveries, as compared to selecting the best models.
Mulla, Shahebaz Hisamuddin
Uncertainty Principle Inequalities Related to Laguerre-Bessel Transform
In this paper, an analogous of Heisenberg inequality is established for Laguerre-Bessel transform. Also, a local uncertainty principle for this transform is investigate
Hamem, Soumeya
Gas Exploration Software for Reducing Uncertainty in Gas ...
... * Improve estimation of reservoir parameters and quantify uncertainty in the estimation when exploring for gas and oil deposits using geophysical data More ...
Measurement uncertainty analysis techniques applied to PV performance measurements
The purpose of this presentation is to provide a brief introduction to measurement uncertainty analysis, outline how it is done, and illustrate uncertainty analysis with examples drawn from the PV field, with particular emphasis toward its use in PV performance measurements. The uncertainty information we know and state concerning a PV performance measurement or a module test result determines, to a significant extent, the value and quality of that result. What is measurement uncertainty analysis It is an outgrowth of what has commonly been called error analysis. But uncertainty analysis, a more recent development, gives greater insight into measurement processes and tests, experiments, or calibration results. Uncertainty analysis gives us an estimate of the I interval about a measured value or an experiment's final result within which we believe the true value of that quantity will lie. Why should we take the time to perform an uncertainty analysis A rigorous measurement uncertainty analysis: Increases the credibility and value of research results; allows comparisons of results from different labs; helps improve experiment design and identifies where changes are needed to achieve stated objectives (through use of the pre-test analysis); plays a significant role in validating measurements and experimental results, and in demonstrating (through the post-test analysis) that valid data have been acquired; reduces the risk of making erroneous decisions; demonstrates quality assurance and quality control measures have been accomplished; define Valid Data as data having known and documented paths of: Origin, including theory; measurements; traceability to measurement standards; computations; uncertainty analysis of results.
Wells, C.
Uncertainty relation for non-Hamiltonian quantum systems
General forms of uncertainty relations for quantum observables of non-Hamiltonian quantum systems are considered. Special cases of uncertainty relations are discussed. The uncertainty relations for non-Hamiltonian quantum systems are considered in the Schroedinger-Robertson form since it allows us to take into account Lie-Jordan algebra of quantum observables. In uncertainty relations, the time dependence of quantum observables and the properties of this dependence are discussed. We take into account that a time evolution of observables of a non-Hamiltonian quantum system is not an endomorphism with respect to Lie, Jordan, and associative multiplications.
Tarasov, Vasily E. [Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, Moscow 119991 (Russian Federation)] [Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, Moscow 119991 (Russian Federation)
Robust Dynamic Traffic Assignment under Demand and Capacity Uncertainty
Assignment under Demand and Capacity Uncertainty ? Giuseppeworst-case sce- nario of demand and capacity con?gurations.uncertain demands and capacities are modeled as unknown-but-
Calafiore, Giuseppe; El Ghaoui, Laurent
Decision making under epistemic uncertainty : an application to seismic design
The problem of accounting for epistemic uncertainty in risk management decisions is conceptually straightforward, but is riddled with practical difficulties. Simple approximations are often used whereby future variations ...
Agarwal, Anna
A surrogate-based uncertainty quantification with quantifiable errors
Surrogate models are often employed to reduce the computational cost required to complete uncertainty quantification, where one is interested in propagating input parameters uncertainties throughout a complex engineering model to estimate responses uncertainties. An improved surrogate construction approach is introduced here which places a premium on reducing the associated computational cost. Unlike existing methods where the surrogate is constructed first, then employed to propagate uncertainties, the new approach combines both sensitivity and uncertainty information to render further reduction in the computational cost. Mathematically, the reduction is described by a range finding algorithm that identifies a subspace in the parameters space, whereby parameters uncertainties orthogonal to the subspace contribute negligible amount to the propagated uncertainties. Moreover, the error resulting from the reduction can be upper-bounded. The new approach is demonstrated using a realistic nuclear assembly model and compared to existing methods in terms of computational cost and accuracy of uncertainties. Although we believe the algorithm is general, it will be applied here for linear-based surrogates and Gaussian parameters uncertainties. The generalization to nonlinear models will be detailed in a separate article. (authors)
Bang, Y.; Abdel-Khalik, H. S. [North Carolina State Univ., Raleigh, NC 27695 (United States)
Model simplification of chemical kinetic systems under uncertainty
This thesis investigates the impact of uncertainty on the reduction and simplification of chemical kinetics mechanisms. Chemical kinetics simulations of complex fuels are very computationally expensive, especially when ...
Coles, Thomas Michael Kyte
Approaches to uncertainty evaluation based on proficiency testing ...
example (glucose in human serum) where the uncertainty .... to the normalised error, EN, (Eq. 2). ... a human plasma sample, expressed in mmol/L. The ana-.
Characterizing Uncertainty for Regional Climate Change Mitigation and Adaptation Decisions
This white paper describes the results of new research to develop an uncertainty characterization process to help address the challenges of regional climate change mitigation and adaptation decisions.
Unwin, Stephen D.; Moss, Richard H.; Rice, Jennie S.; Scott, Michael J.
Reducing Uncertainties in Life Limits of Titanium Alloys in Turbine ...
Presentation Title, Reducing Uncertainties in Life Limits of Titanium Alloys in Turbine Engine Rotors. Author(s), James M. Larsen, Sushant Jha, Christopher J.
Probabilistic accident consequence uncertainty analysis: Dispersion and deposition uncertainty assessment, main report
The development of two new probabilistic accident consequence codes, MACCS and COSYMA, was completed in 1990. These codes estimate the risks presented by nuclear installations based on postulated frequencies and magnitudes of potential accidents. In 1991, the US Nuclear Regulatory Commission (NRC) and the Commission of the European Communities (CEC) began a joint uncertainty analysis of the two codes. The ultimate objective of the joint effort was to develop credible and traceable uncertainty distributions for the input variables of the codes. Expert elicitation was identified as the best technology available for developing a library of uncertainty distributions for the selected consequence parameters. The study was formulated jointly and was limited to the current code models and to physical quantities that could be measured in experiments. Experts developed their distributions independently. To validate the distributions generated for the wet deposition input variables, samples were taken from these distributions and propagated through the wet deposition code model. Resulting distributions closely replicated the aggregated elicited wet deposition distributions. To validate the distributions generated for the dispersion code input variables, samples from the distributions and propagated through the Gaussian plume model (GPM) implemented in the MACCS and COSYMA codes. Project teams from the NRC and CEC cooperated successfully to develop and implement a unified process for the elaboration of uncertainty distributions on consequence code input parameters. Formal expert judgment elicitation proved valuable for synthesizing the best available information. Distributions on measurable atmospheric dispersion and deposition parameters were successfully elicited from experts involved in the many phenomenological areas of consequence analysis. This volume is the first of a three-volume document describing the project.
Harper, F.T.; Young, M.L.; Miller, L.A. [Sandia National Labs., Albuquerque, NM (United States); Hora, S.C. [Univ. of Hawaii, Hilo, HI (United States); Lui, C.H. [Nuclear Regulatory Commission, Washington, DC (United States); Goossens, L.H.J.; Cooke, R.M. [Delft Univ. of Technology (Netherlands); Paesler-Sauer, J. [Research Center, Karlsruhe (Germany); Helton, J.C. [and others
Probabilistic accident consequence uncertainty analysis -- Uncertainty assessment for deposited material and external doses. Volume 2: Appendices
The development of two new probabilistic accident consequence codes, MACCS and COSYMA, was completed in 1990. These codes estimate the consequence from the accidental releases of radiological material from hypothesized accidents at nuclear installations. In 1991, the US Nuclear Regulatory Commission and the Commission of the European Communities began cosponsoring a joint uncertainty analysis of the two codes. The ultimate objective of this joint effort was to systematically develop credible and traceable uncertainty distributions for the respective code input variables. A formal expert judgment elicitation and evaluation process was identified as the best technology available for developing a library of uncertainty distributions for these consequence parameters. This report focuses on the results of the study to develop distribution for variables related to the MACCS and COSYMA deposited material and external dose models. This volume contains appendices that include (1) a summary of the MACCS and COSYMA consequence codes, (2) the elicitation questionnaires and case structures, (3) the rationales and results for the panel on deposited material and external doses, (4) short biographies of the experts, and (5) the aggregated results of their responses.
Goossens, L.H.J.; Kraan, B.C.P.; Cooke, R.M. [Delft Univ. of Technology (Netherlands); Boardman, J. [AEA Technology (United Kingdom); Jones, J.A. [National Radiological Protection Board (United Kingdom); Harper, F.T.; Young, M.L. [Sandia National Labs., Albuquerque, NM (United States); Hora, S.C. [Univ. of Hawaii, Hilo, HI (United States)
Probabilistic accident consequence uncertainty analysis -- Early health effects uncertainty assessment. Volume 2: Appendices
The development of two new probabilistic accident consequence codes, MACCS and COSYMA, was completed in 1990. These codes estimate the consequence from the accidental releases of radiological material from hypothesized accidents at nuclear installations. In 1991, the US Nuclear Regulatory Commission and the Commission of the European Communities began cosponsoring a joint uncertainty analysis of the two codes. The ultimate objective of this joint effort was to systematically develop credible and traceable uncertainty distributions for the respective code input variables. A formal expert judgment elicitation and evaluation process was identified as the best technology available for developing a library of uncertainty distributions for these consequence parameters. This report focuses on the results of the study to develop distribution for variables related to the MACCS and COSYMA early health effects models. This volume contains appendices that include (1) a summary of the MACCS and COSYMA consequence codes, (2) the elicitation questionnaires and case structures, (3) the rationales and results for the panel on early health effects, (4) short biographies of the experts, and (5) the aggregated results of their responses.
Haskin, F.E. [Univ. of New Mexico, Albuquerque, NM (United States); Harper, F.T. [Sandia National Labs., Albuquerque, NM (United States); Goossens, L.H.J.; Kraan, B.C.P. [Delft Univ. of Technology (Netherlands)
Probabilistic accident consequence uncertainty analysis -- Uncertainty assessment for internal dosimetry. Volume 2: Appendices
The development of two new probabilistic accident consequence codes, MACCS and COSYMA, was completed in 1990. These codes estimate the consequence from the accidental releases of radiological material from hypothesized accidents at nuclear installations. In 1991, the US Nuclear Regulatory Commission and the Commission of the European Communities began cosponsoring a joint uncertainty analysis of the two codes. The ultimate objective of this joint effort was to systematically develop credible and traceable uncertainty distributions for the respective code input variables. A formal expert judgment elicitation and evaluation process was identified as the best technology available for developing a library of uncertainty distributions for these consequence parameters. This report focuses on the results of the study to develop distribution for variables related to the MACCS and COSYMA internal dosimetry models. This volume contains appendices that include (1) a summary of the MACCS and COSYMA consequence codes, (2) the elicitation questionnaires and case structures, (3) the rationales and results for the panel on internal dosimetry, (4) short biographies of the experts, and (5) the aggregated results of their responses.
Goossens, L.H.J.; Kraan, B.C.P.; Cooke, R.M. [Delft Univ. of Technology (Netherlands); Harrison, J.D. [National Radiological Protection Board (United Kingdom); Harper, F.T. [Sandia National Labs., Albuquerque, NM (United States); Hora, S.C. [Univ. of Hawaii, Hilo, HI (United States)
A New Measure of Earnings Forecast Uncertainty Xuguang Sheng
A New Measure of Earnings Forecast Uncertainty Xuguang Sheng American University Washington, D of earnings forecast uncertainty as the sum of dispersion among analysts and the variance of mean forecast available to analysts at the time they make their forecasts. Hence, it alleviates some of the limitations
Kim, Kiho
Poster: Data intensive uncertainty quantification: applications to climate modeling
In October of 2009, LLNL began a three-year Strategic Initiative, "The Advance of Uncertainty Quantification Science with Application to Climate Modeling, Inertial Confinement Fusion Design, and Stockpile Stewardship Science." The goal of the project ... Keywords: climate model, uncertainty quantification
John Tannahill; Donald D. Lucas; David Domyancic; Scott Brandon; Richard Klein
Evaluating the role of uncertainty in electric utility capacity planning
This final report on Evaluating the Role of Uncertainty in Electric Utility Capacity Planning is divided into separate sections addressing demand, supply and the simultaneous consideration of both and describes several mathematical characterizations of the effects of uncertainty on the capacity expansion decision. The basic objective is to develop more robust models which can appropriately include the fundamental uncertainties associated with capacity expansion planning in the electric utility industry. Much of what has been developed in this project has been incorporated into a long-term, computer model for capacity expansion planning. A review is provided of certain deterministic capacity expansion methodologies. The effect of load curve uncertainty on capacity planning is considered and the use of a certain expected load curve to account for uncertainty in demand is proposed. How uncertainty influences the allocation of capital costs among the various load curve realizations is also discussed. The supply side uncertainties of fuel prices and random availability of generating units are considered. In certain cases it is shown that the use of the expected fuel costs will furnish a solution which minimizes the total expected costs. The effect of derating units to account for their random availability is also characterized. A stochastic linear program formulated to examine the simultaneous consideration of fuel cost and demand uncertainties is analyzed. This volume includes the report text one appendix with information on linear programming-based analysis of marginal cost pricing in the electric utility industry.
Soyster, A.L.
Short title: STOCHASTIC VARIATIONAL APPROACH TO MINIMUM UNCERTAINTY
We introduce a new variational characterization of Gaussian diffusion processes as minimum uncertainty states. We then define a variational method constrained by kinematics of diffusions and Schrödinger dynamics to seek states of local minimum uncertainty for general non-harmonic potentials. PACS numbers:03.65.-w, 03.65.Ca, 03.65.Bz 1.
Fabrizio Illuminati; Lorenza Viola
Efficient Algorithms for Heavy-Tail Analysis under Interval Uncertainty
Efficient Algorithms for Heavy-Tail Analysis under Interval Uncertainty Vladik Kreinovich1 heavy-tailed distri- butions, i.e., distributions in which (x) decreases as (x) x- . To properly take for computing these ranges. Keywords: heavy-tailed distributions, interval uncertainty, efficient algorithms
Kreinovich, Vladik
LBNL-60592 Distributed Generation Investment by a Microgrid Under Uncertainty Afzal Siddiqui'06 1 Distributed Generation Investment by a Microgrid Under Uncertainty Afzal Siddiqui University a California-based microgrid's decision to invest in a distributed generation (DG) unit that operates
Engineering computation under uncertainty - Capabilities of non-traditional models
This paper provides a review of various non-traditional uncertainty models for engineering computation and responds to the criticism of those models. This criticism imputes inappropriateness in representing uncertain quantities and an absence of numerically ... Keywords: Computational efficiency, Fuzzy models, Fuzzy randomness, Imprecise probabilities, Interval analysis, Uncertainty modeling
Bernd Möller; Michael Beer
Uncertainties in the Anti-neutrino Production at Nuclear Reactors
Anti-neutrino emission rates from nuclear reactors are determined from thermal power measurements and fission rate calculations. The uncertainties in these quantities for commercial power plants and their impact on the calculated interaction rates in electron anti-neutrino detectors is examined. We discuss reactor-to-reactor correlations between the leading uncertainties and their relevance to reactor anti-neutrino experiments.
Z. Djurcic; J. A. Detwiler; A. Piepke; V. R. Foster Jr.; L. Miller; G. Gratta
Djurcic, Z; Piepke, A; Foster, V R; Miller, L; Gratta, G
Random matrix theory for modeling uncertainties in computational mechanics
Random matrix theory for modeling uncertainties in computational mechanics C. Soize Laboratory of Engineering Mechanics, University of Marne-la-Vall´ee, 5 boulevard Descartes, 77454 Marne-la-Vallée, France, e in computational mechanics. If data uncertainties can be modeled by parametric probabilistic methods, for a given
Paris-Sud XI, Université de
Uncertainty measures for general Type-2 fuzzy sets
Five uncertainty measures have previously been defined for interval Type-2 fuzzy sets (IT2 FSs), namely centroid, cardinality, fuzziness, variance and skewness. Based on a recently developed @a-plane representation for a general T2 FS, this paper generalizes ... Keywords: ?-Plane representation, Cardinality, Centroid, Fuzziness, Skewness, Type-2 fuzzy sets, Uncertainty measures, Variance
Daoyuan Zhai; Jerry M. Mendel
Design Feasibility Analysis and Optimization under Uncertainty - A Bayesian
Design Feasibility Analysis and Optimization under Uncertainty - A Bayesian Design Feasibility Analysis and Optimization under Uncertainty - A Bayesian Optimal Decision Framework Speaker(s): Jose M. Ortega Date: October 7, 2003 - 12:00pm Location: Bldg. 90 Seminar Host/Point of Contact: Michael Sohn A new approach to the problem of identifying design feasibility and optimality under uncertainty is introduced. Based on the Bayesian concepts of predictive probability and expected utility, the method can quantify the feasibility of a process design and identify the optimal operation conditions when there are uncertainties in the process parameters. The use of Bayesian statistics enables the treatment of a very wide class of parameter uncertainties, including simple bounds, analytic probability density functions, correlation structures and empirical distributions.
Dark Energy from Quantum Uncertainty of Simultaneity
The observed acceleration expansion of the universe was thought attribute to a mysterious dark energy in the framework of the classical general relativity. The dark energy behaves very similar with a vacuum energy in quantum mechanics. However, once the quantum effects are seriously taken into account, it predicts a wrong order of the vacuum energy and leads to a severe fine-tuning, known as the cosmological constant problem. We abandon the standard interpretation that time is a global parameter in quantum mechanics, replace it by a quantum dynamical variable playing the role of an operational quantum clock system. In the framework of reinterpretation of time, we find that the synchronization of two quantum clocks distance apart can not be realized in all rigor at quantum level. Thus leading to an intrinsic quantum uncertainty of simultaneity between spatial interval, which implies a visional vacuum energy fluctuation and gives an observed dark energy density $\\rho_{de}=\\frac{6}{\\pi}L_{P}^{-2}L_{H}^{-2}$, whe...
Luo, M J
The observed acceleration expansion of the universe was thought attribute to a mysterious dark energy in the framework of the classical general relativity. The dark energy behaves very similar with a vacuum energy in quantum mechanics. However, once the quantum effects are seriously taken into account, it predicts a wrong order of the vacuum energy and leads to a severe fine-tuning, known as the cosmological constant problem. We abandon the standard interpretation that time is a global parameter in quantum mechanics, replace it by a quantum dynamical variable playing the role of an operational quantum clock system. In the framework of reinterpretation of time, we find that the synchronization of two quantum clocks distance apart can not be realized in all rigor at quantum level. Thus leading to an intrinsic quantum uncertainty of simultaneity between spatial interval, which implies a visional vacuum energy fluctuation and gives an observed dark energy density $\\rho_{de}=\\frac{6}{\\pi}L_{P}^{-2}L_{H}^{-2}$, where $L_{P}$ and $L_{H}$ are the Planck and Hubble scale cut-off. The expectation value of zero-point energy automatically vanishes under the quantum dynamical time variable. The fraction of the dark energy is precisely given by $\\Omega_{de}=\\frac{2}{\\pi}$, which does not evolve with the quantum dynamical time variable, so it is "always" comparable to the matter energy density or the critical density. This theory is consistent with current cosmic observations.
M. J. Luo
Uncertainty quantification of limit-cycle oscillations
Different computational methodologies have been developed to quantify the uncertain response of a relatively simple aeroelastic system in limit-cycle oscillation, subject to parametric variability. The aeroelastic system is that of a rigid airfoil, supported by pitch and plunge structural coupling, with nonlinearities in the component in pitch. The nonlinearities are adjusted to permit the formation of a either a subcritical or supercritical branch of limit-cycle oscillations. Uncertainties are specified in the cubic coefficient of the torsional spring and in the initial pitch angle of the airfoil. Stochastic projections of the time-domain and cyclic equations governing system response are carried out, leading to both intrusive and non-intrusive computational formulations. Non-intrusive formulations are examined using stochastic projections derived from Wiener expansions involving Haar wavelet and B-spline bases, while Wiener-Hermite expansions of the cyclic equations are employed intrusively and non-intrusively. Application of the B-spline stochastic projection is extended to the treatment of aerodynamic nonlinearities, as modeled through the discrete Euler equations. The methodologies are compared in terms of computational cost, convergence properties, ease of implementation, and potential for application to complex aeroelastic systems.
Beran, Philip S. [Multidisciplinary Technologies Center, Air Vehicles Directorate, AFRL/VASD, Building 146, 2210 Eighth Street, WPAFB, OH 45433 (United States)]. E-mail: firstname.lastname@example.com; Pettit, Chris L. [United States Naval Academy, 590 Holloway Rd., MS 11-B, Annapolis, MD 21402 (United States)]. E-mail: firstname.lastname@example.com; Millman, Daniel R. [USAF TPS/EDT, 220 South Wolfe Ave, Bldg. 1220, Rm. 131, Edwards AFB, CA 93524-6485 (United States)]. E-mail: email@example.com
Information-Theoretically Secure Communication Under Channel Uncertainty
Secure communication under channel uncertainty is an important and challenging problem in physical-layer security and cryptography. In this dissertation, we take a fundamental information-theoretic view at three concrete settings and use them to shed insight into efficient secure communication techniques for different scenarios under channel uncertainty. First, a multi-input multi-output (MIMO) Gaussian broadcast channel with two receivers and two messages: a common message intended for both receivers (i.e., channel uncertainty for decoding the common message at the receivers) and a confidential message intended for one of the receivers but needing to be kept asymptotically perfectly secret from the other is considered. A matrix characterization of the secrecy capacity region is established via a channel-enhancement argument and an extremal entropy inequality previously established for characterizing the capacity region of a degraded compound MIMO Gaussian broadcast channel. Second, a multilevel security wiretap channel where there is one possible realization for the legitimate receiver channel but multiple possible realizations for the eavesdropper channel (i.e., channel uncertainty at the eavesdropper) is considered. A coding scheme is designed such that the number of secure bits delivered to the legitimate receiver depends on the actual realization of the eavesdropper channel. More specifically, when the eavesdropper channel realization is weak, all bits delivered to the legitimate receiver need to be secure. In addition, when the eavesdropper channel realization is strong, a prescribed part of the bits needs to remain secure. We call such codes security embedding codes, referring to the fact that high-security bits are now embedded into the low-security ones. We show that the key to achieving efficient security embedding is to jointly encode the low-security and high-security bits. In particular, the low-security bits can be used as (part of) the transmitter randomness to protect the high-security ones. Finally, motivated by the recent interest in building secure, robust and efficient distributed information storage systems, the problem of secure symmetrical multilevel diversity coding (S-SMDC) is considered. This is a setting where there are channel uncertainties at both the legitimate receiver and the eavesdropper. The problem of encoding individual sources is first studied. A precise characterization of the entire admissible rate region is established via a connection to the problem of secure coding over a three-layer wiretap network and utilizing some basic polyhedral structure of the admissible rate region. Building on this result, it is then shown that the simple coding strategy of separately encoding individual sources at the encoders can achieve the minimum sum rate for the general S-SMDC problem.
Ly, Hung Dinh
A method to estimate the effect of deformable image registration uncertainties on daily dose mapping
Purpose: To develop a statistical sampling procedure for spatially-correlated uncertainties in deformable image registration and then use it to demonstrate their effect on daily dose mapping. Methods: Sequential daily CT studies are acquired to map anatomical variations prior to fractionated external beam radiotherapy. The CTs are deformably registered to the planning CT to obtain displacement vector fields (DVFs). The DVFs are used to accumulate the dose delivered each day onto the planning CT. Each DVF has spatially-correlated uncertainties associated with it. Principal components analysis (PCA) is applied to measured DVF error maps to produce decorrelated principal component modes of the errors. The modes are sampled independently and reconstructed to produce synthetic registration error maps. The synthetic error maps are convolved with dose mapped via deformable registration to model the resulting uncertainty in the dose mapping. The results are compared to the dose mapping uncertainty that would result from uncorrelated DVF errors that vary randomly from voxel to voxel. Results: The error sampling method is shown to produce synthetic DVF error maps that are statistically indistinguishable from the observed error maps. Spatially-correlated DVF uncertainties modeled by our procedure produce patterns of dose mapping error that are different from that due to randomly distributed uncertainties. Conclusions: Deformable image registration uncertainties have complex spatial distributions. The authors have developed and tested a method to decorrelate the spatial uncertainties and make statistical samples of highly correlated error maps. The sample error maps can be used to investigate the effect of DVF uncertainties on daily dose mapping via deformable image registration. An initial demonstration of this methodology shows that dose mapping uncertainties can be sensitive to spatial patterns in the DVF uncertainties.
Murphy, Martin J.; Salguero, Francisco J.; Siebers, Jeffrey V.; Staub, David; Vaman, Constantin [Department of Radiation Oncology, Virginia Commonwealth University, Richmond Virginia 23298 (United States)
Uncertainties in Predicted Ozone Concentrations Due to Input Uncertainties for the UAM-V Photochemical Grid Model
Based on studies of ozone episodes in the eastern United States using the photochemical grid model, UAM-V, regulatory agencies have made decisions concerning emissions controls. This project analyzes effects of uncertainties in UAM-V input variables (emissions, initial and boundary conditions, meteorological variables, and chemical reactions) on uncertainties in UAM-V ozone predictions for the July 1995 episode.
Uncertainty analysis of steady state incident heat flux measurements in hydrocarbon fuel fires.
The objective of this report is to develop uncertainty estimates for three heat flux measurement techniques used for the measurement of incident heat flux in a combined radiative and convective environment. This is related to the measurement of heat flux to objects placed inside hydrocarbon fuel (diesel, JP-8 jet fuel) fires, which is very difficult to make accurately (e.g., less than 10%). Three methods will be discussed: a Schmidt-Boelter heat flux gage; a calorimeter and inverse heat conduction method; and a thin plate and energy balance method. Steady state uncertainties were estimated for two types of fires (i.e., calm wind and high winds) at three times (early in the fire, late in the fire, and at an intermediate time). Results showed a large uncertainty for all three methods. Typical uncertainties for a Schmidt-Boelter gage ranged from {+-}23% for high wind fires to {+-}39% for low wind fires. For the calorimeter/inverse method the uncertainties were {+-}25% to {+-}40%. The thin plate/energy balance method the uncertainties ranged from {+-}21% to {+-}42%. The 23-39% uncertainties for the Schmidt-Boelter gage are much larger than the quoted uncertainty for a radiative only environment (i.e ., {+-}3%). This large difference is due to the convective contribution and because the gage sensitivities to radiative and convective environments are not equal. All these values are larger than desired, which suggests the need for improvements in heat flux measurements in fires.
Nakos, James Thomas
Sources of uncertainty in the calculation of loads on supports of piping systems
Loads on piping systems are obtained from an analysis of the piping system. The piping system analysis involves uncertainties from various sources. These sources of uncertainties are discussed and ranges of uncertainties are illustrated by simple examples. The sources of uncertainties are summarized and assigned a judgmental ranking of the typical relative significance of the uncertainty.
Rodabaugh, E.C.
The purpose of this presentation is to provide a brief introduction to measurement uncertainty analysis, outline how it is done, and illustrate uncertainty analysis with examples drawn from the PV field, with particular emphasis toward its use in PV performance measurements. The uncertainty information we know and state concerning a PV performance measurement or a module test result determines, to a significant extent, the value and quality of that result. What is measurement uncertainty analysis? It is an outgrowth of what has commonly been called error analysis. But uncertainty analysis, a more recent development, gives greater insight into measurement processes and tests, experiments, or calibration results. Uncertainty analysis gives us an estimate of the I interval about a measured value or an experiment`s final result within which we believe the true value of that quantity will lie. Why should we take the time to perform an uncertainty analysis? A rigorous measurement uncertainty analysis: Increases the credibility and value of research results; allows comparisons of results from different labs; helps improve experiment design and identifies where changes are needed to achieve stated objectives (through use of the pre-test analysis); plays a significant role in validating measurements and experimental results, and in demonstrating (through the post-test analysis) that valid data have been acquired; reduces the risk of making erroneous decisions; demonstrates quality assurance and quality control measures have been accomplished; define Valid Data as data having known and documented paths of: Origin, including theory; measurements; traceability to measurement standards; computations; uncertainty analysis of results.
PULCINELLA -- A General Tool for Propagating Uncertainty in Valuation Networks
We present PULCinella and its use in comparing uncertainty theories. PULCinella is a general tool for Propagating Uncertainty based on the Local Computation technique of Shafer and Shenoy. It may be specialized to different uncertainty theories: at the moment, Pulcinella can propagate probabilities, belief functions, Boolean values, and possibilities. Moreover, Pulcinella allows the user to easily define his own specializations. To illustrate Pulcinella, we analyze two examples by using each of the four theories above. In the first one, we mainly focus on intrinsic differences between theories. In the second one, we take a knowledge engineer viewpoint, and check the adequacy of each theory to a given problem.
Alessandro Saffiotti; Elisabeth Umkehrer
PDF uncertainties at large x and gauge boson production
I discuss how global QCD fits of parton distribution functions can make the somewhat separated fields of high-energy particle physics and lower energy hadronic and nuclear physics interact to the benefit of both. In particular, I will argue that large rapidity gauge boson production at the Tevatron and the LHC has the highest short-term potential to constrain the theoretical nuclear corrections to DIS data on deuteron targets necessary for up/down flavor separation. This in turn can considerably reduce the PDF uncertainty on cross section calculations of heavy mass particles such as W' and Z' bosons.
Accardi, Alberto [Hampton U., JLAB
Neural network uncertainty assessment using Bayesian statistics with application to remote sensing
Neural network uncertainty assessment using Bayesian statistics with application to remote sensing for many inversion problems in remote sensing; however, uncertainty estimates are rarely provided Meteorology and Atmospheric Dynamics: General or miscellaneous; KEYWORDS: remote sensing, uncertainty, neural
Aires, Filipe
Patent Protection, Market Uncertainty, and R&D Investment
Wesley M. Cohen. (2003). "R&D and the Patent Premuim," NBERDoes market uncertainty reduce R&D investments? A firm-level2001). "Irreversibility of R&D investment and the adverse
Toole, Andrew A; Czarnitzki, Dirk
Creating value from uncertainty : a study of ocean transportation contracting
How can financial tools like real options and hedging mitigate and create value from uncertainty in transportation? This paper describes these concepts and identifies research on them that has relevance to transportation. ...
Pálsson, Sigurjón
A Simple Equation for Regional Climate Change and Associated Uncertainty
Simple equations are developed to express regional climate changes for the twenty-first century and associated uncertainty in terms of the global temperature change (GTC) without a dependence on the underlying emission pathways. The equations are ...
Filippo Giorgi
Integration of Uncertainty Information into Power System Operations
Contemporary power systems face uncertainties coming from multiple sources, including forecast errors of load, wind and solar generation, uninstructed deviation and forced outage of traditional generators, loss of transmission lines, and others. With increasing amounts of wind and solar generation being integrated into the system, these uncertainties have been growing significantly. It is critical important to build knowledge of major sources of uncertainty, learn how to simulate them, and then incorporate this information into the decision-making processes and power system operations, for better reliability and efficiency. This paper gives a comprehensive view on the sources of uncertainty in power systems, important characteristics, available models, and ways of their integration into system operations. It is primarily based on previous works conducted at the Pacific Northwest National Laboratory (PNNL).
Makarov, Yuri V.; Lu, Shuai; Samaan, Nader A.; Huang, Zhenyu; Subbarao, Krishnappa; Etingov, Pavel V.; Ma, Jian; Hafen, Ryan P.; Diao, Ruisheng; Lu, Ning
Including model uncertainty in risk-informed decision-making
Model uncertainties can have a significant impact on decisions regarding licensing basis changes. We present a methodology to identify basic events in the risk assessment that have the potential to change the decision and ...
Reinert, Joshua M
Stochastic and Robust Approaches to Optimization Problems under Uncertainty
In the last decade, optimization models under uncertainty have drawn much attention and efficient algorithms have been developed for solving those problems. This article presents the author's recent attempts conducted in collaboration with a number of ...
Masao Fukushima
Designing transit concession contracts to deal with uncertainty
This thesis proposes a performance regime structure for public transit concession contracts, designed so incentives to the concessionaire can be effective given significant uncertainty about the future operating conditions. ...
Blakey, Tara Naomi Chin
Probabilistic Accident Consequence Uncertainty - A Joint CEC/USNRC Study
The joint USNRC/CEC consequence uncertainty study was chartered after the development of two new probabilistic accident consequence codes, MACCS in the U.S. and COSYMA in Europe. Both the USNRC and CEC had a vested interest in expanding the knowledge base of the uncertainty associated with consequence modeling, and teamed up to co-sponsor a consequence uncertainty study. The information acquired from the study was expected to provide understanding of the strengths and weaknesses of current models as well as a basis for direction of future research. This paper looks at the elicitation process implemented in the joint study and discusses some of the uncertainty distributions provided by eight panels of experts from the U.S. and Europe that were convened to provide responses to the elicitation. The phenomenological areas addressed by the expert panels include atmospheric dispersion and deposition, deposited material and external doses, food chain, early health effects, late health effects and internal dosimetry.
Gregory, Julie J.; Harper, Frederick T.
Modeling aviation's global emissions, uncertainty analysis, and applications to policy
(cont.) fuel burn results below 3000 ft. For emissions, the emissions indices were the most influential uncertainties for the variance in model outputs. By employing the model, this thesis examined three policy options for ...
Lee, Joosung Joseph, 1974-
in the natural gas price. Treat- ment of uncertainty viato risk from natural gas price volatility. In particular,exposure to the natural gas price and maximising its cost
Impact of PV forecasts uncertainty in batteries management in microgrids
Impact of PV forecasts uncertainty in batteries management in microgrids Andrea Michiorri Arthur-based battery schedule optimisation in microgrids in presence of network constraints. We examine a specific case
Recanati, Catherine
Reducing Uncertainty for the DeltaQ Duct Leakage Test
the DeltaQ duct Leakage Test". ASHRAE Transactions (inof a new Duct Leakage Test: DeltaQ. LBNL 47308. Walker, I,Uncertainties in the DeltaQ test for Duct Leakage.
Walker, Iain S.; Sherman, Max H.; Dickerhoff, Darryl J.
Real options approach to capacity planning under uncertainty
This thesis highlights the effectiveness of Real Options Analysis (ROA) in capacity planning decisions for engineering projects subject to uncertainty. This is in contrast to the irreversible decision-making proposed by ...
Mittal, Geetanjali, 1979-
Estimating Monthly Precipitation Reconstruction Uncertainty Beginning in 1900
Uncertainty estimates are computed for a statistical reconstruction of global monthly precipitation that was developed in an earlier publication. The reconstruction combined the use of spatial correlations with gauge precipitation and correlations ...
Thomas M. Smith; Samuel S. P. Shen; Li Ren; Phillip A. Arkin
Uncertainty management in a distributed knowledge based system
In many situations, a knowledge source may not give definite hypothesis; it can only express its belief and disbelief in multiple hypotheses, We present a scheme of uncertainty management in such a system. Disbelief is given a considerable importance ...
Naseem A. Khan; Ramesh Jain
Uncertainty in Greenhouse Emissions and Costs of Atmospheric Stabilization
We explore the uncertainty in projections of emissions, and costs of atmospheric stabilization applying the MIT Emissions Prediction and Policy Analysis model, a computable general equilibrium model of the global economy. ...
Webster, Mort D.
of the uncertainty in the natural gas price. Treat- ment ofits exposure to risk from natural gas price volatility. Inits exposure to the natural gas price and maximising its
Analysis and reduction of chemical models under uncertainty.
While models of combustion processes have been successful in developing engines with improved fuel economy, more costly simulations are required to accurately model pollution chemistry. These simulations will also involve significant parametric uncertainties. Computational singular perturbation (CSP) and polynomial chaos-uncertainty quantification (PC-UQ) can be used to mitigate the additional computational cost of modeling combustion with uncertain parameters. PC-UQ was used to interrogate and analyze the Davis-Skodje model, where the deterministic parameter in the model was replaced with an uncertain parameter. In addition, PC-UQ was combined with CSP to explore how model reduction could be combined with uncertainty quantification to understand how reduced models are affected by parametric uncertainty.
Oxberry, Geoff; Debusschere, Bert J.; Najm, Habib N.
Quantifying Global Uncertainties in a Simple Microwave Rainfall Algorithm
While a large number of methods exist in the literature for retrieving rainfall from passive microwave brightness temperatures, little has been written about the quantitative assessment of the expected uncertainties in these rainfall products at ...
Christian Kummerow; Wesley Berg; Jody Thomas-Stahle; Hirohiko Masunaga
Analysis of automated highway system risks and uncertainties. Volume 5
This volume describes a risk analysis performed to help identify important Automated Highway System (AHS) deployment uncertainties and quantify their effect on costs and benefits for a range of AHS deployment scenarios. The analysis identified a suite of key factors affecting vehicle and roadway costs, capacities and market penetrations for alternative AHS deployment scenarios. A systematic protocol was utilized for obtaining expert judgments of key factor uncertainties in the form of subjective probability percentile assessments. Based on these assessments, probability distributions on vehicle and roadway costs, capacity and market penetration were developed for the different scenarios. The cost/benefit risk methodology and analysis provide insights by showing how uncertainties in key factors translate into uncertainties in summary cost/benefit indices.
Sicherman, A.
Uncertainty of Tropical Cyclone Best-Track Information
With the growing use of tropical cyclone (TC) best-track information for weather and climate applications, it is important to understand the uncertainties that are contained in the TC position and intensity information. Here, an attempt is made to ...
Ryan D. Torn; Chris Snyder
Sensitivity Studies of the Models of Radar-Rainfall Uncertainties
It is well acknowledged that there are large uncertainties associated with the operational quantitative precipitation estimates produced by the U.S. national network of the Weather Surveillance Radar-1988 Doppler (WSR-88D). These errors result ...
Gabriele Villarini; Witold F. Krajewski
Reducing CO2 Emissions: Technology, Uncertainty, Decision Making...
Reducing CO2 Emissions: Technology, Uncertainty, Decision Making and Consumer Behavior Speaker(s): Ins Magarida Lima de Azevedo Date: October 31, 2012 - 4:00pm Location: 90-3122...
39] "Improving Thermal Power Accuracy and Plant Safety WhileCanyon Power Plant (DCPP) (California, USA), the thermalthermal power measure- ments and ?ssion rate calculations. The uncertainties in these quantities for commercial power plants
Djurcic, Zelimir
Uncertainties in the Radiative Forcing Due to Sulfate Aerosols
Radiative transfer calculations based on a new sulfate distribution from a chemistry-transport model simulation have been performed. A wide range of sensitivity experiments have been performed to illustrate the large uncertainty in the radiative ...
Gunnar Myhre; Frode Stordal; Tore F. Berglen; Jostein K. Sundet; Ivar S. A. Isaksen
Quantification of Cloud Microphysical Parameterization Uncertainty Using Radar Reflectivity
Uncertainty in cloud microphysical parameterization—a leading order contribution to numerical weather prediction error—is estimated using a Markov chain Monte Carlo (MCMC) algorithm. An inversion is performed on 10 microphysical parameters using ...
Marcus van Lier-Walqui; Tomislava Vukicevic; Derek J. Posselt
Ensemble Representation of Uncertainty in Lagrangian Satellite Rainfall Estimates
A new algorithm LSIM (Lagrangian Simulation) has been developed which enables the interpolation uncertainty present in Lagrangian satellite rainfall algorithms such as CMORPH to be characterized using an ensemble product. The new algorithm ...
T. J. Bellerby
Uncertainty Quantification Tools for Multiphase Flow Simulations using MFIX
Uncertainty Uncertainty Quantification Tools for Multiphase Flow Simulations using MFIX X. Hu 1 , A. Passalacqua 2 , R. O. Fox 1 1 Iowa State University, Department of Chemical and Biological Engineering, Ames, IA 2 Iowa State University, Department of Mechanical Engineering, Ames, IA Project Manager: Steve Seachman University Coal Research and Historically Black Colleges and Universities and Other Minority Institutions Contractors Review Conference Pittsburgh, June 11 th - 13 th 2013 X. Hu, A. Passalacqua, R. O. Fox (ISU) Uncertainty quantification DOE-UCR Review Meeting 2013 1 / 44 Outline 1 Introduction and background 2 Project objectives and milestones 3 Technical progress Univariate case Multivariate case Code structure 4 Future work X. Hu, A. Passalacqua, R. O. Fox (ISU) Uncertainty quantification DOE-UCR Review Meeting 2013 2 / 44 Introduction and background Outline 1 Introduction
Anti-neutrino emission rates from nuclear reactors are determined from thermal power measurements and fission rate calculations. The uncertainties in these quantities for commercial power plants and their impact on the calculated interaction rates in {bar {nu}}{sub e} detectors is examined. We discuss reactor-to-reactor correlations between the leading uncertainties, and their relevance to reactor {bar {nu}}{sub e} experiments.
Djurcic, Zelimir; Detwiler, Jason A.; Piepke, Andreas; Foster Jr., Vince R.; Miller, Lester; Gratta, Giorgio
Uncertainty Representation: Estimating Process Parameters for Forward Price Forecasting
Market prices set the value of electric power assets and contracts, yet forward prices are unavailable for time horizons relevant to most valuations. Price forecasts are inherently uncertain because the drivers of prices are uncertain, but equilibrating market forces also work to reduce the growth of uncertainty over time. Consequently, quantifying the degree of future price uncertainty is difficult, but has tremendous strategic potential for power companies seeking to value real options and invest in fl...
All wind farm uncertainty is not the same: The economics of common versus independent causes
There is uncertainty in the performance of wind energy installations due to unknowns in the local wind environment, machine response to the environment, and the durability of materials. Some of the unknowns are inherently independent from machine to machine while other uncertainties are common to the entire fleet equally. The FAROW computer software for fatigue and reliability of wind turbines is used to calculate the probability of component failure due to a combination of all sources of uncertainty. Although the total probability of component failure due to all effects is sometimes interpreted as the percentage of components likely to fail, this perception is often far from correct. Different amounts of common versus independent uncertainty are reflected in economic risk due to either high probabilities that a small percentage of the fleet will experience problems or low probabilities that the entire fleet will have problems. The average, or expected cost is the same as would be calculated by combining all sources of uncertainty, but the risk to the fleet may be quite different in nature. Present values of replacement costs are compared for two examples reflecting different stages in the design and development process. Results emphasize that an engineering effort to test and evaluate the design assumptions is necessary to advance a design from the high uncertainty of the conceptual stages to the lower uncertainty of a well engineered and tested machine.
Veers, P.S.
Retrieval of Optical Depth for Heavy Smoke Aerosol Plumes: Uncertainties and Sensitivities to the Optical Properties
This paper is concerned with uncertainties in the Advanced Very High Resolution Radiometer (AVHRR)-based retrieval of optical depth for heavy smoke aerosol plumes generated from forest fires that occurred in Canada due to a lack of knowledge on ...
Jeff Wong; Zhanqing Li
System architecture decisions under uncertainty : a case study on automotive battery system design
Flexibility analysis using the Real Options framework is typically utilized on high-level architectural decisions. Using Real Options, a company may develop strategies to mitigate downside risk for future uncertainties ...
Renzi, Matthew Joseph
Optimal Control of Distributed Energy Resources and Demand Response under Uncertainty
Energy Resources and Demand Response under Uncertainty AfzalEnergy Resources and Demand Response under Uncertainty ?DER in conjunction with demand response (DR): the expected
Uncertainty and sensitivity analysis for long-running computer codes : a critical review
This thesis presents a critical review of existing methods for performing probabilistic uncertainty and sensitivity analysis for complex, computationally expensive simulation models. Uncertainty analysis (UA) methods ...
Langewisch, Dustin R
Improvements to Nuclear Data and Its Uncertainties by Theoretical Modeling
This project addresses three important gaps in existing evaluated nuclear data libraries that represent a significant hindrance against highly advanced modeling and simulation capabilities for the Advanced Fuel Cycle Initiative (AFCI). This project will: Develop advanced theoretical tools to compute prompt fission neutrons and gamma-ray characteristics well beyond average spectra and multiplicity, and produce new evaluated files of U and Pu isotopes, along with some minor actinides; Perform state-of-the-art fission cross-section modeling and calculations using global and microscopic model input parameters, leading to truly predictive fission cross-sections capabilities. Consistent calculations for a suite of Pu isotopes will be performed; Implement innovative data assimilation tools, which will reflect the nuclear data evaluation process much more accurately, and lead to a new generation of uncertainty quantification files. New covariance matrices will be obtained for Pu isotopes and compared to existing ones. The deployment of a fleet of safe and efficient advanced reactors that minimize radiotoxic waste and are proliferation-resistant is a clear and ambitious goal of AFCI. While in the past the design, construction and operation of a reactor were supported through empirical trials, this new phase in nuclear energy production is expected to rely heavily on advanced modeling and simulation capabilities. To be truly successful, a program for advanced simulations of innovative reactors will have to develop advanced multi-physics capabilities, to be run on massively parallel super- computers, and to incorporate adequate and precise underlying physics. And all these areas have to be developed simultaneously to achieve those ambitious goals. Of particular interest are reliable fission cross-section uncertainty estimates (including important correlations) and evaluations of prompt fission neutrons and gamma-ray spectra and uncertainties.
Danon, Yaron; Nazarewicz, Witold; Talou, Patrick
Impact of PDF uncertainties at large x on heavy boson production
We explore the sensitivity of W and Z boson production in hadronic collisions to uncertainties in parton distribution functions (PDFs) at large x arising from uncertainties in nuclear corrections when using deuterium data in global QCD fits. The W and Z differential cross sections show increasing influence of nuclear corrections at high boson rapidities, particularly for the d quark, which is diluted somewhat in the decay lepton rapidity distributions. The effects of PDF uncertainties on heavy W' and Z' bosons beyond the Standard Model become progressively more important for larger boson masses or rapidities, both in p-p collisions at the LHC and in p-pbar scattering at the Tevatron.
L. T. Brady, A. Accardi, W. Melnitchouk, J. F. Owens
Assessing uncertainties in the relationship between inhaled particle
Assessing uncertainties in the relationship between inhaled particle Assessing uncertainties in the relationship between inhaled particle concentration, internal deposition and health effects, Chapter 9 Title Assessing uncertainties in the relationship between inhaled particle concentration, internal deposition and health effects, Chapter 9 Publication Type Book Chapter Year of Publication 2005 Authors Price, Phillip N. Secondary Authors Ruzer, Lev S., and Naomi H. Harley Book Title Aerosols Handbook: Measurement, Dosimetry and Health Effects Chapter Chapter Pagination 157-188 Publisher CRC Press, Boca Raton, FL Abstract The question that ultimately motivates most aerosol inhalation research is: for a given inhaled atmosphere, what health effects will result in a specified population? To attempt to address this question, quantitative research on inhaled aerosols has been performed for at least fifty years (Landahl et al, 1951). The physical factors that determine particle deposition have been determined, lung morphology has been quantified (particularly for adults), models of total particle deposition have been created and validated, and a large variety of inhalation experiments have been performed. However many basic questions remain, some of which are identified by the U.S. Committee on Research Priorities for Airborne Particulate Matter (NRC 1998a) as high-priority research areas. Among these are: What are the quantitative relationships between outdoor concentrations measured at stationary monitoring stations, and actual personal exposures? What are the exposures to biologically important constituents of particulate matter that cause responses in potentially susceptible subpopulations and the general population? What is the role of physicochemical characteristics of particulate matter in causing adverse health effects? As these questions show, in spite of significant progress in all areas of aerosol research, many of the most important practical questions remain unanswered or inadequately answered.In this chapter, we discuss the sources and magnitudes of error that hinder the ability to answer basic questions concerning the health effects of inhaled aerosols. We first consider the phenomena that affect the epidemiological studies, starting with studies of residential radon and moving on to fine particle air pollution. Next we discuss the major uncertainties in physical and physiological modeling of the causal chain that leads from inhaled aerosol concentration, to deposition in the airway, to time-dependent dose (that is, the concentration of particles at a given point in the lungs as function of time), to physiological effects, and finally to health effect.
Uncertainties in climatological tropical humidity profiles: Some implications for estimating the greenhouse effect
The vertical profile of water vapor, the principal infrared-absorbing gas in the atmosphere, is an important factor in determining the energy balance of the climate system. This study examines uncertainties in calculating a climatological humidity profile: specifically one derived from radiosonde data representative of the moist and highly convective region over the western tropical Pacific Ocean. Uncertainties in the humidity data are large in conditions of low temperature or low humidity in the mid- and upper troposphere. Results derived from a single United States station (Koror) and from an average of four United States-operated stations (all near the equator west of the date line) yield nearly identical results. No humidity measurements are reported in fully the upper third of the troposphere. The implications of these uncertainties for determining the climatological humidity profile are quantitatively assessed by bracketing the range of plausible assumptions for unreported humidity to produce extreme estimates of the climatological profile. These profiles, together with the observed climatological temperature profile, are used as input to a radiative transfer model to ascertain the uncertainty in clear-sky outgoing infrared radiance due to water vapor uncertainties. The radiance uncertainty is shown to be comparable in magnitude to the purely radiative response of the tropical atmosphere to doubling carbon dioxide. The uncertainty associated with unmeasured upper-tropospheric humidity is approximately equal to that arising from incompletely measured midtropospheric humidity. Clear-sky radiative uncertainties, however, are modest relative to the uncertainty associated with variations of infrared absorption due to clouds, as demonstrated by introducing citrus ice particles into the radiative transfer calculations.
Gutzler, D.S. (Atmospheric and Environmental Research, Inc., Cambridge, MA (United States))
PIV Uncertainty Methodologies for CFD Code Validation at the MIR Facility
Currently, computational fluid dynamics (CFD) is widely used in the nuclear thermal hydraulics field for design and safety analyses. To validate CFD codes, high quality multi dimensional flow field data are essential. The Matched Index of Refraction (MIR) Flow Facility at Idaho National Laboratory has a unique capability to contribute to the development of validated CFD codes through the use of Particle Image Velocimetry (PIV). The significance of the MIR facility is that it permits non intrusive velocity measurement techniques, such as PIV, through complex models without requiring probes and other instrumentation that disturb the flow. At the heart of any PIV calculation is the cross-correlation, which is used to estimate the displacement of particles in some small part of the image over the time span between two images. This image displacement is indicated by the location of the largest peak. In the MIR facility, uncertainty quantification is a challenging task due to the use of optical measurement techniques. Currently, this study is developing a reliable method to analyze uncertainty and sensitivity of the measured data and develop a computer code to automatically analyze the uncertainty/sensitivity of the measured data. The main objective of this study is to develop a well established uncertainty quantification method for the MIR Flow Facility, which consists of many complicated uncertainty factors. In this study, the uncertainty sources are resolved in depth by categorizing them into uncertainties from the MIR flow loop and PIV system (including particle motion, image distortion, and data processing). Then, each uncertainty source is mathematically modeled or adequately defined. Finally, this study will provide a method and procedure to quantify the experimental uncertainty in the MIR Flow Facility with sample test results.
Piyush Sabharwall; Richard Skifton; Carl Stoots; Eung Soo Kim; Thomas Conder
The ongoing deregulation of electricity industries worldwide is providing incentives for microgrids to use small-scale distributed generation (DG) and combined heat and power (CHP) applications via heat exchangers (HXs) to meet local energy loads. Although the electric-only efficiency of DG is lower than that of central-station production, relatively high tariff rates and the potential for CHP applications increase the attraction of on-site generation. Nevertheless, a microgrid contemplatingthe installation of gas-fired DG has to be aware of the uncertainty in the natural gas price. Treatment of uncertainty via real options increases the value of the investment opportunity, which then delays the adoption decision as the opportunity cost of exercising the investment option increases as well. In this paper, we take the perspective of a microgrid that can proceed in a sequential manner with DG capacity and HX investment in order to reduce its exposure to risk from natural gas price volatility. In particular, with the availability of the HX, the microgrid faces a tradeoff between reducing its exposure to the natural gas price and maximising its cost savings. By varying the volatility parameter, we find that the microgrid prefers a direct investment strategy for low levels of volatility and a sequential one for higher levels of volatility.
Siddiqui, Afzal; Maribu, Karl
Uncertainty Quantification and Calibration in Well Construction Cost Estimates
The feasibility and success of petroleum development projects depend to a large degree on well construction costs. Well construction cost estimates often contain high levels of uncertainty. In many cases, these costs have been estimated using deterministic methods that do not reliably account for uncertainty, leading to biased estimates. The primary objective of this work was to improve the reliability of deterministic well construction cost estimates by incorporating probabilistic methods into the estimation process. The method uses historical well cost estimates and actual well costs to develop probabilistic correction factors that can be applied to future well cost estimates. These factors can be applied to the entire well cost or to individual cost components. Application of the methodology to estimation of well construction costs for horizontal wells in a shale gas play resulted in well cost estimates that were well calibrated probabilistically. Overall, average estimated well cost using this methodology was significantly more accurate than average estimated well cost using deterministic methods. Systematic use of this methodology can provide for more accurate and efficient allocation of capital for drilling campaigns, which should have significant impacts on reservoir development and profitability.
Valdes Machado, Alejandro
Investment and Upgrade in Distributed Generation under Uncertainty ?
The ongoing deregulation of electricity industries worldwide is providing incentives for microgrids to use small-scale distributed generation (DG) and combined heat and power (CHP) applications via heat exchangers (HXs) to meet local energy loads. Although the electric-only efficiency of DG is lower than that of central-station production, relatively high tariff rates and the potential for CHP applications increase the attraction of on-site generation. Nevertheless, a microgrid contemplating the installation of gas-fired DG has to be aware of the uncertainty in the natural gas price. Treatment of uncertainty via real options increases the value of the investment opportunity, which then delays the adoption decision as the opportunity cost of exercising the investment option increases as well. In this paper, we take the perspective of a microgrid that can proceed in a sequential manner with DG capacity and HX investment in order to reduce its exposure to risk from natural gas price volatility. In particular, with the availability of the HX, we find that the microgrid faces a tradeoff between reducing its exposure to the natural gas price and maximising its cost savings. By varying the volatility parameter, we find ranges over which direct and sequential investment strategies dominate. Keywords:
Afzal Siddiqui; Karl Maribu
Sensitivity and uncertainty analysis applied to the JHR reactivity prediction
The on-going AMMON program in EOLE reactor at CEA Cadarache (France) provides experimental results to qualify the HORUS-3D/N neutronics calculation scheme used for the design and safety studies of the new Material Testing Jules Horowitz Reactor (JHR). This paper presents the determination of technological and nuclear data uncertainties on the core reactivity and the propagation of the latter from the AMMON experiment to JHR. The technological uncertainty propagation was performed with a direct perturbation methodology using the 3D French stochastic code TRIPOLI4 and a statistical methodology using the 2D French deterministic code APOLLO2-MOC which leads to a value of 289 pcm (1{sigma}). The Nuclear Data uncertainty propagation relies on a sensitivity study on the main isotopes and the use of a retroactive marginalization method applied to the JEFF 3.1.1 {sup 27}Al evaluation in order to obtain a realistic multi-group covariance matrix associated with the considered evaluation. This nuclear data uncertainty propagation leads to a K{sub eff} uncertainty of 624 pcm for the JHR core and 684 pcm for the AMMON reference configuration core. Finally, transposition and reduction of the prior uncertainty were made using the Representativity method which demonstrates the similarity of the AMMON experiment with JHR (the representativity factor is 0.95). The final impact of JEFF 3.1.1 nuclear data on the Begin Of Life (BOL) JHR reactivity calculated by the HORUS-3D/N V4.0 is a bias of +216 pcm with an associated posterior uncertainty of 304 pcm (1{sigma}). (authors)
Leray, O.; Vaglio-Gaudard, C.; Hudelot, J. P.; Santamarina, A.; Noguere, G. [CEA, DER, SPRC, F-13108 St Paul-Lez-Durance (France); Di-Salvo, J. [CEA, DER, SPEx, F-13108 St Paul-Lez-Durance (France)
Visual Scanning Hartmann Optical Tester (VSHOT) Uncertainty Analysis (Milestone Report)
In 1997, an uncertainty analysis was conducted of the Video Scanning Hartmann Optical Tester (VSHOT). In 2010, we have completed a new analysis, based primarily on the geometric optics of the system, and it shows sensitivities to various design and operational parameters. We discuss sources of error with measuring devices, instrument calibrations, and operator measurements for a parabolic trough mirror panel test. These help to guide the operator in proper setup, and help end-users to understand the data they are provided. We include both the systematic (bias) and random (precision) errors for VSHOT testing and their contributions to the uncertainty. The contributing factors we considered in this study are: target tilt; target face to laser output distance; instrument vertical offset; laser output angle; distance between the tool and the test piece; camera calibration; and laser scanner. These contributing factors were applied to the calculated slope error, focal length, and test article tilt that are generated by the VSHOT data processing. Results show the estimated 2-sigma uncertainty in slope error for a parabolic trough line scan test to be +/-0.2 milliradians; uncertainty in the focal length is +/- 0.1 mm, and the uncertainty in test article tilt is +/- 0.04 milliradians.
Gray, A.; Lewandowski, A.; Wendelin, T.
IAEA CRP on HTGR Uncertainty Analysis: Benchmark Definition and Test Cases
Uncertainty and sensitivity studies are essential elements of the reactor simulation code verification and validation process. Although several international uncertainty quantification activities have been launched in recent years in the LWR, BWR and VVER domains (e.g. the OECD/NEA BEMUSE program [1], from which the current OECD/NEA LWR Uncertainty Analysis in Modelling (UAM) benchmark [2] effort was derived), the systematic propagation of uncertainties in cross-section, manufacturing and model parameters for High Temperature Reactor (HTGR) designs has not been attempted yet. This paper summarises the scope, objectives and exercise definitions of the IAEA Coordinated Research Project (CRP) on HTGR UAM [3]. Note that no results will be included here, as the HTGR UAM benchmark was only launched formally in April 2012, and the specification is currently still under development.
Gerhard Strydom; Frederik Reitsma; Hans Gougar; Bismark Tyobeka; Kostadin Ivanov
Adaptive Optimization and Systematic Probing of Infrastructure System Maintenance Policies under Model Uncertainty
Maintenance; Optimization; Probabilistic models; Adaptive systems; Uncertainty principles . Introduction Infrastructure management systems
Madanat, S M; Park, Sejung; Kuhn, K D
Microsoft Word - Documentation - Price Forecast Uncertainty.doc
U.S. Energy Information Administration (EIA) Indexed Site
October 2009 October 2009 1 October 2009 Short-Term Energy Outlook Supplement: Energy Price Volatility and Forecast Uncertainty 1 Summary It is often noted that energy prices are quite volatile, reflecting market participants' adjustments to new information from physical energy markets and/or markets in energy- related financial derivatives. Price volatility is an indication of the level of uncertainty, or risk, in the market. This paper describes how markets price risk and how the market- clearing process for risk transfer can be used to generate "price bands" around observed futures prices for crude oil, natural gas, and other commodities. These bands provide a quantitative measure of uncertainty regarding the range in which markets expect prices to
Statistical uncertainty analysis of radon transport in nonisothermal, unsaturated soils
To accurately predict radon fluxes soils to the atmosphere, we must know more than the radium content of the soil. Radon flux from soil is affected not only by soil properties, but also by meteorological factors such as air pressure and temperature changes at the soil surface, as well as the infiltration of rainwater. Natural variations in meteorological factors and soil properties contribute to uncertainty in subsurface model predictions of radon flux, which, when coupled with a building transport model, will also add uncertainty to predictions of radon concentrations in homes. A statistical uncertainty analysis using our Rn3D finite-element numerical model was conducted to assess the relative importance of these meteorological factors and the soil properties affecting radon transport. 10 refs., 10 figs., 3 tabs.
Holford, D.J.; Owczarski, P.C.; Gee, G.W.; Freeman, H.D.
Modified Phenomena Identification and Ranking Table (PIRT) for Uncertainty Analysis
This paper describes a methodology of characterizing important phenomena, which is also part of a broader research by the authors called 'Modified PIRT'. The methodology provides robust process of phenomena identification and ranking process for more precise quantification of uncertainty. It is a two-step process of identifying and ranking methodology based on thermal-hydraulics (TH) importance as well as uncertainty importance. Analytical Hierarchical Process (AHP) has been used for as a formal approach for TH identification and ranking. Formal uncertainty importance technique is used to estimate the degree of credibility of the TH model(s) used to represent the important phenomena. This part uses subjective justification by evaluating available information and data from experiments, and code predictions. The proposed methodology was demonstrated by developing a PIRT for large break loss of coolant accident LBLOCA for the LOFT integral facility with highest core power (test LB-1). (authors)
Gol-Mohamad, Mohammad P.; Modarres, Mohammad; Mosleh, Ali [University of Maryland, College Park, MD 20742 (United States)
Uncertainty quantification and validation of combined hydrological and macroeconomic analyses.
Changes in climate can lead to instabilities in physical and economic systems, particularly in regions with marginal resources. Global climate models indicate increasing global mean temperatures over the decades to come and uncertainty in the local to national impacts means perceived risks will drive planning decisions. Agent-based models provide one of the few ways to evaluate the potential changes in behavior in coupled social-physical systems and to quantify and compare risks. The current generation of climate impact analyses provides estimates of the economic cost of climate change for a limited set of climate scenarios that account for a small subset of the dynamics and uncertainties. To better understand the risk to national security, the next generation of risk assessment models must represent global stresses, population vulnerability to those stresses, and the uncertainty in population responses and outcomes that could have a significant impact on U.S. national security.
Hernandez, Jacquelynne; Parks, Mancel Jordan; Jennings, Barbara Joan; Kaplan, Paul Garry; Brown, Theresa Jean; Conrad, Stephen Hamilton
Analysis of the lumped fission-product uncertainty in CRBR
An approximation made in most fast reactor analyses is to utilize a single lumped fission product (FP) cross-section set to represent the total effect of the approximately 800 possible fission product nuclides in a depleted reactor model. Recent investigations have analyzed several aspects of this one-lump approximation (burnup dependence, comparison with a two-lump model, etc.), but little has been done in addressing the quality, or uncertainty, in the basic cross-section data utilized in any such representation. Thus, the purpose of this study is to investigate the uncertainty in the FP reactivity effect due to the data uncertainties inherent in an ENDF/B-V composite FP representation specifically designed for application with the current CRBR heterogeneous core concept.
White, J.R.; Schenter, R.E.
Using dual decomposition for solving problems involving data uncertainty |
dual decomposition for solving problems involving data uncertainty dual decomposition for solving problems involving data uncertainty August 14, 2013 Tweet EmailPrint Many applications mdash; energy, routing, scheduling, and production planning, for example mdash; involve problems in which some or all of the data may not be known when decisions under uncertainty must be made. In such cases, approximations with stochastic mixed-integer programming models are often used. Two approaches have been suggested to address such problems: dual decomposition (DD) and branch-and-price (BP). Both approaches divide the problem into two or more subproblems, together with linear constraints that enforce agreement between solutions to the different problems through a series of iterations. Unfortunately, both approaches also suffer from lack
Incorporating uncertainty into electric utility projections and decisions
This paper focuses on how electric utility companies can respond in their decision making to uncertain variables. Here we take a mean- variance type of approach. The mean'' value is an expected cost, on a discounted value basis. We assume that management has risk preferences incorporating a tradeoff between the mean and variance in the utility's net income. Decisions that utilities are faced with can be classified into two types: ex ante and ex post. The ex ante decisions need to be made prior to the uncertainty being revealed and the ex post decision can be postponed until after the uncertainty is revealed. Intuitively, we can say that the ex ante decisions provide a hedge against the uncertainties and the ex post decisions allow the negative outcomes of uncertain variables to be partially mitigated, dampening the losses. An example of an ex post decision is how the system is operated i.e., unit dispatch, and in some cases switching among types of fuels, say with different sulfur contents. For example, if gas prices go up, natural gas combined cycle units are likely to be dispatched at lower capacity factors. If SO{sub 2} emission allowance prices go up, a utility may seek to switch into a lower sulfur coal. Here we assume that regulated electric utilities do have some incentive to lower revenue requirements and hence an incentive to lower the electric rates needed for the utility to break even, thereby earning a fair return on invested capital. This paper presents the general approach first, including applications to capacity expansion and system dispatch. Then a case study is presented focusing on the 1990 Clean Air Act Amendments including SO{sub 2} emissions abatement and banking of allowances under uncertainty. It is concluded that the emission banking decisions should not be made in isolation but rather all the uncertainties in demand, fuel prices, technology performance etc., should be included in the uncertainty analysis affecting emission banking.
Hanson, D.A.
This paper focuses on how electric utility companies can respond in their decision making to uncertain variables. Here we take a mean- variance type of approach. The ``mean`` value is an expected cost, on a discounted value basis. We assume that management has risk preferences incorporating a tradeoff between the mean and variance in the utility`s net income. Decisions that utilities are faced with can be classified into two types: ex ante and ex post. The ex ante decisions need to be made prior to the uncertainty being revealed and the ex post decision can be postponed until after the uncertainty is revealed. Intuitively, we can say that the ex ante decisions provide a hedge against the uncertainties and the ex post decisions allow the negative outcomes of uncertain variables to be partially mitigated, dampening the losses. An example of an ex post decision is how the system is operated i.e., unit dispatch, and in some cases switching among types of fuels, say with different sulfur contents. For example, if gas prices go up, natural gas combined cycle units are likely to be dispatched at lower capacity factors. If SO{sub 2} emission allowance prices go up, a utility may seek to switch into a lower sulfur coal. Here we assume that regulated electric utilities do have some incentive to lower revenue requirements and hence an incentive to lower the electric rates needed for the utility to break even, thereby earning a fair return on invested capital. This paper presents the general approach first, including applications to capacity expansion and system dispatch. Then a case study is presented focusing on the 1990 Clean Air Act Amendments including SO{sub 2} emissions abatement and banking of allowances under uncertainty. It is concluded that the emission banking decisions should not be made in isolation but rather all the uncertainties in demand, fuel prices, technology performance etc., should be included in the uncertainty analysis affecting emission banking.
This paper examines a California-based microgrid?s decision to invest in a distributed generation (DG) unit fuelled by natural gas. While the long-term natural gas generation cost is stochastic, we initially assume that the microgrid may purchase electricity at a fixed retail rate from its utility. Using the real options approach, we find a natural gas generation cost threshold that triggers DG investment. Furthermore, the consideration of operational flexibility by the microgrid increases DG investment, while the option to disconnect from the utility is not attractive. By allowing the electricity price to be stochastic, we next determine an investment threshold boundary and find that high electricity price volatility relative to that of natural gas generation cost delays investment while simultaneously increasing the value of the investment. We conclude by using this result to find the implicit option value of the DG unit when two sources of uncertainty exist.
Marnay, Chris; Siddiqui, Afzal; Marnay, Chris
Guideline for the Treatment of Uncertainty in Risk-Informed Applications
In all scientific and engineering endeavors, there exists some level of uncertainty about the outcome. The development and application of a probabilistic risk assessment (PRA) is no exception. The uncertainty in the PRA model and its results manifests itself in several forms, including parametric, modeling, and completeness uncertainty. Various methods exist to both account for uncertainty in the model and evaluate the impact of uncertainty on the outcome of the analysis. However, these methods have gene...
Estimating uncertainty in thermal-hydraulic codes using the linear variate method
Thermal-hydraulic codes are subject o uncertainties that must be considered in determining whether safety criteria are satisfied in nuclear reactors. Uncertainties correspond to parameters in a thermal-hydraulic model. A thermal-hydraulic model is typically a nonlinear, discontinuous function of the uncertainties. Evaluating the effect of the uncertainties is difficult. This paper describes an efficient Monte Carlo method for determining the effect of the uncertainties.
Kubic, W.L. Jr. (Los Alamos National Laboratory, NM (USA)); White, A.M. (Westinghouse Savannah River Company, Aiken, SC (USA))
Quantification of Uncertainties Due to Opacities in a Laser-Driven Radiative-Shock Problem
This research presents new physics-based methods to estimate predictive uncertainty stemming from uncertainty in the material opacities in radiative transfer computations of key quantities of interest (QOIs). New methods are needed because it is infeasible to apply standard uncertainty-propagation techniques to the O(105) uncertain opacities in a realistic simulation. The new approach toward uncertainty quantification applies the uncertainty analysis to the physical parameters in the underlying model used to calculate the opacities. This set of uncertain parameters is much smaller (O(102)) than the number of opacities. To further reduce the dimension of the set of parameters to be rigorously explored, we use additional screening applied at two different levels of the calculational hierarchy: first, physics-based screening eliminates the physical parameters that are unimportant from underlying physics models a priori; then, sensitivity analysis in simplified versions of the complex problem of interest screens out parameters that are not important to the QOIs. We employ a Bayesian Multivariate Adaptive Regression Spline (BMARS) emulator for this sensitivity analysis. The high dimension of the input space and large number of samples test the efficacy of these methods on larger problems. Ultimately, we want to perform uncertainty quantification on the large, complex problem with the reduced set of parameters. Results of this research demonstrate that the QOIs for target problems agree at for different parameter screening criteria and varying sample sizes. Since the QOIs agree, we have gained confidence in our results using the multiple screening criteria and sample sizes.
Hetzler, Adam C
Probabilistic accident consequence uncertainty analysis -- Uncertainty assessment for deposited material and external doses. Volume 1: Main report
The development of two new probabilistic accident consequence codes, MACCS and COSYMA, was completed in 1990. These codes estimate the consequence from the accidental releases of radiological material from hypothesized accidents at nuclear installations. In 1991, the US Nuclear Regulatory Commission and the Commission of the European Communities began cosponsoring a joint uncertainty analysis of the two codes. The ultimate objective of this joint effort was to systematically develop credible and traceable uncertainty distributions for the respective code input variables. A formal expert judgment elicitation and evaluation process was identified as the best technology available for developing a library of uncertainty distributions for these consequence parameters. This report focuses on the results of the study to develop distribution for variables related to the MACCS and COSYMA deposited material and external dose models.
Sensitivity Analysis and Uncertainty Quantification in Durability ...
Corrosion Control for Safe Interim Storage of Nuclear Reprocessing Waste · Corrosion of High ... Radiation Damage in Zircon by High-Energy Electron Beams.
Evaluate error sources and uncertainty in large scale measurement systems
Modern manufacturing technologies place increasingly higher demands on industrial measurement systems. Over the last decade there have been rapid developments in 3D measurement systems, with the primary requirement coming from industries such as automotives, ... Keywords: Best fit methods, Laser scanner, Measurement errors, Uncertainty
Qing Wang; Nick Zissler; Roger Holden
Polynomial regression with derivative information in nuclear reactor uncertainty quantification*
1 Polynomial regression with derivative information in nuclear reactor uncertainty quantification in the outputs. The usual difficulties in modeling the work of the nuclear reactor models include the large size, applying the existing AD tools to nuclear reactor models still takes considerable development effort
Anitescu, Mihai
Atmospheric Lagrangian coherent structures considering unresolved turbulence and forecast uncertainty
Atmospheric Lagrangian coherent structures considering unresolved turbulence and forecast, the uncertainty of the forecast FTLE fields is analyzed using ensemble forecasting. Unavoidable errors of the forecast velocity data due to the chaotic dynamics of the atmosphere is the salient reason for errors
Ross, Shane
1 APPLICATION OF PROBABILISTIC FORECASTS: DECISION MAKING WITH FORECAST UNCERTAINTY Rick Katz.isse.ucar.edu/HP_rick/dmuu.pdf #12;2 QUOTES ON USE OF PROBABILITY FORECASTS · Lao Tzu (Chinese Philosopher) "He who knows does and Value of Probability Forecasts (4) Cost-Loss Decision-Making Model (5) Simulation Example (6) Economic
Katz, Richard
Urgency, uncertainty, and innovation: Building jet engines in postwar America
Organizational history and theory have in recent years begun to integrate the non rational dimensions of action, relationships, and problem-solving with foundational under-standings of rationality.This study demonstrates that when insufficient knowledge ... Keywords: Cold war, innovation, jet propulsion, military, non-linearity, technology, uncertainty
Philip Scranton
Modeling facial expression of uncertainty in conversational animation
Building animated conversational agents requires developing a fine-grained analysis of the motions and meanings available to interlocutors in face-to-face conversation and implementing strategies for using these motions and meanings to communicate effectively. ... Keywords: embodied conversational agents, face-to-face conversation, facial displays, uncertainty
Matthew Stone; Insuk Oh
Shahab D. Mohaghegh, WVU, ISI Quantifying Uncertainties Associated
blocks. Single Run = 10 Hours on 12 CPUs. Water Injection for Pressure Maintenance. #12;13 Shahab D of next generation of reservoir management tools that would address the needs of smart fields. #12 of the ideas for rate increase by the management, if we show that: We are aware of the uncertainties associated
Mohaghegh, Shahab
Toward an Uncertainty Budget for a Coastal Ocean Model
Estimates of three components of an uncertainty budget for a coastal ocean model in a wind-forced regime are made based on numerical simulations. The budget components behave differently in the shelf regime, inshore of the 200-m isobath, and the ...
Sangil Kim; R. M. Samelson; Chris Snyder
Quantum covariance, quantum Fisher information and the uncertainty principle
In this paper the relation between quantum covariances and quantum Fisher informations are studied. This study is applied to generalize a recently proved uncertainty relation based on quantum Fisher information. The proof given hereconsiderably simplifies the previously proposed proofs and leads to more general inequalities.
Paolo Gibilisco; Fumio Hiai; Denes Petz
Mathematical treatment of uncertainty in the speech recognition process
One of the main difficulties in the speech recognition process is the treatment of the imprecisions around it. They have origin in the differences between the articulatory system of each person and the physical properties of the sound propagation. Moreover, ... Keywords: Hidden Markov model, fuzzy, interval analysis, mathematical treatment, speech recognition, uncertainty
Hesdras Oliveira Viana; Diogo Pereira Silva De Novais; Roque Mendes Prado Trindade
Distributed Generation Investment by a Microgrid under Uncertainty++++ Afzal Siddiqui
1 Distributed Generation Investment by a Microgrid under Uncertainty++++ Afzal Siddiqui University, CA 94720-8163, USA, email@example.com ABSTRACT. This paper examines a California-based microgrid-term natural gas generation cost is stochastic, we initially assume that the microgrid may purchase electricity
Guillas, Serge
Feedback based adaptive compensation of control system sensor uncertainties
In this paper, the problem of adaptively compensating sensor uncertainties is addressed in a feedback based framework. In this study, sensor characteristics are modeled as parametrizable uncertain functions and a compensator is constructed to adaptively ... Keywords: Adaptive control, Adaptive systems, Sensor and data fusion, Tracking and adaptation
Shanshan Li; Gang Tao
Geographic information retrieval: Modeling uncertainty of user's context
Geographic information retrieval (GIR) is nowadays a hot research issue that involves the management of uncertainty and imprecision and the modeling of user preferences and context. Indexing the geographic content of documents implies dealing with the ... Keywords: Bipolar criteria evaluation, Context dependent spatial query, Fuzzy aggregation operators, Geographic footprint, Geographic information retrieval, Soft constraint
Gloria Bordogna; Giorgio Ghisalberti; Giuseppe Psaila
Analyzing uncertainty in TG protection graphs with TG/MC
We introduce TG/MC, a Monte Carlo approach for evaluating the impact of uncertainty about vulnerabilities upon forecasts of security for a real-world system modeled by a protection graph. A TG/MC model defines a vulnerability as a potential change to ... Keywords: Monte Carlo, TG, Take-Grant, protection graph, security
James R. Conrad; Jim Alves-Foss; Sauchi Stephen Lee
Coal supply and cost under technological and environmental uncertainty
Coal supply and cost under technological and environmental uncertainty Submitted in partial, and Rod Lawrence at Foundation Coal. I received a lot of feedback and input on this report, and would like chapters. My conversations with Kurt Walzer at Clean Air Task Force and Rory McIlmoil at Coal Valley Wind
Decision Based Uncertainty Propagation Using Adaptive Gaussian Mixtures
Propagation, Expected Loss, Improved Forecast. I. Introduction Chemical, Biological, Radiological, and Nuclear (DM) toolbox. Based on these forecasts, decisions can be made on evacuating cities, sheltering into a threat level, such as the population density in a city. Thus the ability to propagate the uncertainty
Singh, Tarunraj
Managing Wind Power Forecast Uncertainty in Electric Brandon Keith Mauch
i Managing Wind Power Forecast Uncertainty in Electric Grids Brandon Keith Mauch Co in Electric Grids Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy for aggregated wind farms are often modeled with Gaussian distributions. However, data from several studies have
Stochastic Programming of Vehicle to Building Interactions with Uncertainty
Stochastic Programming of Vehicle to Building Interactions with Uncertainty Stochastic Programming of Vehicle to Building Interactions with Uncertainty in PEVs Driving for a Medium Office Building Title Stochastic Programming of Vehicle to Building Interactions with Uncertainty in PEVs Driving for a Medium Office Building Publication Type Conference Paper LBNL Report Number LBNL-6416E Year of Publication 2013 Authors Cardoso, Gonçalo, Michael Stadler, Mohammad Bozchalui, Ratnesh Sharma, Chris Marnay, Ana Barbosa-Póvoa, and Paulo Ferrão Conference Name 39th Annual Conference of the IEEE Industrial Electronics Society Date Published 10/2013 Conference Location Vienna, Austria Abstract The large scale penetration of electric vehicles (EVs) will introduce technical challenges to the distribution grid, but also carries the potential for vehicle-to-grid services. Namely, if available in large enough numbers, EVs can be used as a distributed energy resource (DER) and their presence can influence optimal DER investment and scheduling decisions in microgrids. In this work, a novel EV fleet aggregator model is introduced in a stochastic formulation of DER-CAM, an optimization tool used to address DER investment and scheduling problems. This is used to assess the impact of EV interconnections on optimal DER solutions considering uncertainty in EV driving schedules. Optimization results indicate that EVs can have a significant impact on DER investments, particularly if considering short payback periods. Furthermore, results suggest that uncertainty in driving schedules carries little significance to total energy costs, which is corroborated by results obtained with the stochastic formulation of the problem.
Natural Gas Price Uncertainty: Establishing Price Floors
This report presents the results of comprehensive calculations of ceiling and floor prices for natural gas. Ceiling prices are set by the price levels at which it is more economic to switch from natural gas to residual fuel oil in steam units and to distillate in combined cycle units. Switching to distillate is very rare, whereas switching to fuel oil is quite common, varying between winter and summer and increasing when natural gas prices are high or oil prices low. Monthly fuel use was examined for 89 ...
We validated one year of Global Forecast System (GFS) predictions of surface meteorological variables (wind speed, air temperature, dewpoint temperature, air pressure) over the entire planet for forecasts extending from zero hours into the future (an analysis) to 36 hours. Approximately 12,000 surface stations world-wide were included in this analysis. Root-Mean-Square- Errors (RMSE) increased as the forecast period increased from zero to 36 hours, but the initial RMSE were almost as large as the 36 hour forecast RMSE for all variables. Typical RMSE were 3 C for air temperature, 2-3mb for sea-level pressure, 3.5 C for dewpoint temperature and 2.5 m/s for wind speed. Approximately 20-40% of the GFS errors can be attributed to a lack of resolution of local features. We attribute the large initial RMSE for the zero hour forecasts to the inability of the GFS to resolve local terrain features that often dominate local weather conditions, e.g., mountain- valley circulations and sea and land breezes. Since the horizontal resolution of the GFS (about 1{sup o} of latitude and longitude) prevents it from simulating these locally-driven circulations, its performance will not improve until model resolution increases by a factor of 10 or more (from about 100 km to less than 10 km). Since this will not happen in the near future, an alternative for the near term to improve surface weather analyses and predictions for specific points in space and time would be implementation of a high-resolution, limited-area mesoscale atmospheric prediction model in regions of interest.
Werth, D.; Garrett, A.
River meander modeling and confronting uncertainty.
This study examines the meandering phenomenon as it occurs in media throughout terrestrial, glacial, atmospheric, and aquatic environments. Analysis of the minimum energy principle, along with theories of Coriolis forces (and random walks to explain the meandering phenomenon) found that these theories apply at different temporal and spatial scales. Coriolis forces might induce topological changes resulting in meandering planforms. The minimum energy principle might explain how these forces combine to limit the sinuosity to depth and width ratios that are common throughout various media. The study then compares the first order analytical solutions for flow field by Ikeda, et al. (1981) and Johannesson and Parker (1989b). Ikeda's et al. linear bank erosion model was implemented to predict the rate of bank erosion in which the bank erosion coefficient is treated as a stochastic variable that varies with physical properties of the bank (e.g., cohesiveness, stratigraphy, or vegetation density). The developed model was used to predict the evolution of meandering planforms. Then, the modeling results were analyzed and compared to the observed data. Since the migration of a meandering channel consists of downstream translation, lateral expansion, and downstream or upstream rotations several measures are formulated in order to determine which of the resulting planforms is closest to the experimental measured one. Results from the deterministic model highly depend on the calibrated erosion coefficient. Since field measurements are always limited, the stochastic model yielded more realistic predictions of meandering planform evolutions. Due to the random nature of bank erosion coefficient, the meandering planform evolution is a stochastic process that can only be accurately predicted by a stochastic model.
Posner, Ari J. (University of Arizona Tucson, AZ)
Performance evaluation of passive cooling in office buildings based on uncertainty and sensitivity analysis
Natural night ventilation is an interesting passive cooling method in moderate climates. Driven by wind and stack generated pressures, it cools down the exposed building structure at night, in which the heat of the previous day is accumulated. The performance of natural night ventilation highly depends on the external weather conditions and especially on the outdoor temperature. An increase of this outdoor temperature is noticed over the last century and the IPCC predicts an additional rise to the end of this century. A methodology is needed to evaluate the reliable operation of the indoor climate of buildings in case of warmer and uncertain summer conditions. The uncertainty on the climate and on other design data can be very important in the decision process of a building project. The aim of this research is to develop a methodology to predict the performance of natural night ventilation using building energy simulation taking into account the uncertainties in the input. The performance evaluation of natural night ventilation is based on uncertainty and sensitivity analysis. The results of the uncertainty analysis showed that thermal comfort in a single office cooled with single-sided night ventilation had the largest uncertainty. The uncertainties on thermal comfort in case of passive stack and cross ventilation were substantially smaller. However, since wind, as the main driving force for cross ventilation, is highly variable, the cross ventilation strategy required larger louvre areas than the stack ventilation strategy to achieve a similar performance. The differences in uncertainty between the orientations were small. Sensitivity analysis was used to determine the most dominant set of input parameters causing the uncertainty on thermal comfort. The internal heat gains, solar heat gain coefficient of the sunblinds, internal convective heat transfer coefficient, thermophysical properties related to thermal mass, set-point temperatures controlling the natural night ventilation, the discharge coefficient C{sub d} of the night ventilation opening and the wind pressure coefficients C{sub p} were identified to have the largest impact on the uncertainty of thermal comfort. The impact of the warming climate on the uncertainty of thermal comfort was determined. The uncertainty on thermal comfort appeared to increase significantly when a weather data set with recurrence time of 10 years (warm weather) was applied in the transient simulations in stead of a standard weather data set. Natural night ventilation, designed for normal weather conditions, was clearly not able to ensure a high probability of good thermal comfort in warm weather. To ensure a high probability of good thermal comfort and to reduce the performance uncertainty in a warming climate, natural night ventilation has to be combined with additional measures. Different measures were analysed, based on the results of the sensitivity analysis. All the measures were shown to significantly decrease the uncertainty of thermal comfort in warm weather. The study showed the importance to carry out simulations with a warm weather data set together with the analysis under typical conditions. This approach allows to gain a better understanding of the performance of a natural night ventilation design, and to optimize the design to a robust solution. (author)
Breesch, H. [Building Physics, Construction and Services, Department of Architecture and Urban Planning, Ghent University, J. Plateaustraat 22, B-9000 Ghent (Belgium); Sustainable Building Research Group, Department of Construction, Catholic University College Ghent, Gebroeders Desmetstraat 1, B-9000 Ghent (Belgium); Janssens, A. [Building Physics, Construction and Services, Department of Architecture and Urban Planning, Ghent University, J. Plateaustraat 22, B-9000 Ghent (Belgium)
Quantifying the Uncertainty in Estimates of World Conventional Oil Resources
Since Hubbert proposed the "peak oil" concept to forecast ultimate recovery of crude oil for the U.S. and the world, there have been countless debates over the timing of peak world conventional oil production rate and ultimate recovery. From review of the literature, forecasts were grouped into those that are like Hubbert's with an imminent peak, and those that do not predict an imminent peak. Both groups have bases for their positions. Viewpoints from the two groups are polarized and the rhetoric is pointed and sometimes personal. A big reason for the large divide between the two groups is the failure of both to acknowledge the significant uncertainty in their estimates. Although some authors attempt to quantify uncertainty, most use deterministic methods and present single values, with no ranges. This research proposes that those that do attempt to quantify uncertainty underestimate it significantly. The objective of this thesis is to rigorously quantify the uncertainty in estimates of ultimate world conventional oil production and time to peak rate. Two different methodologies are used. The first is a regression technique based on historical production data using Hubbert's model and the other methodology uses mathematical models. However, I conduct the analysis probabilistically, considering errors in both the data and the model, which results in likelihood probability distributions for world conventional oil production and time to peak rate. In the second method, I use a multiple-experts analysis to combine estimates from the multitude of papers presented in the literature, yielding an overall distribution of estimated world conventional oil production. Giving due consideration to uncertainty, Hubbert-type mathematical modeling results in large uncertainty ranges that encompass both groups of forecasts (imminent peak and no imminent peak). These ranges are consistent with those from the multiple-experts analysis. In short, the industry does not have enough information at this time to say with any reliability what the ultimate world conventional oil production will be. It could peak soon, somewhere in the distant future, or somewhere in between. It would be wise to consider all of these possible outcomes in planning and making decisions regarding capital investment and formulation of energy policy.
Tien, Chih-Ming
Conceptual and computational basis for the quantification of margins and uncertainty.
In 2001, the National Nuclear Security Administration of the U.S. Department of Energy in conjunction with the national security laboratories (i.e, Los Alamos National Laboratory, Lawrence Livermore National Laboratory and Sandia National Laboratories) initiated development of a process designated Quantification of Margins and Uncertainty (QMU) for the use of risk assessment methodologies in the certification of the reliability and safety of the nation's nuclear weapons stockpile. This presentation discusses and illustrates the conceptual and computational basis of QMU in analyses that use computational models to predict the behavior of complex systems. Topics considered include (1) the role of aleatory and epistemic uncertainty in QMU, (2) the representation of uncertainty with probability, (3) the probabilistic representation of uncertainty in QMU analyses involving only epistemic uncertainty, (4) the probabilistic representation of uncertainty in QMU analyses involving aleatory and epistemic uncertainty, (5) procedures for sampling-based uncertainty and sensitivity analysis, (6) the representation of uncertainty with alternatives to probability such as interval analysis, possibility theory and evidence theory, (7) the representation of uncertainty with alternatives to probability in QMU analyses involving only epistemic uncertainty, and (8) the representation of uncertainty with alternatives to probability in QMU analyses involving aleatory and epistemic uncertainty. Concepts and computational procedures are illustrated with both notional examples and examples from reactor safety and radioactive waste disposal.
Helton, Jon Craig (Arizona State University, Tempe, AZ)
The minimum-uncertainty coherent states for Landau levels
The Glauber minimum-uncertainty coherent states with two variables for Landau levels, based on the representation of Weyl-Heisenberg algebra by two different modes, have been studied about four decades ago. Here, we introduce new two-variable coherent states with minimum uncertainty relationship for Landau levels in three different methods: the infinite unitary representation of su(1, 1) is realized in two different methods, first, by consecutive levels with the same energy gaps and also with the same value for z-angular momentum quantum number, then, by shifting z-angular momentum mode number by two units while the energy level remaining the same. Besides, for su(2), whether by lowest Landau levels or Landau levels with lowest z-angular momentum, just one finite unitary representation is introduced. Having constructed the generalized Klauder-Perelomov coherent states, for any of the three representations, we obtain their Glauber coherency by displacement operator of Weyl-Heisenberg algebra.
Dehghani, A. [Physics Department, Payame Noor University, P. O. Box 19395-4697 Tehran (Iran, Islamic Republic of); Fakhri, H. [Department of Theoretical Physics and Astrophysics, Faculty of Physics, University of Tabriz, P. O. Box 51666-16471 Tabriz (Iran, Islamic Republic of); Mojaveri, B. [Department of Physics, Azarbaijan Shahid Madani University, P. O. Box 51745-406 Tabriz (Iran, Islamic Republic of)
Reduction in maximum time uncertainty of paired time signals
DOE Patents (OSTI)
Reduction in the maximum time uncertainty (t/sub max/ - t/sub min/) of a series of paired time signals t/sub 1/ and t/sub 2/ varying between two input terminals and representative of a series of single events where t/sub 1/ less than or equal to t/sub 2/ and t/sub 1/ + t/sub 2/ equals a constant, is carried out with a circuit utilizing a combination of OR and AND gates as signal selecting means and one or more time delays to increase the minimum value (t/sub min/) of the first signal t/sub 1/ closer to t/sub max/ and thereby reduce the difference. The circuit may utilize a plurality of stages to reduce the uncertainty by factors of 20 to 800.
Theodosiou, G.E.; Dawson, J.W.
The Generalized Uncertainty Principle and Black Hole Remnants
In the current standard viewpoint small black holes are believed to emit black body radiation at the Hawking temperature, at least until they approach Planck size, after which their fate is open to conjecture. A cogent argument against the existence of remnants is that, since no evident quantum number prevents it, black holes should radiate completely away to photons and other ordinary stable particles and vacuum, like any unstable quantum system. Here we argue the contrary, that the generalized uncertainty principle may prevent their total evaporation in exactly the same way that the uncertainty principle prevents the hydrogen atom from total collapse: the collapse is prevented, not by symmetry, but by dynamics, as a minimum size and mass are approached.
Chen, Pisin
Distributed Generation Investment by a Microgrid Under Uncertainty Distributed Generation Investment by a Microgrid Under Uncertainty Speaker(s): Afzal Siddiqui Date: July 24, 2006 - 12:00pm Location: 90-3122 This study examines a California-based microgrid's decision to invest in a distributed generation (DG) unit that operates on natural gas. While the long-term natural gas generation cost is stochastc, we initially assume that the microgrid may purchase electricity at a fixed retail rate from its utility. Using the real options approach, we find natural gas generation cost thresholds that trigger DG investment. Furthermore, the consideration of operational flexibility by the microgrid accelerates DG investment, while the option to disconnect entirely from the utility is not attractive. By allowing the electricity price to be stochastic, we next determine an
Argonne CNM Highlight: Deciphering Uncertainties in the Cost of Solar
Deciphering Uncertainties in the Cost of Solar Energy Deciphering Uncertainties in the Cost of Solar Energy Photovoltaic electricity is a rapidly growing renewable energy source and will ultimately assume a major role in global energy production. The cost of solar-generated electricity is typically compared with electricity produced by traditional sources with a levelized cost of energy (LCOE) calculation. Generally, LCOE is treated as a definite number, and the assumptions lying beneath that result are rarely reported or even understood. We shed light on some of the key assumptions and offer a new approach to calculating LCOE for photovoltaics based on input parameter distributions feeding a Monte Carlo simulation. In this framework, the influence of assumptions and confidence intervals becomes clear.
Estimated Uncertainties in the Idaho National Laboratory Matched-Index-of-Refraction Lower Plenum Experiment
The purpose of the fluid dynamics experiments in the MIR (Matched-Index-of-Refraction) flow system at Idaho National Laboratory (INL) is to develop benchmark databases for the assessment of Computational Fluid Dynamics (CFD) solutions of the momentum equations, scalar mixing, and turbulence models for typical Very High Temperature Reactor (VHTR) plenum geometries in the limiting case of negligible buoyancy and constant fluid properties. The experiments use optical techniques, primarily particle image velocimetry (PIV) in the INL MIR flow system. The benefit of the MIR technique is that it permits optical measurements to determine flow characteristics in passages and around objects to be obtained without locating a disturbing transducer in the flow field and without distortion of the optical paths. The objective of the present report is to develop understanding of the magnitudes of experimental uncertainties in the results to be obtained in such experiments. Unheated MIR experiments are first steps when the geometry is complicated. One does not want to use a computational technique, which will not even handle constant properties properly. This report addresses the general background, requirements for benchmark databases, estimation of experimental uncertainties in mean velocities and turbulence quantities, the MIR experiment, PIV uncertainties, positioning uncertainties, and other contributing measurement uncertainties.
Donald M. McEligot; Hugh M. McIlroy, Jr.; Ryan C. Johnson
Parametric Uncertainty Impacts on Option 2 Safety Significance Categorization
This report describes an assessment to determine the impact of parametric uncertainty on the safety significance categorization of structures, systems, and components (SSCs) using risk-importance measures. The study supports Risk-Informed Option 2, which allows elimination of special treatment requirements for low-risk-significant SSCs. The U.S. Nuclear Regulatory Commission (NRC) has proposed an Option 2 Rulemaking, 10 CFR 50.69. Industry, through the Nuclear Energy Institute (NEI) with EPRI technical s...
Quantification Of Margins And Uncertainties: A Bayesian Approach (full Paper)
Quantification of Margins and Uncertainties (QMU) is 'a formalism for dealing with the reliability of complex technical systems, and the confidence which can be placed in estimates of that reliability.' (Eardleyet al, 2005). In this paper, we show how QMU may be interpreted in the framework of Bayesian statistical inference, using a probabilistic network. The Bayesian approach clarifies the probabilistic underpinnings of the formalism, and shows how the formalism can be used for deciSion-making.
Wallstrom, Timothy C [Los Alamos National Laboratory
Born-Jordan Quantization and the Uncertainty Principle
The Weyl correspondence and the related Wigner formalism lie at the core of traditional quantum mechanics. We discuss here an alternative quantization scheme, whose idea goes back to Born and Jordan, and which has recently been revived in another context, namely time-frequency analysis. We show that in particular the uncertainty principle does not enjoy full symplectic covariance properties in the Born and Jordan scheme, as opposed to what happens in the Weyl quantization.
Maurice A. de Gosson
Theory Uncertainty in Extracting the Proton's Weak Charge: White Paper
We review the state-of-the-art and address open questions relative to the theory uncertainty of the $\\gamma-Z$ box contribution to the $A_{PV}$ measurement within the QWEAK experiment at Jefferson Lab. This white paper summarizes the contributions by participants and discussion sessions on this topic within the MITP Workshop on Precision Electroweak Physics held in Mainz, Germany, September 23 - October 11, 2013 \\url{http://indico.cern.ch/conferenceDisplay.py?confId=248072
Gorchtein, Mikhail; Hurth, Tobias; Spiesberger, Hubert; Kumar, Krishna; Ramsey-Musolf, Michael J; Meyer, Harvey B
Mikhail Gorchtein; Jens Erler; Tobias Hurth; Hubert Spiesberger; Krishna Kumar; Michael J. Ramsey-Musolf; Harvey B. Meyer
Comment on ''Improved bounds on entropic uncertainty relations''
We provide an analytical proof of the entropic uncertainty relations presented by J. I. de Vicente and J. Sanchez-Ruiz [Phys. Rev. A 77, 042110 (2008)] and also show that the replacement of Eq. (27) by Eq. (29) in that reference introduces solutions that do not take fully into account the constraints of the problem, which in turn lead to some mistakes in their treatment.
Bosyk, G. M.; Portesi, M.; Plastino, A.; Zozor, S. [Instituto de Fisica La Plata (IFLP, CONICET), and Departamento de Fisica, Facultad de Ciencias Exactas, Universidad Nacional de La Plata, C.C. 67, 1900 La Plata (Argentina); Laboratoire Grenoblois d'Image, Parole, Signal et Automatique (GIPSA-Lab, CNRS), 961 rue de la Houille Blanche, F-38402 Saint Martin d'Heres (France)
Minimum Uncertainty, Coherence and Squeezing in Diffusion Processes, and Stochastic Quantization
We show that uncertainty relations, as well as minimum uncertainty coherent and squeezed states, are structural properties for diffusion processes. Through Nelson stochastic quantization we derive the stochastic image of the quantum mechanical coherent and squeezed states.
Salvatore De Martino; Silvio De Siena; Fabrizio Illuminati; Giuseppe Vitiello
We show that uncertainty relations, as well as minimum uncertainty coherent and squeezed states, are structural properties for diffusion processes. Through Nelson stochastic quantization we derive the stochastic image of the quantum mechanical coherent and squeezed states. 1
S. De Martino; S. De Siena; F. Illuminati; G. Vitiello
An Efficient Stochastic Bayesian Approach to Optimal Parameter and Uncertainty Estimation for Climate Model Predictions
One source of uncertainty for climate model predictions arises from the fact that climate models have been optimized to reproduce observational means. To quantify the uncertainty resulting from a realistic range of model configurations, it is ...
Charles Jackson; Mrinal K. Sen; Paul L. Stoffa
Potential Benefits of Seasonal Inflow Prediction Uncertainty for Reservoir Release Decisions
This paper examines the conditions for which beneficial use of forecast uncertainty may be made for improved reservoir release decisions. It highlights the parametric dependencies of the effects of uncertainty in seasonal inflow volumes on the ...
Konstantine P. Georgakakos; Nicholas E. Graham
and Demand Response under Uncertainty • F P t : wholesale natural gasdemand response and DER under uncertain electricity and natural gasand Demand Response under Uncertainty Energy Price Models We assume that the logarithms of the deseasonalized electricity and natural gas
Communicating Uncertainty in Weather Forecasts: A Survey of the U.S. Public
Weather forecasts are inherently uncertain, and meteorologists have information about weather forecast uncertainty that is not readily available to most forecast users. Yet effectively communicating forecast uncertainty to nonmeteorologists ...
Rebecca E. Morss; Julie L. Demuth; Jeffrey K. Lazo
Adaptive Vision and Force Tracking Control for Robots With Constraint Uncertainty
In force control applications of robots, it is difficult to obtain an exact model of a constraint surface. In presence of the constraint uncertainty, the robot needs to adapt to the uncertainty in external parameters due ...
Cheah, Chien Chern
Probabilistic Estimates of Transient Climate Sensitivity Subject to Uncertainty in Forcing and Natural Variability
In this paper, the authors address the impact of uncertainty on estimates of transient climate sensitivity (TCS) of the globally averaged surface temperature, including both uncertainty in past forcing and internal variability in the climate ...
Lauren E. Padilla; Geoffrey K. Vallis; Clarence W. Rowley
Errors and Uncertainties in Dose Reconstruction for Radiation Effects Research
Dose reconstruction for studies of the health effects of ionizing radiation have been carried out for many decades. Major studies have included Japanese bomb survivors, atomic veterans, downwinders of the Nevada Test Site and Hanford, underground uranium miners, and populations of nuclear workers. For such studies to be credible, significant effort must be put into applying the best science to reconstructing unbiased absorbed doses to tissues and organs as a function of time. In many cases, more and more sophisticated dose reconstruction methods have been developed as studies progressed. For the example of the Japanese bomb survivors, the dose surrogate "distance from the hypocenter" was replaced by slant range, and then by TD65 doses, DS86 doses, and more recently DS02 doses. Over the years, it has become increasingly clear that an equal level of effort must be expended on the quantitative assessment of uncertainty in such doses, and to reducing and managing uncertainty. In this context, this paper reviews difficulties in terminology, explores the nature of Berkson and classical uncertainties in dose reconstruction through examples, and proposes a path forward for Joint Coordinating Committee for Radiation Effects Research (JCCRER) Project 2.4 that requires a reasonably small level of effort for DOSES-2008.
Strom, Daniel J.
Monte Carlo parameter studies and uncertainty analyses with MCNP5
A software tool called mcnp-pstudy has been developed to automate the setup, execution, and collection of results from a series of MCNPS Monte Carlo calculations. This tool provides a convenient means of performing parameter studies, total uncertainty analyses, parallel job execution on clusters, stochastic geometry modeling, and other types of calculations where a series of MCNPS jobs must be performed with varying problem input specifications. Monte Carlo codes are being used for a wide variety of applications today due to their accurate physical modeling and the speed of today's computers. In most applications for design work, experiment analysis, and benchmark calculations, it is common to run many calculations, not just one, to examine the effects of design tolerances, experimental uncertainties, or variations in modeling features. We have developed a software tool for use with MCNP5 to automate this process. The tool, mcnp-pstudy, is used to automate the operations of preparing a series of MCNP5 input files, running the calculations, and collecting the results. Using this tool, parameter studies, total uncertainty analyses, or repeated (possibly parallel) calculations with MCNP5 can be performed easily. Essentially no extra user setup time is required beyond that of preparing a single MCNP5 input file.
Brown, F. B. (Forrest B.); Sweezy, J. E. (Jeremy E.); Hayes, R. B. (Robert B.)
An EPGPT-based approach for uncertainty quantification
Generalized Perturbation Theory (GPT) has been widely used by many scientific disciplines to perform sensitivity analysis and uncertainty quantification. This manuscript employs recent developments in GPT theory, collectively referred to as Exact-to-Precision Generalized Perturbation Theory (EPGPT), to enable uncertainty quantification for computationally challenging models, e.g. nonlinear models associated with many input parameters and many output responses and with general non-Gaussian parameters distributions. The core difference between EPGPT and existing GPT is in the way the problem is formulated. GPT formulates an adjoint problem that is dependent on the response of interest. It tries to capture via the adjoint solution the relationship between the response of interest and the constraints on the state variations. EPGPT recasts the problem in terms of a smaller set of what is referred to as the 'active' responses which are solely dependent on the physics model and the boundary and initial conditions rather than on the responses of interest. The objective of this work is to apply an EPGPT methodology to propagate cross-sections variations in typical reactor design calculations. The goal is to illustrate its use and the associated impact for situations where the typical Gaussian assumption for parameters uncertainties is not valid and when nonlinear behavior must be considered. To allow this demonstration, exaggerated variations will be employed to stimulate nonlinear behavior in simple prototypical neutronics models. (authors)
Wang, C.; Abdel-Khalik, H. S. [Dept. of Nuclear Engineering, North Caroline State Univ., Raleigh, NC 27695 (United States)
Uncertainties in (E)UV model atmosphere fluxes (Research Note)
Context. During the comparison of synthetic spectra calculated with two NLTE model atmosphere codes, namely TMAP and TLUSTY, we encounter systematic differences in the EUV fluxes due to the treatment of level dissolution by pressure ionization. Aims. In the case of Sirius B, we demonstrate an uncertainty in modeling the EUV flux reliably in order to challenge theoreticians to improve the theory of level dissolution. Methods. We calculated synthetic spectra for hot, compact stars using state-of-the-art NLTE model-atmosphere techniques. Results. Systematic differences may occur due to a code-specific cutoff frequency of the H I Lyman bound-free opacity. This is the case for TMAP and TLUSTY. Both codes predict the same flux level at wavelengths lower than about 1500 Ĺ for stars with effective temperatures (Teff) below about 30 000 K only, if the same cutoff frequency is chosen. Conclusions. The theory of level dissolution in high-density plasmas, which is available for hydrogen only should be generalized to all species. Especially, the cutoff frequencies for the bound-free opacities should be defined in order to make predictions of UV fluxes more reliable.
T. Rauch
2012 CERTS R&M Peer Review - Transmission Investment Assessment Under Uncertainty - Ben Hobbs
Transmission Transmission Investments Under Uncertainty & Transmission Investments Under Uncertainty & High Renewable Penetration: Representing Market Response using a Multi-stage Stochastic Model Approach with Recourse Model Approach with Recourse Benjamin F. Hobbs & Francisco Munoz Geography & Environmental Engineering Applied Math & Stat Geography & Environmental Engineering, Applied Math & Stat. Environment, Energy, Sustainability & Health Institute The Johns Hopkins University Richard E Schuler Richard E. Schuler Civil & Environmental Engineering, and Economics Cornell University August 2, 2011 Thanks to Saamrat Kasina and Harry van der Weijde for their assistance, and DOE CERTS for funding and DOE CERTS for funding O i Overview 1. The problem 1. The problem
Uncertainty quantification using polynomial chaos expansion with points of monomial cubature rules
This paper proposes an efficient method for estimating uncertainty propagation and identifying influence factors contributing to uncertainty. In general, the system is dominated by some of the main effects and lower-order interactions due to the sparsity-of-effect ... Keywords: Monomial cubature rules, Polynomial chaos expansion, Sampling points, Uncertainty quantification
D. L. Wei; Z. S. Cui; J. Chen
Uncertainty propagation in puff-based dispersion models using polynomial chaos
Atmospheric dispersion is a complex nonlinear physical process with numerous uncertainties in model parameters, inputs, source parameters, initial and boundary conditions. Accurate propagation of these uncertainties through the dispersion models is crucial ... Keywords: Atmospheric dispersion, Non-Gaussian distributions, Polynomial chaos, Sensitivity analysis, Uncertainty propagation
Umamaheswara Konda; Tarunraj Singh; Puneet Singla; Peter Scott
Quantification of Variability and Uncertainty in Hourly NOx Emissions from Coal-Fired Power Plants
1 Quantification of Variability and Uncertainty in Hourly NOx Emissions from Coal-Fired Power to quantify variability and uncertainty for NOx emissions from coal-fired power plants. Data for hourly NOx Uncertainty, Variability, Emission Factors, Coal-Fired Power Plants, NOx emissions, Regression Models
Frey, H. Christopher
Radiochemical analyses of surface water samples, in the framework of Environmental Monitoring, have associated uncertainties for the radioisotopic results reported. These uncertainty analyses pertain to the tritium results from surface water samples collected at five locations on the Savannah River near the U.S. Department of Energy's Savannah River Site (SRS). Uncertainties can result from the field-sampling routine, can be incurred during transport due to the physical properties of the sample, from equipment limitations, and from the measurement instrumentation used. The uncertainty reported by the SRS in their Annual Site Environmental Report currently considers only the counting uncertainty in the measurements, which is the standard reporting protocol for radioanalytical chemistry results. The focus of this work is to provide an overview of all uncertainty components associated with SRS tritium measurements, estimate the total uncertainty according to ISO 17025, and to propose additional experiments to verify some of the estimated uncertainties. The main uncertainty components discovered and investigated in this paper are tritium absorption or desorption in the sample container, HTO/H{sub 2}O isotopic effect during distillation, pipette volume, and tritium standard uncertainty. The goal is to quantify these uncertainties and to establish a combined uncertainty in order to increase the scientific depth of the SRS Annual Site Environmental Report.
Atkinson, R.
A stochastic approach to estimate the uncertainty of dose mapping caused by uncertainties in b-spline registration
Purpose: In fractionated radiation therapy, image guidance with daily tomographic imaging becomes more and more clinical routine. In principle, this allows for daily computation of the delivered dose and for accumulation of these daily dose distributions to determine the actually delivered total dose to the patient. However, uncertainties in the mapping of the images can translate into errors of the accumulated total dose, depending on the dose gradient. In this work, an approach to estimate the uncertainty of mapping between medical images is proposed that identifies areas bearing a significant risk of inaccurate dose accumulation. Methods: This method accounts for the geometric uncertainty of image registration and the heterogeneity of the dose distribution, which is to be mapped. Its performance is demonstrated in context of dose mapping based on b-spline registration. It is based on evaluation of the sensitivity of dose mapping to variations of the b-spline coefficients combined with evaluation of the sensitivity of the registration metric with respect to the variations of the coefficients. It was evaluated based on patient data that was deformed based on a breathing model, where the ground truth of the deformation, and hence the actual true dose mapping error, is known. Results: The proposed approach has the potential to distinguish areas of the image where dose mapping is likely to be accurate from other areas of the same image, where a larger uncertainty must be expected. Conclusions: An approach to identify areas where dose mapping is likely to be inaccurate was developed and implemented. This method was tested for dose mapping, but it may be applied in context of other mapping tasks as well.
Hub, Martina; Thieke, Christian; Kessler, Marc L.; Karger, Christian P. [Department of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), 69120 Heidelberg (Germany); Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany and Department of Radiation Oncology, University Clinic Heidelberg, 69120 Heidelberg (Germany); Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan 48109 (United States); Department of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), 69120 Heidelberg (Germany)
Probabilistic accident consequence uncertainty analysis: Dispersion and deposition uncertainty assessment. Volume 3, Appendices C, D, E, F, and G
The development of two new probabilistic accident consequence codes, MACCS and COSYMA, completed in 1990, estimate the risks presented by nuclear installations based on postulated frequencies and magnitudes of potential accidents. In 1991, the US Nuclear Regulatory Commission (NRC) and the Commission of the European Communities (CEC) began a joint uncertainty analysis of the two codes. The objective was to develop credible and traceable uncertainty distributions for the input variables of the codes. Expert elicitation, developed independently, was identified as the best technology available for developing a library of uncertainty distributions for the selected consequence parameters. The study was formulated jointly and was limited to the current code models and to physical quantities that could be measured in experiments. To validate the distributions generated for the wet deposition input variables, samples were taken from these distributions and propagated through the wet deposition code model along with the Gaussian plume model (GPM) implemented in the MACCS and COSYMA codes. Resulting distributions closely replicated the aggregated elicited wet deposition distributions. Project teams from the NRC and CEC cooperated successfully to develop and implement a unified process for the elaboration of uncertainty distributions on consequence code input parameters. Formal expert judgment elicitation proved valuable for synthesizing the best available information. Distributions on measurable atmospheric dispersion and deposition parameters were successfully elicited from experts involved in the many phenomenological areas of consequence analysis. This volume is the third of a three-volume document describing the project and contains descriptions of the probability assessment principles; the expert identification and selection process; the weighting methods used; the inverse modeling methods; case structures; and summaries of the consequence codes.
Harper, F.T.; Young, M.L.; Miller, L.A. [Sandia National Labs., Albuquerque, NM (United States)] [and others
Bayesian Uncertainty Quantification for Large Scale Spatial Inverse Problems
We considered a Bayesian approach to nonlinear inverse problems in which the unknown quantity is a high dimension spatial field. The Bayesian approach contains a natural mechanism for regularization in the form of prior information, can incorporate information from heterogeneous sources and provides a quantitative assessment of uncertainty in the inverse solution. The Bayesian setting casts the inverse solution as a posterior probability distribution over the model parameters. Karhunen-Lo'eve expansion and Discrete Cosine transform were used for dimension reduction of the random spatial field. Furthermore, we used a hierarchical Bayes model to inject multiscale data in the modeling framework. In this Bayesian framework, we have shown that this inverse problem is well-posed by proving that the posterior measure is Lipschitz continuous with respect to the data in total variation norm. The need for multiple evaluations of the forward model on a high dimension spatial field (e.g. in the context of MCMC) together with the high dimensionality of the posterior, results in many computation challenges. We developed two-stage reversible jump MCMC method which has the ability to screen the bad proposals in the first inexpensive stage. Channelized spatial fields were represented by facies boundaries and variogram-based spatial fields within each facies. Using level-set based approach, the shape of the channel boundaries was updated with dynamic data using a Bayesian hierarchical model where the number of points representing the channel boundaries is assumed to be unknown. Statistical emulators on a large scale spatial field were introduced to avoid the expensive likelihood calculation, which contains the forward simulator, at each iteration of the MCMC step. To build the emulator, the original spatial field was represented by a low dimensional parameterization using Discrete Cosine Transform (DCT), then the Bayesian approach to multivariate adaptive regression spline (BMARS) was used to emulate the simulator. Various numerical results were presented by analyzing simulated as well as real data.
Mondal, Anirban
Minimal multi-element stochastic collocation for uncertainty quantification of discontinuous functions
We propose a multi-element stochastic collocation method that can be applied in high-dimensional parameter space for functions with discontinuities lying along manifolds of general geometries. The key feature of the method is that the parameter space ... Keywords: Discontinuous functions, Generalized polynomial chaos, Multi-element, Stochastic collocation, Uncertainty quantification
John D. Jakeman, Akil Narayan, Dongbin Xiu
Fault diagnosis for smart grid with uncertainty information based on data
The concept of Smart Grid has gained significant acceptance during the last several years due to the high cost of energy, environment concerns, and major advances in distributed generation (DG) technologies. Distribution systems have traditionally been ... Keywords: fault diagnosis, intuitionistic uncertainty sets, rough sets, smart grid
Qiuye Sun; Zhongxu Li; Jianguo Zhou; Xue Liang
Modelling uncertainty in the sustainability of Intelligent Transport Systems for highways using probabilistic data fusion
The implementation of ITS to increase the efficiency of saturated highways has become increasingly prevalent. It is a high level objective for many international governments and operators that highways should be managed in a way that is both sustainable ... Keywords: Intelligent Transport Systems, Low carbon-energy policy, Uncertainty modelling
Ben Kolosz, Susan Grant-Muller, Karim Djemame
Design Features and Technology Uncertainties for the Next Generation Nuclear Plant
This report presents the conclusions, observations, and recommendations of the Independent Technology Review Group (ITRG) regarding design features and important technology uncertainties associated with very-high-temperature nuclear system concepts for the Next Generation Nuclear Plant (NGNP). The ITRG performed its reviews during the period November 2003 through April 2004.
John M. Ryskamp; Phil Hildebrandt; Osamu Baba; Ron Ballinger; Robert Brodsky; Hans-Wolfgang Chi; Dennis Crutchfield; Herb Estrada; Jeane-Claude Garnier; Gerald Gordon; Richard Hobbins; Dan Keuter; Marilyn Kray; Philippe Martin; Steve Melancon; Christian Simon; Henry Stone; Robert Varrin; Werner von Lensa
Uncertainty quantification given discontinuous climate model response and a limited number of model runs.
Uncertainty quantification in complex climate models is challenged by the sparsity of available climate model predictions due to the high computational cost of model runs. Another feature that prevents classical uncertainty analysis from being readily applicable is bifurcative behavior in climate model response with respect to certain input parameters. A typical example is the Atlantic Meridional Overturning Circulation. The predicted maximum overturning stream function exhibits discontinuity across a curve in the space of two uncertain parameters, namely climate sensitivity and CO2 forcing. We outline a methodology for uncertainty quantification given discontinuous model response and a limited number of model runs. Our approach is two-fold. First we detect the discontinuity with Bayesian inference, thus obtaining a probabilistic representation of the discontinuity curve shape and location for arbitrarily distributed input parameter values. Then, we construct spectral representations of uncertainty, using Polynomial Chaos (PC) expansions on either side of the discontinuity curve, leading to an averaged-PC representation of the forward model that allows efficient uncertainty quantification. The approach is enabled by a Rosenblatt transformation that maps each side of the discontinuity to regular domains where desirable orthogonality properties for the spectral bases hold. We obtain PC modes by either orthogonal projection or Bayesian inference, and argue for a hybrid approach that targets a balance between the accuracy provided by the orthogonal projection and the flexibility provided by the Bayesian inference - where the latter allows obtaining reasonable expansions without extra forward model runs. The model output, and its associated uncertainty at specific design points, are then computed by taking an ensemble average over PC expansions corresponding to possible realizations of the discontinuity curve. The methodology is tested on synthetic examples of discontinuous model data with adjustable sharpness and structure.
CASMO5/TSUNAMI-3D spent nuclear fuel reactivity uncertainty analysis
The CASMO5 lattice physics code is used in conjunction with the TSUNAMI-3D sequence in ORNL's SCALE 6 code system to estimate the uncertainties in hot-to-cold reactivity changes due to cross-section uncertainty for PWR assemblies at various burnup points. The goal of the analysis is to establish the multiplication factor uncertainty similarity between various fuel assemblies at different conditions in a quantifiable manner and to obtain a bound on the hot-to-cold reactivity uncertainty over the various assembly types and burnup attributed to fundamental cross-section data uncertainty. (authors)
Ferrer, R.; Rhodes, J. [Studsvik Scandpower, Inc., 504 Shoup Ave., Idaho Falls, ID 83402 (United States); Smith, K. [Dept. of Nuclear Science and Engineering, Massachusetts Inst. of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139 (United States)
The Savannah River Remediation (SRR) Defense Waste Processing Facility (DWPF) has identified a technical issue related to the amount of antifoam added to the Chemical Process Cell (CPC). Specifically, due to the long duration of the concentration and reflux cycles for the Sludge Receipt and Adjustment Tank (SRAT), additional antifoam has been required. The additional antifoam has been found to impact the melter flammability analysis as an additional source of carbon and hydrogen. To better understand and control the carbon and hydrogen contributors to the melter flammability analysis, SRR's Waste Solidification Engineering (WSE) has requested, via a Technical Task Request (TTR), that the Savannah River National Laboratory (SRNL) conduct an error evaluation of the measurements of key Slurry Mix Evaporator (SME) anions. SRNL issued a Task Technical and Quality Assurance Plan (TTQAP) [2] in response to that request, and the work reported here was conducted under the auspices of that TTQAP. The TTR instructs SRNL to conduct an error evaluation of anion measurements generated by the DWPF Laboratory using Ion Chromatography (IC) performed on SME samples. The anions of interest include nitrate, oxalate, and formate. Recent measurements of SME samples for these anions as well as measurements of total organic carbon (TOC) were provided to SRNL by DWPF Laboratory Operations (Lab OPS) personnel for this evaluation. This work was closely coordinated with the efforts of others within SRNL that are investigating the Chemical Process Cell (CPC) contributions to the melter flammability. The objective of that investigation was to develop a more comprehensive melter flammability control strategy that when implemented in DWPF will rely on process measurements. Accounting for the uncertainty of the measurements is necessary for successful implementation. The error evaluations conducted as part of this task will facilitate the integration of appropriate uncertainties for the measurements utilized in that control strategy. The flammability control strategy presented in relies on SME measurements of TOC and nitrate while one of the uses by WSE of the oxalate and formate measurement data will be the estimation of the amount of carbon coming from antifoam additions. The estimation is to be conducted by backing out contributions to the measured TOC concentration in the SME from the oxalate and the formate concentrations that are measured in the SME. The resulting adjusted TOC value will provide a basis for WSE to estimate the amount of antifoam that was added for that SME batch. The uncertainties of the oxalate, formate, and TOC measurements provided by the evaluations conducted as part of this task will allow for the propagation of their uncertainties into the estimated quantity of carbon coming from the added antifoam. The purpose of this technical report is to present the measurements generated by the DWPF Laboratory for recent SME batches, to conduct an evaluation of their uncertainties, and to provide the approach for propagating the uncertainties associated with these measurements into DWPF's strategies for controlling melter flammability and for monitoring antifoam additions. JMP Version 7.0.2 was used to support the analyses presented in this report.
Edwards, T.
Uncertainty Analysis for Broadband Solar Radiometric Instrumentation Calibrations and Measurements: An Update; Preprint
The measurement of broadband solar radiation has grown in importance since the advent of solar renewable energy technologies in the 1970's, and the concern about the Earth's radiation balance related to climate change in the 1990's. In parallel, standardized methods of uncertainty analysis and reporting have been developed. Historical and updated uncertainties are based on the current international standardized uncertainty analysis method. Despite the fact that new and sometimes overlooked sources of uncertainty have been identified over the period 1988 to 2004, uncertainty in broadband solar radiometric instrumentation remains at 3% to 5% for pyranometers, and 2% to 3% for pyrheliometers. Improvements in characterizing correction functions for radiometer data may reduce total uncertainty. We analyze the theoretical standardized uncertainty sensitivity coefficients for the instrumentation calibration measurement equation and highlight the single parameter (thermal offset voltages), which contributes the most to the observed calibration responsivities.
Myers, D. R.; Reda, I. M.; Wilcox, S. M.; Stoffel, T. L.
The Perihelion Precession of Mercury and the Generalized Uncertainty Principle
Very recently authors in [1] proposed a new Generalized Uncertainty Principle (or GUP) with a linear term in Plank length. In this Letter the effect of this linear term is studied perturbatively in the context of Keplerian orbits. The angle by which the perihelion of the orbit revolves over a complete orbital cycle is computed. The result is applied in the context of the precession of the perihelion of Mercury. As a consequence we get a lower bound of the new intermediate length scale offered by the GUP which is approximately 40 orders of magnitude below Plank length.
Barun Majumder
On uncertainties associated with expected backgrounds in planned experiments
The expected numbers of events of several backgrounds in experiment are estimated from Monte Carlo experiments. In the analysis we take into account an integrated luminosity of Monte Carlo experiments. The expected number of events allows to construct the distribution of probabilities of number of events which in real experiment may be observed (in accordance with formulae in [1]). The formulae allow to take into account statistical uncertainty of corresponding Monte Carlo experiment. The influence of systematics is determined by additional Monte Carlo experiments with expected number of events.
Bityukov, Sergey; Smirnova, Vera [Institute for high energy physics, 142281 Protvino (Russian Federation); Krasnikov, Nikolai [Institute for nuclear research RAS, Prospect 60-letiya Octyabrya, 7a, 117312 Moscow (Russian Federation)
Savannah River Site ECS-2 tests uncertainty report
This document presents a measurement uncertainty analysis for the instruments used in the ECS-2 test series conducted for the Savannah River Site at the Idaho National Engineering Laboratory. The tests are a series of downflow dryout heat transfer experiments designed to support computer code development and verification in setting limits for the Savannah River Production reactors. The measurements include input current, voltage, and power; air and water flows, fluid and metal temperatures, and absolute and differential pressures. An analysis of the data acquisition system as it relates to these measurements is also included. 18 refs., 6 figs., 12 tabs.
Wilkins, S.C.; Larson, R.A.
Sensitivities of nuclear reaction rates to a variation of nuclear properties are studied. Target nuclei range from proton- to neutron dripline for 10 {<=} Z {<=} 83. Reactions considered are nucleon- and {alpha}-induced reactions mediated by strong interaction. The contribution of reactions occurring on the target ground state to the total stellar rate is also given. General dependencies on various input quantities are discussed. Additionally, sensitivities of laboratory cross-sections of nucleon-, {alpha}-, and {gamma}-induced reactions are shown, allowing us to estimate the impact of cross-section measurements. Finally, recommended procedures to explore and improve reaction rate uncertainties using the present sensitivity data are outlined.
Rauscher, T. [Department of Physics, University of Basel, CH-4056 Basel (Switzerland)
Video Scanning Hartmann Optical Tester (VSHOT) Uncertainty Analysis: Preprint
This purely analytical work is based primarily on the geometric optics of the system and shows sensitivities to various design and operational parameters. We discuss sources of error with measuring devices, instrument calibrations, and operator measurements for a parabolic trough test. In this paper, we include both the random (precision) and systematic (bias) errors for VSHOT testing and their contributions to the uncertainty. The contributing factors that we considered in this study are target tilt, target face to laser output distance, instrument vertical offset, scanner tilt, distance between the tool and the test piece, camera calibration, and scanner/calibration.
Lewandowski, A.; Gray, A.
Modelling the evolution of uncertainty levels during design
the term 'uncertainty' to refer to everything that contributes to a lack of definition, lack of knowledge or lack of trust in knowledge. This may differ from colloquial usage of the term but is consistent with much of the literature (e.g., [7,8]). Some... ]. For instance, the fidelity of preliminary aeroengine gas path design can be quantifiably related to the tools used at each step in the design process [30]. Designers can thus assess the accuracy of their performance estimates with respect to the values...
Wynn, David C; Grebici, Khadidja; Clarkson, P John
Stochastic or probabilistic modeling approaches are being applied more frequently in the United States and globally to quantify uncertainty and enhance understanding of model response in performance assessments for disposal of radioactive waste. This increased use has resulted in global interest in sharing results of research and applied studies that have been completed to date. This technical report reflects the results of a workshop that was held to share results of research and applied work related to performance assessments conducted at United States Department of Energy sites. Key findings of this research and applied work are discussed and recommendations for future activities are provided.
Seitz, R
Uncertainty Analysis of RELAP5-3D
As world-wide energy consumption continues to increase, so does the demand for the use of alternative energy sources, such as Nuclear Energy. Nuclear Power Plants currently supply over 370 gigawatts of electricity, and more than 60 new nuclear reactors have been commissioned by 15 different countries. The primary concern for Nuclear Power Plant operation and lisencing has been safety. The safety of the operation of Nuclear Power Plants is no simple matter- it involves the training of operators, design of the reactor, as well as equipment and design upgrades throughout the lifetime of the reactor, etc. To safely design, operate, and understand nuclear power plants, industry and government alike have relied upon the use of best-estimate simulation codes, which allow for an accurate model of any given plant to be created with well-defined margins of safety. The most widely used of these best-estimate simulation codes in the Nuclear Power industry is RELAP5-3D. Our project focused on improving the modeling capabilities of RELAP5-3D by developing uncertainty estimates for its calculations. This work involved analyzing high, medium, and low ranked phenomena from an INL PIRT on a small break Loss-Of-Coolant Accident as wall as an analysis of a large break Loss-Of- Coolant Accident. Statistical analyses were performed using correlation coefficients. To perform the studies, computer programs were written that modify a template RELAP5 input deck to produce one deck for each combination of key input parameters. Python scripting enabled the running of the generated input files with RELAP5-3D on INL's massively parallel cluster system. Data from the studies was collected and analyzed with SAS. A summary of the results of our studies are presented.
Alexandra E Gertman; Dr. George L Mesina
Neutron skin uncertainties of Skyrme energy density functionals
Background: Neutron-skin thickness is an excellent indicator of isovector properties of atomic nuclei. As such, it correlates strongly with observables in finite nuclei that depend on neutron-to-proton imbalance and the nuclear symmetry energy that characterizes the equation of state of neutron-rich matter. A rich worldwide experimental program involving studies with rare isotopes, parity violating electron scattering, and astronomical observations is devoted to pinning down the isovector sector of nuclear models. Purpose: We assess the theoretical systematic and statistical uncertainties of neutron-skin thickness and relate them to the equation of state of nuclear matter, and in particular to nuclear symmetry energy parameters. Methods: We use the nuclear superfluid Density Functional Theory with several Skyrme energy density functionals and density dependent pairing. To evaluate statistical errors and their budget, we employ the statistical covariance technique. Results: We find that the errors on neutron skin increase with neutron excess. Statistical errors due to uncertain coupling constants of the density functional are found to be larger than systematic errors, the latter not exceeding 0.06 fm in most neutron-rich nuclei across the nuclear landscape. The single major source of uncertainty is the poorly determined slope L of the symmetry energy that parametrizes its density dependence. Conclusions: To provide essential constraints on the symmetry energy of the nuclear energy density functional, next-generation measurements of neutron skins are required to deliver precision better than 0.06 fm.
M. Kortelainen; J. Erler; W. Nazarewicz; N. Birge; Y. Gao; E. Olsen
Data Filtering Impact on PV Degradation Rates and Uncertainty (Poster)
To sustain the commercial success of photovoltaics (PV) it becomes vital to know how power output decreases with time. In order to predict power delivery, degradation rates must be determined accurately. Data filtering, any data treatment assessment of long-term field behavior, is discussed as part of a more comprehensive uncertainty analysis and can be one of the greatest sources of uncertainty in long-term performance studies. Several distinct filtering methods such as outlier removal and inclusion of only sunny days on several different metrics such as PVUSA, performance ratio, DC power to plane-of-array irradiance ratio, uncorrected, and temperature-corrected were examined. PVUSA showed the highest sensitivity while temperature-corrected power over irradiance ratio was found to be the least sensitive to data filtering conditions. Using this ratio it is demonstrated that quantification of degradation rates with a statistical accuracy of +/- 0.2%/year within 4 years of field data is possible on two crystalline silicon and two thin-film systems.
Jordan, D. C.; Kurtz, S. R.
The spectre of uncertainty in communicating technological risk
The literature does not clearly describe the potential moral and ethical conflicts that can exist between technology sponsors and the technical communicators whose job it is to present potentially risky technology to the non-technical people most likely to be imperiled by such risk. Equally important, the literature does not address the issue of uncertainty -- not the uncertainty likely to be experienced by the community at risk, but the unreliable processes and methodologies used by technology sponsors to define, quantify, and develop strategies to mitigate technological risks. In this paper, the author goes beyond a description of risk communication, the nature of the generally predictable interaction between technology advocates and non-technically trained individuals, and current trends in the field. Although that kind of information is critical to the success of any risk communication activity, and he has included it when necessary to provide background and perspective, without knowing how and why risk assessment is done, it has limited practical applicability outside the sterile, value-free vacuum in which it is usually framed. Technical communicators, particularly those responsible for communicating potential technological risk, must also understand the social, political, economic, statistical, and ethical issues they will invariably encounter.
Broesius, M.T.
Revised cost savings estimate with uncertainty for enhanced sludge washing of underground storage tank waste
Enhanced Sludge Washing (ESW) has been selected to reduce the amount of sludge-based underground storage tank (UST) high-level waste at the Hanford site. During the past several years, studies have been conducted to determine the cost savings derived from the implementation of ESW. The tank waste inventory and ESW performance continues to be revised as characterization and development efforts advance. This study provides a new cost savings estimate based upon the most recent inventory and ESW performance revisions, and includes an estimate of the associated cost uncertainty. Whereas the author`s previous cost savings estimates for ESW were compared against no sludge washing, this study assumes the baseline to be simple water washing which more accurately reflects the retrieval activity along. The revised ESW cost savings estimate for all UST waste at Hanford is $6.1 B {+-} $1.3 B within 95% confidence. This is based upon capital and operating cost savings, but does not include development costs. The development costs are assumed negligible since they should be at least an order of magnitude less than the savings. The overall cost savings uncertainty was derived from process performance uncertainties and baseline remediation cost uncertainties, as determined by the author`s engineering judgment.
DeMuth, S.
Adjoint-Based Uncertainty Quantification and Sensitivity Analysis for Reactor Depletion Calculations
Depletion calculations for nuclear reactors model the dynamic coupling between the material composition and neutron flux and help predict reactor performance and safety characteristics. In order to be trusted as reliable predictive tools and inputs to licensing and operational decisions, the simulations must include an accurate and holistic quantification of errors and uncertainties in its outputs. Uncertainty quantification is a formidable challenge in large, realistic reactor models because of the large number of unknowns and myriad sources of uncertainty and error. We present a framework for performing efficient uncertainty quantification in depletion problems using an adjoint approach, with emphasis on high-fidelity calculations using advanced massively parallel computing architectures. This approach calls for a solution to two systems of equations: (a) the forward, engineering system that models the reactor, and (b) the adjoint system, which is mathematically related to but different from the forward system. We use the solutions of these systems to produce sensitivity and error estimates at a cost that does not grow rapidly with the number of uncertain inputs. We present the framework in a general fashion and apply it to both the source-driven and k-eigenvalue forms of the depletion equations. We describe the implementation and verification of solvers for the forward and ad- joint equations in the PDT code, and we test the algorithms on realistic reactor analysis problems. We demonstrate a new approach for reducing the memory and I/O demands on the host machine, which can be overwhelming for typical adjoint algorithms. Our conclusion is that adjoint depletion calculations using full transport solutions are not only computationally tractable, they are the most attractive option for performing uncertainty quantification on high-fidelity reactor analysis problems.
Stripling, Hayes Franklin
Method to Calculate Uncertainty Estimate of Measuring Shortwave Solar Irradiance using Thermopile and Semiconductor Solar Radiometers
The uncertainty of measuring solar irradiance is fundamentally important for solar energy and atmospheric science applications. Without an uncertainty statement, the quality of a result, model, or testing method cannot be quantified, the chain of traceability is broken, and confidence cannot be maintained in the measurement. Measurement results are incomplete and meaningless without a statement of the estimated uncertainty with traceability to the International System of Units (SI) or to another internationally recognized standard. This report explains how to use International Guidelines of Uncertainty in Measurement (GUM) to calculate such uncertainty. The report also shows that without appropriate corrections to solar measuring instruments (solar radiometers), the uncertainty of measuring shortwave solar irradiance can exceed 4% using present state-of-the-art pyranometers and 2.7% using present state-of-the-art pyrheliometers. Finally, the report demonstrates that by applying the appropriate corrections, uncertainties may be reduced by at least 50%. The uncertainties, with or without the appropriate corrections might not be compatible with the needs of solar energy and atmospheric science applications; yet, this report may shed some light on the sources of uncertainties and the means to reduce overall uncertainty in measuring solar irradiance.
Reda, I.
Application of price uncertainty quantification models and their impacts on project evaluations
This study presents an analysis of several recently published methods for quantifying the uncertainty in economic evaluations due to uncertainty in future oil prices. Conventional price forecasting methods used in the industry typically underestimate the range of uncertainty in oil and gas price forecasts. These forecasts traditionally consider pessimistic, most-likely, and optimistic cases in an attempt to quantify economic uncertainty. The recently developed alternative methods have their unique strengths as well as weaknesses that may affect their applicability in particular situations. While stochastic methods can improve the assessment of price uncertainty they can also be tedious to implement. The inverted hockey stick method is found to be an easily applied alternative to the stochastic methods. However, the primary basis for validating this method has been found to be unreliable. In this study, a consistent and reliable validation of uncertainty estimates predicted by the inverted hockey stick method is presented. Verifying the reliability of this model will ensure reliable quantification of economic uncertainty. Although we cannot eliminate uncertainty from investment evaluations, we can better quantify the uncertainty by accurately predicting the volatility in future oil and gas prices. Reliably quantifying economic uncertainty will enable operators to make better decisions and allocate their capital with increased efficiency.
Fariyibi, Festus Lekan
Propagation of nuclear data uncertainties for ELECTRA burn-up calculations
The European Lead-Cooled Training Reactor (ELECTRA) has been proposed as a training reactor for fast systems within the Swedish nuclear program. It is a low-power fast reactor cooled by pure liquid lead. In this work, we propagate the uncertainties in Pu-239 transport data to uncertainties in the fuel inventory of ELECTRA during the reactor life using the Total Monte Carlo approach (TMC). Within the TENDL project the nuclear models input parameters were randomized within their uncertainties and 740 Pu-239 nuclear data libraries were generated. These libraries are used as inputs to reactor codes, in our case SERPENT, to perform uncertainty analysis of nuclear reactor inventory during burn-up. The uncertainty in the inventory determines uncertainties in: the long-term radio-toxicity, the decay heat, the evolution of reactivity parameters, gas pressure and volatile fission product content. In this work, a methodology called fast TMC is utilized, which reduces the overall calculation time. The uncertainty in the long-term radiotoxicity, decay heat, gas pressure and volatile fission products were found to be insignificant. However, the uncertainty of some minor actinides were observed to be rather large and therefore their impact on multiple recycling should be investigated further. It was also found that, criticality benchmarks can be used to reduce inventory uncertainties due to nuclear data. Further studies are needed to include fission yield uncertainties, more isotopes, and a larger set of benchmarks.
H. Sjöstrand; E. Alhassan; J. Duan; C. Gustavsson; A. Koning; S. Pomp; D. Rochman; M. Österlund
Data & model conditioning for multivariate systematic uncertainty in model calibration, validation, and extrapolation.
This paper discusses implications and appropriate treatment of systematic uncertainty in experiments and modeling. Systematic uncertainty exists when experimental conditions, and/or measurement bias errors, and/or bias contributed by post-processing the data, are constant over the set of experiments but the particular values of the conditions and/or biases are unknown to within some specified uncertainty. Systematic uncertainties in experiments do not automatically show up in the output data, unlike random uncertainty which is revealed when multiple experiments are performed. Therefore, the output data must be properly 'conditioned' to reflect important sources of systematic uncertainty in the experiments. In industrial scale experiments the systematic uncertainty in experimental conditions (especially boundary conditions) is often large enough that the inference error on how the experimental system maps inputs to outputs is often quite substantial. Any such inference error and uncertainty thereof also has implications in model validation and calibration/conditioning; ignoring systematic uncertainty in experiments can lead to 'Type X' error in these procedures. Apart from any considerations of modeling and simulation, reporting of uncertainty associated with experimental results should include the effects of any significant systematic uncertainties in the experiments. This paper describes and illustrates the treatment of multivariate systematic uncertainties of interval and/or probabilistic natures, and combined cases. The paper also outlines a practical and versatile 'real-space' framework and methodology within which experimental and modeling uncertainties (correlated and uncorrelated, systematic and random, aleatory and epistemic) are treated to mitigate risk in model validation, calibration/conditioning, hierarchical modeling, and extrapolative prediction.
Romero, Vicente Jose
Constraining uncertainties about the sources and magnitude of polycyclic
Constraining uncertainties about the sources and magnitude of polycyclic Constraining uncertainties about the sources and magnitude of polycyclic aromatic hydrocarbon (PAH)levels in ambient air: the State of Minnesota as a case study Title Constraining uncertainties about the sources and magnitude of polycyclic aromatic hydrocarbon (PAH)levels in ambient air: the State of Minnesota as a case study Publication Type Journal Article LBNL Report Number LBNL-54473 Year of Publication 2004 Authors Lobscheid, Agnes B., and Thomas E. McKone Journal Atmospheric Environment Volume 38 Start Page Chapter Pagination 5501-5515 Abstract Emissions data are often lacking or uncertain for many airborne contaminants. Chemicals, such as polycyclic aromatic hydrocarbons (PAHs), emitted from combustion sources, fall into this category. Currently available ambient-air emission inventories of PAHs either fail to account for population-based activities (such as residential wood combustion and motor vehicle activity) and/or report 'total PAH' or particulate organic matter emissions instead of individual compounds. We measure the degree of overlap between predicted concentrations from estimated emissions with measured concentrations. Our analysis is, based on probabilistic analysis of measured outdoor air concentrations with those predicted from mass-balance models. . Based on available information, we estimate the relative magnitude of emissions from four major sources ofPAHs to outdoor air- (1) on-road motor vehicles, including light-duty gasoline vehicles and diesel-powered buses and medium and heavy duty trucks; (2) residential wood combustion; and (3) power generation from external combustion boilers. We use the CalTOX regional multimedia mass-balance model to evaluate our emissions estimates in rural and urban regions of the state of Minnesota, USA. We compare model estimatesof outdoor PAH airborne concentrations with those reported by the Minnesota Children's Pesticide Exposure Study (MNCPES). With these measured concentrations we probabilistically evaluate our emissions and interpret the reliability of our emissions estimates for specific PAHs. The median estimates of our predicted outdoor air concentrations agree within an order of magnitude of measured concentrations. For fourrepresentative PAHs, we were able to obtain a reasonable degree of overlap between empirical and predicted distributions of outdoor air concentrations. Our combination of models, emissions estimates, and empirical concentration data estimate exposure in a manner that is more reliable than any of these tools alone. Thereby, we increase our confidence about our plausible ranges of emissions and predicted concentrations
Optimal Reservoir Management and Well Placement Under Geologic Uncertainty
Reservoir management, sometimes referred to as asset management in the context of petroleum reservoirs, has become recognized as an important facet of petroleum reservoir development and production operations. In the first stage of planning field development, the simulation model is calibrated to dynamic data (history matching). One of the aims of the research is to extend the streamline based generalized travel time inversion method for full field models with multimillion cells through the use of grid coarsening. This makes the streamline based inversion suitable for high resolution simulation models with decades long production history and numerous wells by significantly reducing the computational effort. In addition, a novel workflow is proposed to integrate well bottom-hole pressure data during model calibration and the approach is illustrated via application to the CO2 sequestration. In the second stage, field development strategies are optimized. The strategies are primarily focused on rate optimization followed by infill well drilling. A method is proposed to modify the streamline-based rate optimization approach which previously focused on maximizing sweep efficiency by equalizing arrival time of the waterfront to producers, to account for accelerated production for improving the net present value (NPV). Optimum compromise between maximizing sweep efficiency and maximizing NPV can be selected based on a 'trade-off curve.' The proposed method is demonstrated on field scale application considering geological uncertainty. Finally, a novel method for well placement optimization is proposed that relies on streamlines and time of flight to first locate the potential regions of poorly swept and drained oil. Specifically, the proposed approach utilizes a dynamic measure based on the total streamline time of flight combined with static and dynamic parameters to identify "Sweet-Spots" for infill drilling. The "Sweet-Spots" can be either used directly as potential well-placement locations or as starting points during application of a formal optimization technique. The main advantage of the proposed method is its computational efficiency in calculating dynamic measure map. The complete workflow was also demonstrated on a multimillion cell reservoir model of a mature carbonate field with notable success. The infill locations based on dynamic measure map have been verified by subsequent drilling.
Taware, Satyajit Vijay
Estimation and reduction of the uncertainties in chemical models: Application to hot core chemistry
It is not common to consider the role of uncertainties in the rate coefficients used in interstellar gas-phase chemical models. In this paper, we report a new method to determine both the uncertainties in calculated molecular abundances and their sensitivities to underlying uncertainties in the kinetic data utilized. The method is used in hot core models to determine if previous analyses of the age and the applicable cosmic-ray ionization rate are valid. We conclude that for young hot cores ($\\le 10^4$ yr), the modeling uncertainties related to rate coefficients are reasonable so that comparisons with observations make sense. On the contrary, the modeling of older hot cores is characterized by strong uncertainties for some of the important species. In both cases, it is crucial to take into account these uncertainties to draw conclusions from the comparison of observations with chemical models.
Wakelam, V; Herbst, E; Caselli, P; Wakelam, Valentine; Selsis, Franck; Herbst, Eric; Caselli, Paola
Valentine Wakelam; Franck Selsis; Eric Herbst; Paola Caselli
Fuzzy-algebra uncertainty analysis for abnormal-environment safety assessment
Many safety (risk) analyses depend on uncertain inputs and on mathematical models chosen from various alternatives, but give fixed results (implying no uncertainty). Conventional uncertainty analyses help, but are also based on assumptions and models, the accuracy of which may be difficult to assure. Some of the models and assumptions that on cursory examination seem reasonable can be misleading. As a result, quantitative assessments, even those accompanied by uncertainty measures, can give unwarranted impressions of accuracy. Since analysis results can be a major contributor to a safety-measure decision process, risk management depends on relating uncertainty to only the information available. The uncertainties due to abnormal environments are even more challenging than those in normal-environment safety assessments, and therefore require an even more cautious approach. A fuzzy algebra analysis is proposed in this report that has the potential to appropriately reflect the information available and portray uncertainties well, especially for abnormal environments.
Cooper, J.A.
Isotopic Bias and Uncertainty for Burnup Credit Applications
The application of burnup credit requires calculating the isotopic inventory of the irradiated fuel. The depletion calculation simulates the burnup of the fuel under reactor operating conditions. The result of the depletion analysis is the predicted isotopic composition, which is ultimately input to a criticality analysis to determine the system multiplication factor (k{sub eff}). This paper demonstrates an approach for calculating the isotopic bias and uncertainty in k{sub eff} for commercial spent nuclear fuel burnup credit. This paper covers 74 different radiochemical assayed spent fuel samples from 22 different fuel assemblies that were irradiated in eight different pressurized water reactors (PWRs). The samples evaluated span an enrichment range of 2.556 wt% U-235 through 4.67 wt% U-235, and burnups from 6.92 GWd/MTU through 55.7 GWd/MTU.
J.M. Scaglione
Thermodynamic uncertainty relations again: A reply to Lavenda
In a previous paper (Found. Phys. 29, 655, (1999)), we have presented a review of various approaches in the literature towards the derivation of so-called thermodynamic uncertainty relations in statistical thermodynamics. This review has been critical. We have argued that some of these approaches are sound, i.e. they reach a valid conclusion, albeit under restricted conditions, whereas others were found to be incoherent and could not withstand the scrutiny of logical analysis. In the latter category we have included work of Lavenda on this topic. However, in a comment (Found. Phys. Lett. 13, 487 (2000)), Lavenda claims to have uncovered "fundamental errors" in our paper. In this reply we show that these claims are mistaken.
J. Uffink; J. Van Lith
Reaction rate uncertainties and the {nu}p-process
Current hydrodynamical simulations of core collapse supernovae find proton-rich early ejecta. At the same time, the models fail to eject neutron-rich matter, thus leaving the origin of the main r-process elements unsolved. However, the proton-rich neutrino-driven winds from supernovae have been identified as a possible production site for light n-capture elements beyond iron (such as Ge, Sr, Y, Zr) through the {nu}p-process. The detailed nucleosynthesis patterns of the {nu}p-process depend on the hydrodynamic conditions and the nuclear reaction rates of key reactions. We investigate the impact of reaction rate uncertainties on the {nu}p-process nucleosynthesis.
Froehlich, C.; Rauscher, T. [Department of Physics, North Carolina State University, Raleigh, NC 27695 (United States); Dept. of Physics, University of Basel, 4056 Basel (Switzerland)
Microsoft Word - feb10-Price Uncertainty Supplement.doc
February 2010 February 2010 Short-Term Energy Outlook Energy Price Volatility and Forecast Uncertainty 1 February 12, 2010 Release Crude Oil Prices. WTI crude oil spot prices averaged $78.33 per barrel in January 2010, almost $4 per barrel higher than the prior month's average and matching the $78-per-barrel forecast in last month's Outlook. The WTI spot price peaked at $83.12 on January 6 and then fell to $72.85 on January 29 as the weather turned warm and concerns about the strength of world economic recovery increased. EIA forecasts that WTI spot prices will remain near current levels over the next few months, averaging $76 per barrel in February and March, before rising to about $82 per barrel in the late
Guidelines for Managing Reactor Vessel Material Uncertainties: Part 1: General Approach Part 2: Implementation Guide
Uncertainties about reactor vessel material toughness properties can be a concern for utilities when characterizing vessel integrity. In addition, recent emphasis on variability in material chemistry and initial toughness properties has added to regulatory concerns. This two-part guidelines document provides a general approach (Part 1) for dealing with weld metal property variability and material uncertainties and demonstrates examples of different approaches (Part 2) for dealing with these uncertainties...
Principles and applications of measurement and uncertainty analysis in research and calibration
Interest in Measurement Uncertainty Analysis has grown in the past several years as it has spread to new fields of application, and research and development of uncertainty methodologies have continued. This paper discusses the subject from the perspectives of both research and calibration environments. It presents a history of the development and an overview of the principles of uncertainty analysis embodied in the United States National Standard, ANSI/ASME PTC 19.1-1985, Measurement Uncertainty. Examples are presented in which uncertainty analysis was utilized or is needed to gain further knowledge of a particular measurement process and to characterize final results. Measurement uncertainty analysis provides a quantitative estimate of the interval about a measured value or an experiment result within which the true value of that quantity is expected to lie. Years ago, Harry Ku of the United States National Bureau of Standards stated that The informational content of the statement of uncertainty determines, to a large extent, the worth of the calibrated value.'' Today, that statement is just as true about calibration or research results as it was in 1968. Why is that true What kind of information should we include in a statement of uncertainty accompanying a calibrated value How and where do we get the information to include in an uncertainty statement How should we interpret and use measurement uncertainty information This discussion will provide answers to these and other questions about uncertainty in research and in calibration. The methodology to be described has been developed by national and international groups over the past nearly thirty years, and individuals were publishing information even earlier. Yet the work is largely unknown in many science and engineering arenas. I will illustrate various aspects of uncertainty analysis with some examples drawn from the radiometry measurement and calibration discipline from research activities.
Wells, C.V.
Interest in Measurement Uncertainty Analysis has grown in the past several years as it has spread to new fields of application, and research and development of uncertainty methodologies have continued. This paper discusses the subject from the perspectives of both research and calibration environments. It presents a history of the development and an overview of the principles of uncertainty analysis embodied in the United States National Standard, ANSI/ASME PTC 19.1-1985, Measurement Uncertainty. Examples are presented in which uncertainty analysis was utilized or is needed to gain further knowledge of a particular measurement process and to characterize final results. Measurement uncertainty analysis provides a quantitative estimate of the interval about a measured value or an experiment result within which the true value of that quantity is expected to lie. Years ago, Harry Ku of the United States National Bureau of Standards stated that ``The informational content of the statement of uncertainty determines, to a large extent, the worth of the calibrated value.`` Today, that statement is just as true about calibration or research results as it was in 1968. Why is that true? What kind of information should we include in a statement of uncertainty accompanying a calibrated value? How and where do we get the information to include in an uncertainty statement? How should we interpret and use measurement uncertainty information? This discussion will provide answers to these and other questions about uncertainty in research and in calibration. The methodology to be described has been developed by national and international groups over the past nearly thirty years, and individuals were publishing information even earlier. Yet the work is largely unknown in many science and engineering arenas. I will illustrate various aspects of uncertainty analysis with some examples drawn from the radiometry measurement and calibration discipline from research activities.
day • EP t : wholesale electricity price during day t (in $/Response under Uncertainty 2006 electricity price Simulatedsample path Electricity price ($/MWh e ) Day Figure 3:
Long-Term Planning for Nuclear Energy Systems Under Deep Uncertainty
scientific resources for decommissioning a nuclear facility.t) i Decommissioning Decommissioning of a nuclear facilityDecommissioning Funding: Ethics, Implementa- tion, Uncertainties. Nuclear
Kim, Lance Kyungwoo
Guideline for the Treatment of Uncertainty in Risk-Informed Applications: Technical Basis Document
This report provides the technical basis for a process to characterize the uncertainty distribution for risk metrics derived from probabilistic risk assessments (PRAs).
Maximizing Gross Margin of a Pumped Storage Hydroelectric Facility Under Uncertainty in Price and Water Inflow.
??The operation of a pumped storage hydroelectric facility is subject to uncertainty. This is especially true in today's energy markets. Published models to achieve optimal… (more)
Ikudo, Akina
Uncertainty Analysis of Certified Photovoltaic Measurements at the National Renewable Energy Laboratory
Discusses NREL Photovoltaic Cell and Module Performance Characterization Group's procedures to achieve lowest practical uncertainty in measuring PV performance with respect to reference conditions.
Emery, K.
maintenance and repair policies in infrastructure managementOptimal maintenance decisions for pavement management. " J.Maintenance; Optimization; Probabilistic models; Adaptive systems; Uncertainty principles . Introduction Infrastructure management
Calibration of a distributed flood forecasting model with input uncertainty using a Bayesian framework
Calibrated probabilistic forecasting using ensemble modelSutcliffe (1970), River flow forecasting through conceptuala Distributed Flood Forecasting Model with Input Uncertainty
Li, M.
Bayesian Neural Networks for Uncertainty Analysis of Hydrologic Modeling: A Comparison of Two Schemes
Bayesian Neural Networks (BNNs) have been shown as useful tools to analyze modeling uncertainty of Neural Networks (NNs). This research focuses on the comparison of two BNNs. The first BNNs (BNN-I) use statistical methods to describe the characteristics of different uncertainty sources (input, parameter, and model structure) and integrate these uncertainties into a Markov Chain Monte Carlo (MCMC) framework to estimate total uncertainty. The second BNNs (BNN-II) lump all uncertainties into a single error term (i.e. the residual between model prediction and measurement). In this study, we propose a simple BNN-II, which use Genetic Algorithms (GA) and Bayesian Model Averaging (BMA) to calibrate Neural Networks with different structures (number of hidden units) and combine the predictions from different NNs to derive predictions and uncertainty analysis. We tested these two BNNs in two watersheds for daily and monthly hydrologic simulation. The BMA based BNNs developed in this study outperforms BNN-I in the two watersheds in terms of both accurate prediction and uncertainty estimation. These results show that, given incomplete understanding of the characteristics associated with each uncertainty source, the simple lumped error approach may yield better prediction and uncertainty estimation.
Zhang, Xuesong; Zhao, Kaiguang
Nuclear data uncertainties by the PWR MOX/UO{sub 2} core rod ejection benchmark
Rod ejection transient of the OECD/NEA and U.S. NRC PWR MOX/UO{sub 2} core benchmark is considered under the influence of nuclear data uncertainties. Using the GRS uncertainty and sensitivity software package XSUSA the propagation of the uncertainties in nuclear data up to the transient calculations are considered. A statistically representative set of transient calculations is analyzed and both integral as well as local output quantities are compared with the benchmark results of different participants. It is shown that the uncertainties in nuclear data play a crucial role in the interpretation of the results of the simulation. (authors)
Pasichnyk, I.; Klein, M.; Velkov, K.; Zwermann, W.; Pautz, A. [Boltzmannstr. 14, D-85748 Garching b. Muenchen (Germany)
BWRVIP-189: BWR Vessel and Internals Project, Evaluation of RAMA Fluence Methodology Calculational Uncertainty
This report documents the overall calculational uncertainty associated with the application of the Radiation Application Modeling Application (RAMA) Fluence Methodology to BWR reactor pressure vessel fluence evaluations.
From CVaR to Uncertainty Set: Implications in Joint Chance
Jan 12, 2007 ... From CVaR to Uncertainty Set: Implications in Joint Chance Constrained Optimization. Melvyn Sim (dscsimm ***at*** nus.edu.sg) Chung Piaw ...
Uncertainty for Satellite and Station Solar Data in the Updated NSRDB
Solar Resource Assessment Workshop, Denver CO, Oct 29, 2008 presentation: Uncertainty for Satellite and Station Solar Data in the Updated NSRDB,
Myers, D. R.
Uncertainty analysis of an IGCC system with single-stage entrained-flow gasifier
Integrated Gasification Combined Cycle (IGCC) systems using coal gasification is an attractive option for future energy plants. Consequenty, understanding the system operation and optimizing gasifier performance in the presence of uncertain operating conditions is essential to extract the maximum benefits from the system. This work focuses on conducting such a study using an IGCC process simulation and a high-fidelity gasifier simulation coupled with stochastic simulation and multi-objective optimization capabilities. Coal gasifiers are the necessary basis of IGCC systems, and hence effective modeling and uncertainty analysis of the gasification process constitutes an important element of overall IGCC process design and operation. In this work, an Aspen Plus{reg_sign} steady-state process model of an IGCC system with carbon capture enables us to conduct simulation studies so that the effect of gasification variability on the whole process can be understood. The IGCC plant design consists of an single-stage entrained-flow gasifier, a physical solvent-based acid gas removal process for carbon capture, two model-7FB combustion turbine generators, two heat recovery steam generators, and one steam turbine generator in a multi-shaft 2x2x1 configuration. In the Aspen Plus process simulation, the gasifier is represented as a simplified lumped-parameter, restricted-equilibrium reactor model. In this work, we also make use of a distributed-parameter FLUENT{reg_sign} computational fluid dynamics (CFD) model to characterize the uncertainty for the entrained-flow gasifier. The CFD-based gasifer model is much more comprehensive, predictive, and hence better suited to understand the effects of uncertainty. The possible uncertain parameters of the gasifier model are identified. This includes input coal composition as well as mass flow rates of coal, slurry water, and oxidant. Using a selected number of random (Monte Carlo) samples for the different parameters, the CFD model is simulated to observe the variations in the output variables (such as syngas composition, gas and ash flow rates etc.). The same samples are then used to conduct simulations using the Aspen Plus IGCC model. The simulation results for the high-fidelity CFD-based gasifier model and the Aspen Plus equilibrium reactor model for selected uncertain parameters are then used to perform the estimation. Defining the ratio of CFD based results to the Aspen Plus result as the uncertainty factor (UF), the work quantifies the extent of uncertainty and then uses uniform* distribution to characterize the uncertainty factor distribution. The characterization and quantification of uncertainty is then used to conduct stochastic simulation of the IGCC system in Aspen Plus. The CAPE-OPEN compliant stochastic simulation capability allows one to conduct a rigorous analysis and generate the feasible space for the operation of the IGCC system. The stochastic simulation results can later be used to conduct multi-objective optimization of the gasifier using a set of identified decision variables. The CAPE-OPEN compliant multi-objective capability in Aspen Plus can be used to conduct the analysis. Since the analysis is based on the uncertainty modeling studies of the gasifier, the optimization accounts for possible uncertainties in the operation of the system. The results for the optimized IGCC system and the gasifier, obtained from the stochastic simulation results, are expected to be more rigorous and hence closer to those obtained from CFD-based rigorous modeling.
Shastri, Y.; Diwekar, U.; Zitney, S.
Cassini Spacecraft Uncertainty Analysis Data and Methodology Review and Update/Volume 1: Updated Parameter Uncertainty Models for the Consequence Analysis
Uncertainty distributions for specific parameters of the Cassini General Purpose Heat Source Radioisotope Thermoelectric Generator (GPHS-RTG) Final Safety Analysis Report consequence risk analysis were revised and updated. The revisions and updates were done for all consequence parameters for which relevant information exists from the joint project on Probabilistic Accident Consequence Uncertainty Analysis by the United States Nuclear Regulatory Commission and the Commission of European Communities.
CALiPER Exploratory Study: Accounting for Uncertainty in Lumen Measurements
With a well-defined and shared understanding of uncertainty in lumen measurements, testing laboratories can better evaluate their processes, contributing to greater consistency and credibility of lighting testing a key component of the U.S. Department of Energy (DOE) Commercially Available LED Product Evaluation and Reporting (CALiPER) program. Reliable lighting testing is a crucial underlying factor contributing toward the success of many energy-efficient lighting efforts, such as the DOE GATEWAY demonstrations, Lighting Facts Label, ENERGY STAR® energy efficient lighting programs, and many others. Uncertainty in measurements is inherent to all testing methodologies, including photometric and other lighting-related testing. Uncertainty exists for all equipment, processes, and systems of measurement in individual as well as combined ways. A major issue with testing and the resulting accuracy of the tests is the uncertainty of the complete process. Individual equipment uncertainties are typically identified, but their relative value in practice and their combined value with other equipment and processes in the same test are elusive concepts, particularly for complex types of testing such as photometry. The total combined uncertainty of a measurement result is important for repeatable and comparative measurements for light emitting diode (LED) products in comparison with other technologies as well as competing products. This study provides a detailed and step-by-step method for determining uncertainty in lumen measurements, working closely with related standards efforts and key industry experts. This report uses the structure proposed in the Guide to Uncertainty Measurements (GUM) for evaluating and expressing uncertainty in measurements. The steps of the procedure are described and a spreadsheet format adapted for integrating sphere and goniophotometric uncertainty measurements is provided for entering parameters, ordering the information, calculating intermediate values and, finally, obtaining expanded uncertainties. Using this basis and examining each step of the photometric measurement and calibration methods, mathematical uncertainty models are developed. Determination of estimated values of input variables is discussed. Guidance is provided for the evaluation of the standard uncertainties of each input estimate, covariances associated with input estimates and the calculation of the result measurements. With this basis, the combined uncertainty of the measurement results and finally, the expanded uncertainty can be determined.
Bergman, Rolf; Paget, Maria L.; Richman, Eric E.
A Higher Order GUP with Minimal Length Uncertainty and Maximal Momentum II: Applications
In a recent paper, we presented a nonperturbative higher order generalized uncertainty principle (GUP) that is consistent with various proposals of quantum gravity such as string theory, loop quantum gravity, doubly special relativity, and predicts both a minimal length uncertainty and a maximal observable momentum. In this Letter, we find exact maximally localized states and present a formally self-adjoint and naturally perturbative representation of this modified algebra. Then we extend this GUP to D dimensions that will be shown it is noncommutative and find invariant density of states. We show that the presence of the maximal momentum results in upper bounds on the energy spectrum of the free particle and the particle in box. Moreover, this form of GUP modifies blackbody radiation spectrum at high frequencies and predicts a finite cosmological constant. Although it does not solve the cosmological constant problem, it gives a better estimation with respect to the presence of just the minimal length.
Pouria Pedram
Nuclear Structure Uncertainties in Parity-Violating Electron Scattering from Carbon 12
High precision measurements of the parity-violating asymmetry in polarized electron scattering from nuclei can be used to extract information on nuclear and nucleon structure or to determine Standard Model couplings and higher-order radiative corrections. To this end, low uncertainties are also required in the effects that inevitably arise from modeling the underlying nuclear structure. An experimental precision of a few tenths of a percent may be attainable for the asymmetry if the appropriate kinematic range is chosen, as will be discussed here for the case of $^{12}$C. And given this, the dual goal of ascertaining both the sizes of various nuclear structure related effects and of providing estimates of their uncertainties for this particular target will be discussed.
O. Moreno; T. W. Donnelly
Uncertainties in Cancer Risk Coefficients for Environmental Exposure to Radionuclides. An Uncertainty Analysis for Risk Coefficients Reported in Federal Guidance Report No. 13
Federal Guidance Report No. 13 (FGR 13) provides risk coefficients for estimation of the risk of cancer due to low-level exposure to each of more than 800 radionuclides. Uncertainties in risk coefficients were quantified in FGR 13 for 33 cases (exposure to each of 11 radionuclides by each of three exposure pathways) on the basis of sensitivity analyses in which various combinations of plausible biokinetic, dosimetric, and radiation risk models were used to generate alternative risk coefficients. The present report updates the uncertainty analysis in FGR 13 for the cases of inhalation and ingestion of radionuclides and expands the analysis to all radionuclides addressed in that report. The analysis indicates that most risk coefficients for inhalation or ingestion of radionuclides are determined within a factor of 5 or less by current information. That is, application of alternate plausible biokinetic and dosimetric models and radiation risk models (based on the linear, no-threshold hypothesis with an adjustment for the dose and dose rate effectiveness factor) is unlikely to change these coefficients by more than a factor of 5. In this analysis the assessed uncertainty in the radiation risk model was found to be the main determinant of the uncertainty category for most risk coefficients, but conclusions concerning the relative contributions of risk and dose models to the total uncertainty in a risk coefficient may depend strongly on the method of assessing uncertainties in the risk model.
Pawel, David [U.S. Environmental Protection Agency; Leggett, Richard Wayne [ORNL; Eckerman, Keith F [ORNL; Nelson, Christopher [U.S. Environmental Protection Agency
Economics of shareware: How do uncertainty and piracy affect shareware quality and brand premium?
Increased network speed has opened up new opportunities for developers to distribute their products as shareware through the Internet. This paper analyzes the interrelationships among key issues that are central to the software industry, including uncertainty, ... Keywords: Piracy, Pricing, Shareware economics, Uncertainty, Versioning
Wendy Hui; Byungjoon Yoo; Kar Yan Tam
Integrated modelling of risk and uncertainty underlying the cost and effectiveness of water quality measures
In this paper we present an overview of the most important sources of uncertainty when analysing the least cost way to improve water quality. The estimation of the cost-effectiveness of water quality measures is surrounded by environmental, economic ... Keywords: Cost-effectiveness, Integrated modelling, Risk, Uncertainty, Water quality
Roy Brouwer; Chris De Blois
Many-objective de Novo water supply portfolio planning under deep uncertainty
This paper proposes and demonstrates a new interactive framework for sensitivity-informed de Novo planning to confront the deep uncertainty within water management problems. The framework couples global sensitivity analysis using Sobol' variance decomposition ... Keywords: Decision support, Many-objective decision analytics, Multiobjective evolutionary algorithms, Risk, Robust decision making, Sensitivity analysis, Uncertainty
Joseph R. Kasprzyk; Patrick M. Reed; Gregory W. Characklis; Brian R. Kirsch
Uncertainty in Scenarios of Human-Caused Climate NATHAN J MANTUA1
greenhouse gas emissions and atmospheric concentrations, and second is the uncertainty associated for eliminating, or even vastly reducing, environmental uncertainty for the purpose of improved natural resource emerged on key aspects of global climate change: humans have unquestionably altered the composition
Mantua, Nathan
Determination of Uncertainty in Gross Calorific Value of Coal Using Bomb Calorimeter
A bomb calorimeter is an apparatus used for measuring the performance of coal in term of heat of combustion. Recent awareness has been created regarding uncertainty of measurement, due to mainly two reasons. Laboratory accreditation, which has steadily ... Keywords: Bomb Calorimeter, Gross Calorific Value, Heat, Uncertainty, Water Equivalent
N.K. Mandavgade; S.B. Jaju; R.R. Lakhe
MINERAL: A program for the propagation of analytical uncertainty through mineral formula recalculations
MINERAL (MINeral ERror AnaLysis) is a MATLAB^(R) based program that performs mineral formula recalculations and calculates the error on formula unit cations though the propagation of analytical uncertainties. The program is focused on 9 common mineral ... Keywords: Error, Mineral recalculation, Uncertainty
Sarah M. H. De Angelis; Owen K. Neill
Learning uncertainty models from weather forecast performance databases using quantile regression
Forecast uncertainty information is not available in the immediate output of Numerical weather prediction (NWP) models. Such important information is required for optimal decision making processes in many domains. Prediction intervals are a prominent ... Keywords: numerical weather forecast, prediction interval, quantile regression, uncertainty modeling
Ashkan Zarnani; Petr Musilek
Interaction of loading pattern and nuclear data uncertainties in reactor core calculations
Along with best-estimate calculations for design and safety analysis, understanding uncertainties is important to determine appropriate design margins. In this framework, nuclear data uncertainties and their propagation to full core calculations are a critical issue. To deal with this task, different error propagation techniques, deterministic and stochastic are currently developed to evaluate the uncertainties in the output quantities. Among these is the sampling based uncertainty and sensitivity software XSUSA which is able to quantify the influence of nuclear data covariance on reactor core calculations. In the present work, this software is used to investigate systematically the uncertainties in the power distributions of two PWR core loadings specified in the OECD UAM-Benchmark suite. With help of a statistical sensitivity analysis, the main contributors to the uncertainty are determined. Using this information a method is studied with which loading patterns of reactor cores can be optimized with regard to minimizing power distribution uncertainties. It is shown that this technique is able to halve the calculation uncertainties of a MOX/UOX core configuration. (authors)
Klein, M.; Gallner, L.; Krzykacz-Hausmann, B.; Pautz, A.; Velkov, K.; Zwermann, W. [Gesellschaft fuer Anlagen- und Reaktorsicherheit GRS MbH, Boltzmannstr. 14, D- 85748 Garching b. Muenchen (Germany)
Carbon Cycle Uncertainty Increases Climate Change Risks and Mitigation Challenges PAUL A. T. HIGGINS
Carbon Cycle Uncertainty Increases Climate Change Risks and Mitigation Challenges PAUL A. T about the carbon cycle: 1) that elevated atmospheric CO2 concentrations will enhance terrestrial carbon that carbon cycle uncertainty is considerably larger than currently recognized and that plausible carbon cycle
Kammen, Daniel M.
Measuring the uncertainty of RFID data based on particle filter and particle swarm optimization
The management of the uncertainties over data is an urgent problem of novel applications such as cyber-physical system, sensor network and RFID data management. In order to adapt the characteristics of evolving over time of sensor data in real-time location ... Keywords: Adaptive, Cyber-physical system, Particle filter, RFID data, Real-time location tracing service, Uncertainty
Yongli Wang; Jiangbo Qian
Climate uncertainty and implications for U.S. state-level risk assessment through 2050.
Decisions for climate policy will need to take place in advance of climate science resolving all relevant uncertainties. Further, if the concern of policy is to reduce risk, then the best-estimate of climate change impacts may not be so important as the currently understood uncertainty associated with realizable conditions having high consequence. This study focuses on one of the most uncertain aspects of future climate change - precipitation - to understand the implications of uncertainty on risk and the near-term justification for interventions to mitigate the course of climate change. We show that the mean risk of damage to the economy from climate change, at the national level, is on the order of one trillion dollars over the next 40 years, with employment impacts of nearly 7 million labor-years. At a 1% exceedance-probability, the impact is over twice the mean-risk value. Impacts at the level of individual U.S. states are then typically in the multiple tens of billions dollar range with employment losses exceeding hundreds of thousands of labor-years. We used results of the Intergovernmental Panel on Climate Change's (IPCC) Fourth Assessment Report 4 (AR4) climate-model ensemble as the referent for climate uncertainty over the next 40 years, mapped the simulated weather hydrologically to the county level for determining the physical consequence to economic activity at the state level, and then performed a detailed, seventy-industry, analysis of economic impact among the interacting lower-48 states. We determined industry GDP and employment impacts at the state level, as well as interstate population migration, effect on personal income, and the consequences for the U.S. trade balance.
Loose, Verne W.; Lowry, Thomas Stephen; Malczynski, Leonard A.; Tidwell, Vincent Carroll; Stamber, Kevin Louis; Kelic, Andjelka; Backus, George A.; Warren, Drake E.; Zagonel, Aldo A.; Ehlen, Mark Andrew; Klise, Geoffrey T.; Vargas, Vanessa N.
Summary Short?Term Energy Outlook Supplement: Energy Price Volatility and Forecast Uncertainty 1
It is often noted that energy prices are quite volatile, reflecting market participants' adjustments to new information from physical energy markets and/or markets in energyrelated financial derivatives. Price volatility is an indication of the level of uncertainty, or risk, in the market. This paper describes how markets price risk and how the marketclearing process for risk transfer can be used to generate "price bands " around observed futures prices for crude oil, natural gas, and other commodities. These bands provide a quantitative measure of uncertainty regarding the range in which markets expect prices to trade. The Energy Information Administration's (EIA) monthly Short-Term Energy Outlook (STEO) publishes "base case " projections for a variety of energy prices that go out 12 to 24 months (every January the STEO forecast is extended through December of the following year). EIA has recognized that all price forecasts are highly uncertain and has described the uncertainty by identifying the market factors that may significantly move prices away from their expected paths, such as economic growth, Organization of Petroleum Exporting Countries (OPEC) behavior, geo-political events, and hurricanes.
unknown authors
Uncertainty estimation of core safety parameters using cross-correlations of covariance matrix
An uncertainty estimation method for core safety parameters, for which measurement values are not obtained, is proposed. We empirically recognize the correlations among the prediction errors among core safety parameters, e.g., a correlation between the control rod worth and assembly relative power of corresponding position. Correlations of uncertainties among core safety parameters are theoretically estimated using the covariance of cross sections and sensitivity coefficients for core parameters. The estimated correlations among core safety parameters are verified through the direct Monte-Carlo sampling method. Once the correlation of uncertainties among core safety parameters is known, we can estimate the uncertainty of a safety parameter for which measurement value is not obtained. Furthermore, the correlations can be also used for the reduction of uncertainties of core safety parameters. (authors)
Yamamoto, A.; Yasue, Y.; Endo, T. [Graduate School of Engineering, Nagoya Univ., Furo-cho, Chikusa-ku, Nagoya (Japan); Kodama, Y.; Ohoka, Y.; Tatsumi, M. [Nuclear Fuel Industries, Ltd., Asashiro-nishi, Kumatori-cho, Osaka (Japan)
Uncertainty in site inspection and tracking database estimates of savings
The authors systematically analyze impact evaluation results of three commercial lighting rebate DSM programs. The research includes (1) analysis of ex ante and ex post estimates of program performance, broken down into critical program parameters: hours of operation, watts saved per measure, and measures installed per site; (2) construction of probability distributions of program performance, both in the aggregate and for these critical program parameters; and (3) use of these analyses and distributions to draw conclusions about the accuracy of savings estimates from a variety of evaluation methods. The analysis suggests that realization rates (a ratio of metered savings estimates to tracking database savings estimates) for the sample of participants they examine are subject to tremendous variability, calling into question the usefulness of a point estimate of the realization rate. Discrepancies in estimates of hours of operation are responsible for most of the uncertainty in the realization rate. Finally, the impact of shorter measure lifetimes on savings estimates suggest that persistence studies should be an evaluation priority.
Sonnenblick, R.; Eto, J. [Lawrence Berkeley National Lab., CA (United States)
Utilization of extended bayesian networks in decision making under uncertainty
Bayesian network tool (called IKE for Integrated Knowledge Engine) has been developed to assess the probability of undesirable events. The tool allows indications and observables from sensors and/or intelligence to feed directly into hypotheses of interest, thus allowing one to quantify the probability and uncertainty of these events resulting from very disparate evidence. For example, the probability that a facility is processing nuclear fuel or assembling a weapon can be assessed by examining the processes required, establishing the observables that should be present, then assembling information from intelligence, sensors and other information sources related to the observables. IKE also has the capability to determine tasking plans, that is, prioritize which observable should be collected next to most quickly ascertain the 'true' state and drive the probability toward 'zero' or 'one.' This optimization capability is called 'evidence marshaling.' One example to be discussed is a denied facility monitoring situation; there is concern that certain process(es) are being executed at the site (due to some intelligence or other data). We will show how additional pieces of evidence will then ascertain with some degree of certainty the likelihood of this process(es) as each piece of evidence is obtained. This example shows how both intelligence and sensor data can be incorporated into the analysis. A second example involves real-time perimeter security. For this demonstration we used seismic, acoustic, and optical sensors linked back to IKE. We show how these sensors identified and assessed the likelihood of 'intruder' versus friendly vehicles.
Van Eeckhout, Edward M [Los Alamos National Laboratory; Leishman, Deborah A [Los Alamos National Laboratory; Gibson, William L [Los Alamos National Laboratory
Remaining uncertainties in the kinetic mechanism of hydrogen combustion
An analysis of the performance of an updated hydrogen combustion mechanism is presented. Particular attention was paid to different channels of reaction between H atoms and HO{sub 2} radicals, to pressure dependence of the recombination of HO{sub 2} radicals, and to the anomalous rate constant of reaction between OH and HO{sub 2} radicals. The contemporary choice of the reaction rate constants is presented with the emphasis on their uncertainties. Then the predictions of ignition, oxidation, flame burning velocities, and flame structure of hydrogen-oxygen-inert mixtures are shown. The modeling range covers ignition experiments from 950 to 2700 K and from subatmospheric pressures up to 87 atm; hydrogen oxidation in a flow reactor at temperatures around 900 K from 0.3 up to 15.7 atm; flame burning velocities in hydrogen-oxygen-inert mixtures from 0.35 up to 4 atm; and hydrogen flame structure at 1 and 10 atm. Comparison of the modeling and experiments is discussed in terms of the range of applicability of the present detailed mechanism. The necessity for analysis of the mechanism to have an exhaustive list of reactions is emphasized. (author)
Konnov, Alexander A. [Department of Mechanical Engineering, Vrije Universiteit Brussel, Brussels (Belgium)
OPEC Middle East plans for rising world demand amid uncertainty
The Middle Eastern members of the Organization of Petroleum Exporting Countries must plan for huge increases in oil production capacity yet wonder whether markets for the new output will develop as expected. With worldwide oil consumption rising and non-OPEC output likely to reach its resource limits soon, OPEC member countries face major gains in demand for their crude oil. To meet the demand growth, those with untapped resources will have to invest heavily in production capacity. Most OPEC members with such resources are in the Middle East. But financing the capacity investments remains a challenge. Some OPEC members have opened up to foreign equity participation in production projects, and others may eventually do so as financial pressures grow. That means additions to the opportunities now available to international companies in the Middle East. Uncertainties, however, hamper planning and worry OPEC. Chief among them are taxation and environmental policies of consuming-nation governments. This paper reviews these concerns and provides data on production, pricing, capital investment histories and revenues.
Ismail, I.A.H. [Organization of Petroleum Exporting Countries, Vienna (Austria)
Calibration under uncertainty for finite element models of masonry monuments
Historical unreinforced masonry buildings often include features such as load bearing unreinforced masonry vaults and their supporting framework of piers, fill, buttresses, and walls. The masonry vaults of such buildings are among the most vulnerable structural components and certainly among the most challenging to analyze. The versatility of finite element (FE) analyses in incorporating various constitutive laws, as well as practically all geometric configurations, has resulted in the widespread use of the FE method for the analysis of complex unreinforced masonry structures over the last three decades. However, an FE model is only as accurate as its input parameters, and there are two fundamental challenges while defining FE model input parameters: (1) material properties and (2) support conditions. The difficulties in defining these two aspects of the FE model arise from the lack of knowledge in the common engineering understanding of masonry behavior. As a result, engineers are unable to define these FE model input parameters with certainty, and, inevitably, uncertainties are introduced to the FE model.
Atamturktur, Sezer,; Hemez, Francois,; Unal, Cetin
Incorporating the Technology Roadmap Uncertainties into the Project Risk Assessment
This paper describes two methods, Technology Roadmapping and Project Risk Assessment, which were used to identify and manage the technical risks relating to the treatment of sodium bearing waste at the Idaho National Engineering and Environmental Laboratory. The waste treatment technology under consideration was Direct Vitrification. The primary objective of the Technology Roadmap is to identify technical data uncertainties for the technologies involved and to prioritize the testing or development studies to fill the data gaps. Similarly, project management's objective for a multi-million dollar construction project includes managing all the key risks in accordance to DOE O 413.3 - "Program and Project Management for the Acquisition of Capital Assets." In the early stages, the Project Risk Assessment is based upon a qualitative analysis for each risk's probability and consequence. In order to clearly prioritize the work to resolve the technical issues identified in the Technology Roadmap, the issues must be cross- referenced to the project's Risk Assessment. This will enable the project to get the best value for the cost to mitigate the risks.
Bonnema, Bruce Edward
This paper describes two methods, Technology Roadmapping and Project Risk Assessment, which were used to identify and manage the technical risks relating to the treatment of sodium bearing waste at the Idaho National Engineering and Environmental Laboratory. The waste treatment technology under consideration was Direct Vitrification. The primary objective of the Technology Roadmap is to identify technical data uncertainties for the technologies involved and to prioritize the testing or development studies to fill the data gaps. Similarly, project management's objective for a multi-million dollar construction project includes managing all the key risks in accordance to DOE O 413.3 - ''Program and Project Management for the Acquisition of Capital Assets.'' In the early stages, the Project Risk Assessment is based upon a qualitative analysis for each risk's probability and consequence. In order to clearly prioritize the work to resolve the technical issues identified in the Technology Roadmap, the issues must be cross- referenced to the project's Risk Assessment. This will enable the project to get the best value for the cost to mitigate the risks.
Bonnema, B.E.
Continuous reservoir simulation incorporating uncertainty quantification and real-time data
A significant body of work has demonstrated both the promise and difficulty of quantifying uncertainty in reservoir simulation forecasts. It is generally accepted that accurate and complete quantification of uncertainty should lead to better decision making and greater profitability. Many of the techniques presented in past work attempt to quantify uncertainty without sampling the full parameter space, saving on the number of simulation runs, but inherently limiting and biasing the uncertainty quantification in the resulting forecasts. In addition, past work generally has looked at uncertainty in synthetic models and does not address the practical issues of quantifying uncertainty in an actual field. Both of these issues must be addressed in order to rigorously quantify uncertainty in practice. In this study a new approach to reservoir simulation is taken whereby the traditional one-time simulation study is replaced with a new continuous process potentially spanning the life of the reservoir. In this process, reservoir models are generated and run 24 hours a day, seven days a week, allowing many more runs than previously possible and yielding a more thorough exploration of possible reservoir descriptions. In turn, more runs enabled better estimates of uncertainty in resulting forecasts. A new technology to allow this process to run continuously with little human interaction is real-time production and pressure data, which can be automatically integrated into runs. Two tests of this continuous simulation process were conducted. The first test was conducted on the Production with Uncertainty Quantification (PUNQ) synthetic reservoir. Comparison of our results with previous studies shows that the continuous approach gives consistent and reasonable estimates of uncertainty. The second study was conducted in real time on a live field. This study demonstrates the continuous simulation process and shows that it is feasible and practical for real world applications.
Holmes, Jay Cuthbert
How do Uncertainties in the Surface Chemical Abundances of the Sun Affect the Predicted Solar Neutrino Fluxes?
We show that uncertainties in the values of the surface heavy element abundances of the Sun are the largest source of the theoretical uncertainty in calculating the p-p, pep, 8B, 13N, 15O, and 17F solar neutrino fluxes. We evaluate for the first time the sensitivity (partial derivative) of each solar neutrino flux with respect to the surface abundance of each element. We then calculate the uncertainties in each neutrino flux using `conservative (preferred)' and `optimistic' estimates for the uncertainties in the element abundances. The total conservative (optimistic) composition uncertainty in the predicted 8B neutrino flux is 11.6% (5.0%) when sensitivities to individual element abundances are used. The traditional method that lumps all abundances into a single quantity (total heavy element to hydrogen ratio, Z/X) yields a larger uncertainty, 20%. The uncertainties in the carbon, oxygen, neon, silicon, sulphur, and iron abundances all make significant contributions to the uncertainties in calculating solar neutrino fluxes; the uncertainties of different elements are most important for different neutrino fluxes. The uncertainty in the iron abundance is the largest source of the estimated composition uncertainties of the important 7Be and 8B solar neutrinos. Carbon is the largest contributor to the uncertainty in the calculation of the p-p, 13N, and 15O neutrino fluxes. However, for all neutrino fluxes, several elements contribute comparable amounts to the total composition uncertainty.
John N. Bahcall; Aldo M. Serenelli
Characterization, propagation and analysis of aleatory and epistemic uncertainty in the 2008 performance assessment for the proposed repository for high-level radioactive waste at Yucca Mountain, Nevada
The 2008 performance assessment (PA) for the proposed repository for high-level radioactive waste at Yucca Mountain (YM), Nevada, illustrates the conceptual structure of risk assessments for complex systems. The 2008 YM PA is based on the following three ...
Clifford W. Hansen; Jon C. Helton; Cédric J. Sallaberry
Uncertainty Quantification of Hypothesis Testing for the Integrated Knowledge Engine
The Integrated Knowledge Engine (IKE) is a tool of Bayesian analysis, based on Bayesian Belief Networks or Bayesian networks for short. A Bayesian network is a graphical model (directed acyclic graph) that allows representing the probabilistic structure of many variables assuming a localized type of dependency called the Markov property. The Markov property in this instance makes any node or random variable to be independent of any non-descendant node given information about its parent. A direct consequence of this property is that it is relatively easy to incorporate new evidence and derive the appropriate consequences, which in general is not an easy or feasible task. Typically we use Bayesian networks as predictive models for a small subset of the variables, either the leave nodes or the root nodes. In IKE, since most applications deal with diagnostics, we are interested in predicting the likelihood of the root nodes given new observations on any of the children nodes. The root nodes represent the various possible outcomes of the analysis, and an important problem is to determine when we have gathered enough evidence to lean toward one of these particular outcomes. This document presents criteria to decide when the evidence gathered is sufficient to draw a particular conclusion or decide in favor of a particular outcome by quantifying the uncertainty in the conclusions that are drawn from the data. The material in this document is organized as follows: Section 2 presents briefly a forensics Bayesian network, and we explore evaluating the information provided by new evidence by looking first at the posterior distribution of the nodes of interest, and then at the corresponding posterior odds ratios. Section 3 presents a third alternative: Bayes Factors. In section 4 we finalize by showing the relation between the posterior odds ratios and Bayes factors and showing examples these cases, and in section 5 we conclude by providing clear guidelines of how to use these for the type of Bayesian networks used in IKE.
Cuellar, Leticia [Los Alamos National Laboratory
The thermal distribution system couples the HVAC components to the building envelope, and shares many properties of the buildings envelope including moisture, conduction and most especially air leakage performance. Duct leakage has a strong influence on air flow rates through building envelopes (usually resulting in much greater flows than those due to natural infiltration) because unbalanced duct air flows and leaks result in building pressurization and depressurization. As a tool to estimate this effect, the DeltaQ duct leakage test has been developed over the past several years as an improvement to existing duct pressurization tests. It focuses on measuring the air leakage flows to outside at operating conditions that are required for envelope infiltration impacts and energy loss calculations for duct systems. The DeltaQ test builds on the standard envelope tightness blower door measurement techniques by repeating the tests with the system air handler off and on. The DeltaQ test requires several assumptions to be made about duct leakage and its interaction with the duct system and building envelope in order to convert the blower door results into duct leakage at system operating conditions. This study examined improvements to the DeltaQ test that account for some of these assumptions using a duct system and building envelope in a test laboratory. The laboratory measurements used a purpose-built test chamber coupled to a duct system typical of forced air systems in US homes. Special duct leaks with controlled air-flow were designed and installed into an airtight duct system. This test apparatus allowed the systematic variation of the duct and envelope leakage and accurate measurement of the duct leakage flows for comparison to DeltaQ test results. This paper will discuss the laboratory test apparatus design, construction and operation, the various analysis techniques applied to the calculation procedure and present estimates of uncertainty in measured duct leakage.
Incorporation of parametric uncertainty into complex kinetic mechanisms: Application to hydrogen oxidation in supercritical water
In this study, uncertainty analysis is applied to a supercritical water hydrogen oxidation mechanism to determine the effect of uncertainties in reaction rate constants and species thermochemistry on predicted species concentrations. Forward rate constants and species thermochemistry are assumed to be the sole contributors to uncertainty in the reaction model with all other model parameters and inputs treated as deterministic quantities. Uncertainty propagation is performed using traditional Monte Carlo (MC) simulation and a new, more computationally efficient, probabilistic collocation method called the Deterministic Equivalent Modeling Method (DEMM). The results of both analyses show that there is considerable uncertainty in all predicted species concentrations. The predicted H{sub 2} and O{sub 2} concentrations vary {+-}70% from their median values. Similarly, the HO{sub 2} concentration ranges from +90 to {minus}70% of its median, while the H{sub 2}O{sub 2} concentration varies by +180 to {minus}80%. In addition, the DEMM methodology identified two key model parameters, the standard-state heat of formation of HO{sub 2} radical and the forward rate constant for H{sub 2}O{sub 2} dissociation, as the largest contributors to the uncertainty in the predicted hydrogen and oxygen species concentrations. The analyses further show that the change in model predictions due to the inclusion of real-gas effects, which are potentially important for SCWO process modeling, is small relative to the uncertainty introduced by the model parameters themselves.
Phenix, B.D.; Dinaro, J.L.; Tatang, M.A.; Tester, J.W.; Howard, J.B.; McRae, G.J. [Massachusetts Inst. of Tech., Cambridge, MA (United States). Dept. of Chemical Engineering and Energy Lab.
The European Lead-Cooled Training Reactor (ELECTRA) has been proposed as a training reactor for fast systems within the Swedish nuclear program. It is a low-power fast reactor cooled by pure liquid lead. In this work, we propagate the uncertainties in Pu-239 transport data to uncertainties in the fuel inventory of ELECTRA during the reactor life using the Total Monte Carlo approach (TMC). Within the TENDL project the nuclear models input parameters were randomized within their uncertainties and 740 Pu-239 nuclear data libraries were generated. These libraries are used as inputs to reactor codes, in our case SERPENT, to perform uncertainty analysis of nuclear reactor inventory during burn-up. The uncertainty in the inventory determines uncertainties in: the long-term radio-toxicity, the decay heat, the evolution of reactivity parameters, gas pressure and volatile fission product content. In this work, a methodology called fast TMC is utilized, which reduces the overall calculation time. The uncertainty in the ...
ostrand, H; Duan, J; Gustavsson, C; Koning, A; Pomp, S; Rochman, D; Osterlund, M
Review of uncertainty estimates associated with models for assessing the impact of breeder reactor radioactivity releases
The purpose is to summarize estimates based on currently available data of the uncertainty associated with radiological assessment models. The models being examined herein are those recommended previously for use in breeder reactor assessments. Uncertainty estimates are presented for models of atmospheric and hydrologic transport, terrestrial and aquatic food-chain bioaccumulation, and internal and external dosimetry. Both long-term and short-term release conditions are discussed. The uncertainty estimates presented in this report indicate that, for many sites, generic models and representative parameter values may be used to calculate doses from annual average radionuclide releases when these calculated doses are on the order of one-tenth or less of a relevant dose limit. For short-term, accidental releases, especially those from breeder reactors located in sites dominated by complex terrain and/or coastal meteorology, the uncertainty in the dose calculations may be much larger than an order of magnitude. As a result, it may be necessary to incorporate site-specific information into the dose calculation under these circumstances to reduce this uncertainty. However, even using site-specific information, natural variability and the uncertainties in the dose conversion factor will likely result in an overall uncertainty of greater than an order of magnitude for predictions of dose or concentration in environmental media following shortterm releases.
Miller, C.; Little, C.A.
Sensitivity and uncertainty in the effective delayed neutron fraction ({beta}{sub eff})
Precise knowledge of effective delayed neutron fraction ({beta}{sub eff}) and of the corresponding uncertainty is important for reactor safety analysis. The interest in developing the methodology for estimating the uncertainty in {beta}{sub eff} was expressed in the scope of the UAM project of the OECD/NEA. A novel approach for the calculation of the nuclear data sensitivity and uncertainty of the effective delayed neutron fraction is proposed, based on the linear perturbation theory. The method allows the detailed analysis of components of {beta}{sub eff} uncertainty. The procedure was implemented in the SUSD3D sensitivity and uncertainty code applied to several fast neutron benchmark experiments from the ICSBEP and IRPhE databases. According to the JENDL-4 covariance matrices and taking into account the uncertainty in the cross sections and in the prompt and delayed fission spectra the total uncertainty in {beta}eff was found to be of the order of {approx}2 to {approx}3.5 % for the studied fast experiments. (authors)
Kodeli, I. I. [Jozef Stefan Inst., Jamova 39, Ljubljana (Slovenia)
Reproducibility of soil moisture ensembles when representing soil parameter uncertainty using a Latin Hypercube-based approach with correlation control
[1] Representation of model input uncertainty is critical in ensemble-based data assimilation. Monte Carlo sampling of model inputs produces uncertainty in the hydrologic state through the model dynamics. Small Monte Carlo ...
Flores, Alejandro N.
Cloud-Resolving Ensemble Simulations of Mediterranean Heavy Precipitating Events: Uncertainty on Initial Conditions and Lateral Boundary Conditions
This study assesses the impact of uncertainty on convective-scale initial conditions (ICs) and the uncertainty on lateral boundary conditions (LBCs) in cloud-resolving simulations with the Application of Research to Operations at Mesoscale (AROME)...
Benoît Vié; Olivier Nuissier; Véronique Ducrocq
Uncertainty Analysis Framework - Hanford Site-Wide Groundwater Flow and Transport Model
Pacific Northwest National Laboratory (PNNL) embarked on a new initiative to strengthen the technical defensibility of the predictions being made with a site-wide groundwater flow and transport model at the U.S. Department of Energy Hanford Site in southeastern Washington State. In FY 2000, the focus of the initiative was on the characterization of major uncertainties in the current conceptual model that would affect model predictions. The long-term goals of the initiative are the development and implementation of an uncertainty estimation methodology in future assessments and analyses using the site-wide model. This report focuses on the development and implementation of an uncertainty analysis framework.
Cole, Charles R.; Bergeron, Marcel P.; Murray, Christopher J.; Thorne, Paul D.; Wurstner, Signe K.; Rogers, Phillip M.
Multiplicative scale uncertainties in the unified approach for constructing confidence intervals
We have investigated how uncertainties in the estimation of the detection efficiency affect the 90% confidence intervals in the unified approach for constructing confidence intervals. The study has been conducted for experiments where the number of detected events is large and can be described by a Gaussian probability density function. We also assume the detection efficiency has a Gaussian probability density and study the range of the relative uncertainties $\\sigma_\\epsilon$ between 0 and 30%. We find that the confidence intervals provide proper coverage and increase smoothly and continuously from the intervals that ignore scale uncertainties with a quadratic dependence on $\\sigma_\\epsilon$.
Smith, E S
Balance Calibration – A Method for Assigning a Direct-Reading Uncertainty to an Electronic Balance.
Paper Title: Balance Calibration – A method for assigning a direct-reading uncertainty to an electronic balance. Intended Audience: Those who calibrate or use electronic balances. Abstract: As a calibration facility, we provide on-site (at the customer's location) calibrations of electronic balances for customers within our company. In our experience, most of our customers are not using their balance as a comparator, but simply putting an unknown quantity on the balance and reading the displayed mass value. Manufacturer's specifications for balances typically include specifications such as readability, repeatability, linearity, and sensitivity temperature drift, but what does this all mean when the balance user simply reads the displayed mass value and accepts the reading as the true value? This paper discusses a method for assigning a direct-reading uncertainty to a balance based upon the observed calibration data and the environment where the balance is being used. The method requires input from the customer regarding the environment where the balance is used and encourages discussion with the customer regarding sources of uncertainty and possible means for improvement; the calibration process becomes an educational opportunity for the balance user as well as calibration personnel. This paper will cover the uncertainty analysis applied to the calibration weights used for the field calibration of balances; the uncertainty is calculated over the range of environmental conditions typically encountered in the field and the resulting range of air density. The temperature stability in the area of the balance is discussed with the customer and the temperature range over which the balance calibration is valid is decided upon; the decision is based upon the uncertainty needs of the customer and the desired rigor in monitoring by the customer. Once the environmental limitations are decided, the calibration is performed and the measurement data is entered into a custom spreadsheet. The spreadsheet uses measurement results, along with the manufacturer's specifications, to assign a direct-read measurement uncertainty to the balance. The fact that the assigned uncertainty is a best-case uncertainty is discussed with the customer; the assigned uncertainty contains no allowance for contributions associated with the unknown weighing sample, such as density, static charges, magnetism, etc. The attendee will learn uncertainty considerations associated with balance calibrations along with one method for assigning an uncertainty to a balance used for non-comparison measurements.
Mike Stears
Brief paper: Identification and control: Joint input design and H? state feedback with ellipsoidal parametric uncertainty via LMIs
One obstacle in connecting robust control with models generated from prediction error identification is that very few control design methods are able to directly cope with the ellipsoidal parametric uncertainty regions that are generated by such identification ... Keywords: Confidence ellipsoids, H-infinity identification, Identification for robust control, LMI optimization, Least-squares identification, Parameter uncertainty, Robust identification, Robustness to uncertainties, Uncertain linear systems
Märta Barenthin; Hĺkan Hjalmarsson
A Multimodel Study of Parametric Uncertainty in Predictions of Climate Response to Rising Greenhouse Gas Concentrations
One tool for studying uncertainties in simulations of future climate is to consider ensembles of general circulation models where parameterizations have been sampled within their physical range of plausibility. This study is about simulations ...
Benjamin M. Sanderson
Models and Uncertainty in the Policy Process: A Case Study of...
Uncertainty in the Policy Process: A Case Study of Air Quality Planning in Central California Speaker(s): James Fine Date: June 6, 2002 - 12:00pm Location: Bldg. 90 Air quality...
Appreciating Wind Energy's Probabilistic Nature within the Uncertainty Context of Electric Power System Network Planning
Electric power system network planning is influenced by the uncertainty in many parameters, such as future customer-demand/fossil-fuel-price parameter projections and new generation plant locations, which can generally be modeled in an approximate or ...
Daniel J. Burke, M. J. O'Malley
Kiwi: An Evaluated Library of Uncertainties in Nuclear Data and Package for Nuclear Sensitivity Studies
This report describes Kiwi, a program developed at Livermore to enable mature studies of the relation between imperfectly known nuclear physics and uncertainties in simulations of complicated systems. Kiwi includes a library of evaluated nuclear data uncertainties, tools for modifying data according to these uncertainties, and a simple interface for generating processed data used by transport codes. As well, Kiwi provides access to calculations of k eigenvalues for critical assemblies. This allows the user to check implications of data modifications against integral experiments for multiplying systems. Kiwi is written in python. The uncertainty library has the same format and directory structure as the native ENDL used at Livermore. Calculations for critical assemblies rely on deterministic and Monte Carlo codes developed by B division.
Pruet, J
Blending scheduling under uncertainty based on particle swarm optimization with hypothesis test
Blending is an important unit operation in process industry. As a nonlinear optimization problem with constraints, it is difficult to obtain optimal solution for blending scheduling, especially under uncertainty. As a novel evolutionary computing technique, ...
Hui Pan; Ling Wang
Uncertainty in Temperature and Precipitation Datasets over Terrestrial Regions of the Western Arctic
A better understanding of the interannual variability in temperature and precipitation datasets used as forcing fields for hydrologic models will lead to a more complete description of hydrologic model uncertainty, in turn helping scientists ...
Sheldon Drobot; James Maslanik; Ute Christina Herzfeld; Charles Fowler; Wanli Wu
Optimization under moment, robust, and data-driven models of uncertainty
We study the problem of moments and present two diverse applications that apply both the hierarchy of moment relaxation and the moment duality theory. We then propose a moment-based uncertainty model for stochastic ...
Doan, Xuan Vinh
Uncertainties in WSR-88D Measurements and Their Impacts on Monitoring Life Cycles
Radar measurement uncertainties associated with storm top, cloud top, and other height measurements are well recognized; however, the authors feel the resulting impacts on the trends of storm features are not as well documented or understood by ...
Kenneth W. Howard; Jonathan J. Gourley; Robert A. Maddox
An Explanation of Uncertainties in Point Cloudiness/Solar Energy Relationships
The uncertainties in average relationships between paint cloudiness and solar energy flux density can largely be accounted for through scatter in the overestimation of cloud shades by point cloudiness. This is demonstrated using monthly averaged ...
C. J. Stigter
Uncertainties in Global Ocean Surface Heat Flux Climatologies Derived from Ship Observations
A methodology to define uncertainties associated with ocean surface heat flux calculations has been developed and applied to a global climatology that utilizes a summary of the Comprehensive Ocean–Atmosphere Data Set surface observations. ...
Peter J. Gleckler; Bryan C. Weare
Representation, simulation and control of manufacturing process with different forms of uncertainties
This paper suggests a new methodology for effectively describing and analyzing manufacturing processes with uncertainties. Uncertain information in the form of variance and vagueness are captured using probability distribution and fuzzy logic. The captured ...
Hyunsoo Lee; Hongsuk Park; Amamath Banerjee
IAPSO Standard Seawater: Definition of the Uncertainty in the Calibration Procedure, and Stability of Recent Batches
Standard seawater (SSW) has been employed by oceanographers as a reference material in the determination of salinity for over a century. In all that time, this is the first study to determine the uncertainty of the SSW manufacturing process. SSW ...
Sheldon Bacon; Fred Culkin; Nigel Higgs; Paul Ridout
Strategic investment in power generation under uncertainty : Electric Reliability Council of Texas
The purpose of this study is to develop a strategy for investment in power generation technologies in the future given the uncertainties in climate policy and fuel prices. First, such studies are commonly conducted using ...
Chiyangwa, Diana Kudakwashe
Sampling Uncertainty and Confidence Intervals for the Brier Score and Brier Skill Score
For probability forecasts, the Brier score and Brier skill score are commonly used verification measures of forecast accuracy and skill. Using sampling theory, analytical expressions are derived to estimate their sampling uncertainties. The Brier ...
A. Allen Bradley; Stuart S. Schwartz; Tempei Hashino
An Analysis of Precipitation Variability, Persistence, and Observational Data Uncertainty in the Western United States
This paper presents an intercomparison of precipitation observations for the western United States. Using nine datasets, the authors provide a comparative climatology and season- and location-specific evaluations of precipitation uncertainty for ...
Kristen J. Guirguis; Roni Avissar
The Uncertainty in the Prediction of Flash Floods in the Northern Mediterranean Environment
Development of an operational flood forecasting system and assessment of forecast uncertainty are the principal topics of this paper. Flood forecasting procedures are developed for a Mediterranean environment. A procedure that uses the Ensemble ...
Luca Ferraris; Roberto Rudari; Franco Siccardi
An uncertainty model for a diagnostic expert system based on fuzzy algebras of strict monotonic operations
Expert knowledge in most of application domains is uncertain, incomplete and perception-based. For processing such expert knowledge an expert system should be able to represent and manipulate perception-based evaluations of uncertainties of facts and ...
Leonid Sheremetov; Ildar Batyrshin; Denis Filatov; Jorge Martínez-Muńoz
Assessing Hydrologic Impact of Climate Change with Uncertainty Estimates: Bayesian Neural Network Approach
A major challenge in assessing the hydrologic effect of climate change remains the estimation of uncertainties associated with different sources, such as the global climate models, emission scenarios, downscaling methods, and hydrologic models. ...
Mohammad Sajjad Khan; Paulin Coulibaly
A Weather and Climate Enterprise Strategic Implementation Plan for Generating and Communicating Forecast Uncertainty Information
The American Meteorological Society (AMS) Weather and Climate Enterprise Strategic Implementation Plan for Generating and Communicating Forecast Uncertainty (the Plan) is summarized. The Plan (available on the AMS website at www.ametsoc.org/boardpges/cwce/...
Paul A. Hirschberg; Elliot Abrams; Andrea Bleistein; William Bua; Luca Delle Monache; Thomas W. Dulong; John E. Gaynor; Bob Glahn; Thomas M. Hamill; James A. Hansen; Douglas C. Hilderbrand; Ross N. Hoffman; Betty Hearn Morrow; Brenda Philips; John Sokich; Neil Stuart
Robust Bayesian Uncertainty Analysis of Climate System Properties Using Markov Chain Monte Carlo Methods
A Bayesian uncertainty analysis of 12 parameters of the Bern2.5D climate model is presented. This includes an extensive sensitivity study with respect to the major statistical assumptions. Special attention is given to the parameter representing ...
Lorenzo Tomassini; Peter Reichert; Reto Knutti; Thomas F. Stocker; Mark E. Borsuk
Flood Risk, Uncertainty, and Scientific Information for Decision Making: Lessons from an Interdisciplinary Project
The magnitude of flood damage in the United States, combined with the uncertainty in current estimates of flood risk, suggest that society could benefit from improved scientific information about flood risk. To help address this perceived need, a ...
Rebecca E. Morss; Olga V. Wilhelmi; Mary W. Downton; Eve Gruntfest
Investment Timing and Capacity Choice for Small-Scale Wind Power Under Uncertainty
REFERENCES [1] American Wind Power Association (AWEA), Road-CHOICE FOR SMALL-SCALE WIND POWER UNDER UNCERTAINTY Stein-Power production from wind power has stochastic inflows, and
Fleten, Stein-Erik; Maribu, Karl Magnus
Impact of Surface Parameter Uncertainties within the Canadian Regional Ensemble Prediction System
The aim of this study is to assess the impact of uncertainties in surface parameter and initial conditions on numerical prediction with the Canadian Regional Ensemble Prediction System (REPS). As part of this study, the Canadian version of the ...
Christophe Lavaysse; Marco Carrera; Stéphane Bélair; Normand Gagnon; Ronald Frenette; Martin Charron; M. K. Yau
Monitoring Drought Conditions and Their Uncertainties in Africa Using TRMM Data
The main objective of this study is to evaluate the uncertainties due to sample size associated with the estimation of the standardized precipitation index (SPI) and their impact on the level of confidence in drought monitoring in Africa using ...
G. Naumann; P. Barbosa; H. Carrao; A. Singleton; J. Vogt
Uncertainty in Climate Change Projections of the Hadley Circulation: The Role of Internal Variability
The uncertainty arising from internal climate variability in climate change projections of the Hadley circulation (HC) is presently unknown. In this paper it is quantified by analyzing a 40-member ensemble of integrations of the Community Climate ...
Sarah M. Kang; Clara Deser; Lorenzo M. Polvani
Designing for uncertainty : novel shapes and behaviors using scissor-pair transformable structures
Much current research in design and computation, within an architectural framework, aims to reduce uncertainty as much as possible. The general belief is that, during the conceptual design process, the certainty about the ...
Rosenberg, Daniel, S.M. Massachusetts Institute of Technology
Uncertainties in Climatological Tropical Humidity Profiles: Some Implications for Estimating the Greenhouse Effect
The vertical profile of water vapor, the principal infrared-absorbing gas in the atmosphere, is an important factor in determining the energy balance of the climate system. This study examines uncertainties in calculating a climatologicai ...
Dayid S. Gutzler
Model Uncertainty in a Mesoscale Ensemble Prediction System: Stochastic versus Multiphysics Representations
A multiphysics and a stochastic kinetic-energy backscatter scheme are employed to represent model uncertainty in a mesoscale ensemble prediction system using the Weather Research and Forecasting model. Both model-error schemes lead to significant ...
J. Berner; S.-Y. Ha; J. P. Hacker; A. Fournier; C. Snyder
Robust optimization for network-based resource allocation problems under uncertainty
We consider large-scale, network-based, resource allocation problems under uncertainty, with specific focus on the class of problems referred to as multi-commodity flow problems with time-windows. These problems are at the ...
Marla, Lavanya
Misinterpretations of the "Cone of Uncertainty" in Florida during the 2004 Hurricane Season
This article reviews the evolution, communication, and differing interpretations of the National Hurricane Center's "cone of uncertainty" hurricane forecast graphic. It concludes with a discussion of this graphic from the perspective of risk ...
Kenneth Broad; Anthony Leiserowitz; Jessica Weinkle; Marissa Steketee
Implications of Parameter Uncertainty on Soil Moisture Drought Analysis in Germany
Simulated soil moisture is increasingly used to characterize agricultural droughts but its parametric uncertainty, which essentially affects all hydrological fluxes and state variables, is rarely considered for identifying major drought events. In ...
Luis Samaniego; Rohini Kumar; Matthias Zink
Uncertainty Propagation of Regional Climate Model Precipitation Forecasts to Hydrologic Impact Assessment
A Monte Carlo framework is adopted for propagating uncertainty in dynamically downscaled seasonal forecasts of area-averaged daily precipitation to associated streamflow response calculations. Daily precipitation is modeled as a mixture of two ...
Phaedon C. Kyriakidis; Norman L. Miller; Jinwon Kim
Mixtures of Gaussians for Uncertainty Description in Bivariate Latent Heat Flux Proxies
This paper proposes a new probabilistic approach for describing uncertainty in the ensembles of latent heat flux proxies. The proxies are obtained from hourly Bowen ratio and satellite-derived measurements, respectively, at several locations in ...
R. Wójcik; Peter A. Troch; H. Stricker; P. Torfs; E. Wood; H. Su; Z. Su
Influence of air quality model resolution on uncertainty associated with health impacts
We use regional air quality modeling to evaluate the impact of model resolution on uncertainty associated with the human health benefits resulting from proposed air quality regulations. Using a regional photochemical model ...
Thompson, Tammy M.
How 'Good' are the Best Tracks? - Estimating Uncertainty in the Atlantic Hurricane Database
"Best tracks" are National Hurricane Center (NHC) post-storm analyses of the intensity, central pressure, position, and size of Atlantic and eastern North Pacific basin tropical and subtropical cyclones. This paper estimates the uncertainty (...
Christopher W. Landsea; James L. Franklin
Assessing early investments in low carbon technologies under uncertainty : the case of Carbon Capture and Storage
Climate change is a threat that could be mitigated by introducing new energy technologies into the electricity market that emit fewer greenhouse gas (GHG) emissions. We face many uncertainties that would affect the demand ...
Ereira, Eleanor Charlotte
Reanalyses-Based Tropospheric Temperature Estimates: Uncertainties in the Context of Global Climate Change Detection
Uncertainties in estimates of tropospheric mean temperature were investigated in the context of climate change detection through comparisons of the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) ...
Muthuvel Chelliah; C. F. Ropelewski
GRACE Mission Design: Impact of Uncertainties in Disturbance Environment and Satellite Force Models
The Gravity Recovery and Climate Experiment (GRACE) primary mission will be performed by making measurements of the inter-satellite range change between two co-planar, low altitude, near-polar orbiting satellites. Understanding the uncertainties in the ...
Mazanek Daniel D.; Kumar Renjith R.; Seywald Hans; Qu Min
Managing uncertainty in systems with a Valuation Approach for Strategic Changeability
Complex engineering systems are frequently exposed to large amounts of uncertainty, as many exogenous, uncontrollable conditions change over time and can affect the performance and value delivery of a system. Engineering ...
Fitzgerald, Matthew Edward
Predictability of Extratropical Cyclones: The Influence of Initial Condition and Model Uncertainties
Errors in numerical weather forecasts can be attributed to two causes: deficiencies in the modeling system and inaccurate initial conditions. Understanding of the characteristics of the growth of forecast spread related to model uncertainty is ...
Hongyan Zhu; Alan Thorpe
Verification of Ensemble-Based Uncertainty Circles around Tropical Cyclone Track Forecasts
Several tropical cyclone forecasting centers issue uncertainty information with regard to their official track forecasts, generally using the climatological distribution of position error. However, such methods are not able to convey information ...
Thierry Dupont; Matthieu Plu; Philippe Caroff; Ghislain Faure
Uncertainties in Achieving Energy Savings from HVAC Maintenance Measures in the Field
Uncertainties in Achieving Energy Savings from HVAC Maintenance Measures in the Field Kristin Group, Davis, CA, USA 4 Southern California Edison, Irwindale, CA, USA ABSTRACT HVAC maintenance utilities across the nation to include HVAC maintenance measures in energy efficiency programs
California at Davis, University of
Uncertainty in climate change projections of the Hadley circulation: the role of internal variability
Effects of the Uncertainty about Global Economic Recovery on Energy Transition and CO2 Price
This paper examines the impact that uncertainty over economic growth may have on global energy transition and CO2 prices. We use a general-equilibrium model derived from MERGE, and define several stochastic scenarios for ...
Durand-Lasserve, Olivier
Direct Aerosol Radiative Forcing Uncertainty Based on a Radiative Perturbation Analysis
To provide a lower bound for the uncertainty in measurement-based clear- and all-sky direct aerosol radiative forcing (DARF), a radiative perturbation analysis is performed for the ideal case in which the perturbations in global mean aerosol ...
Norman G. Loeb; Wenying Su
Numerical Uncertainties in Simulation of Reversible Isentropic Processes and Entropy Conservation: Part II
The objectives of this study are 1) to provide the framework for an in-depth statistical analysis of the numerical uncertainties in the simulation of conservation of entropy, potential vorticity, and like properties under appropriate modeling ...
Donald R. Johnson; Allen J. Lenzen; Tom H. Zapotocny; Todd K. Schaack
The Impact of Initial Condition Uncertainty on Numerical Simulations of Blocking
The impact of initial condition uncertainty (ICU) on the onset and maintenance of eastern North Pacific blocking is examined within the framework of a general circulation model (GCM) and the perfect model assumption. Comparisons are made with the ...
Paul A. Nutter; Steven L. Mullen; David P. Baumhefner
System level assessment of uncertainty in aviation environmental policy impact analysis
This thesis demonstrates the assessment of uncertainty of a simulation model at the system level, which takes into account the interaction between the modules that comprise the system. Results from this system level ...
Liem, Rhea Patricia
Uncertainty of Boundary Layer Heat Budgets Computed from Wind Profiler—RASS Networks
Uncertainties in the evaluation of the atmospheric heat budget, in which the turbulent heat flux divergence term is calculated as a residual, are investigated for a triangular array of 915-MHz wind profilers—radio acoustic sounding systems (RASS) ...
Markus Furger; C. David Whiteman; James M. Wilczak
Re-estimation of Nuclear Data and JEFF 3.1.1 Uncertainty Calculations
This paper describes the method to define relevant targeted integral measurements that allow the improvement of nuclear data evaluations and the determination of corresponding reliable covariances. {sup 235}U and {sup 56}Fe examples are pointed out for the improvement of JEFF3 data. Utilizations of these covariances are shown for Sensitivity and Representativity studies, Uncertainty calculations, and Transposition of experimental results to industrial applications. S/U studies are more and more used in Reactor Physics and Safety-Criticality. However, the reliability of study results relies strongly on the ND covariance relevancy. Our method derives the real uncertainty associated with each evaluation from calibration on targeted integral measurements. These realistic covariance matrices allow reliable JEFF3.1.1 calculation of prior uncertainty due to nuclear data, as well as uncertainty reduction based on representative integral experiments, in challenging design calculations such as GEN3 and RJH reactors.
Santamarina, A. [Commissariat a l'Energie Atomique et aux Energies Alternatives; Bernard, D. [Commissariat a l'Energie Atomique et aux Energies Alternatives; Dos Santos, N. [Commissariat a l'Energie Atomique et aux Energies Alternatives; Leray, O. [Commissariat a l'Energie Atomique et aux Energies Alternatives; Vaglio, C. [Commissariat a l'Energie Atomique et aux Energies Alternatives; Leal, Luiz C [ORNL
A Review of Uncertainties in Global Temperature Projections over the Twenty-First Century
Quantification of the uncertainties in future climate projections is crucial for the implementation of climate policies. Here a review of projections of global temperature change over the twenty-first century is provided for the six illustrative ...
R. Knutti; M. R. Allen; P. Friedlingstein; J. M. Gregory; G. C. Hegerl; G. A. Meehl; M. Meinshausen; J. M. Murphy; G.-K. Plattner; S. C. B. Raper; T. F. Stocker; P. A. Stott; H. Teng; T. M. L. Wigley
Using NASA Remote Sensing Data to Reduce Uncertainty of Land-Use Transitions in
NASA Remote Sensing Data to Reduce Uncertainty of Land-Use Transitions in NASA Remote Sensing Data to Reduce Uncertainty of Land-Use Transitions in Global Carbon-Climate Models: Data Management Plan L. Chini, G.C. Hurtt, M. Hansen, and P. Potapov Department of Geography University of Maryland The following Data Management Plan was part of the NASA ROSES 2012 Proposal Using NASA Remote Sensing Data to Reduce Uncertainty of Land-Use Transitions in Global Carbon- Climate Models (summary) submitted to the Terrestrial Ecology Program. It is presented as an example plan. Data Management Plan The proposed project will generate important new datasets of remote-sensing-based land-use transitions and their inherent uncertainty. Our plan for managing these datasets includes quality assessment, long-term archiving, and data sharing and dissemination (along with documentation
Data uncertainty impact in radiotoxicity evaluation connected to EFR and IRF systems
Time-dependent sensitivity techniques, which have been used in the past for standard reactor applications, have been adapted to calculate the impact of data uncertainties in radiotoxicity evaluations. The methodology has been applied to different strategies of radioactive waste management connected with the EFR and IFR reactor fuel cycles. Results are provided in terms of sensitivity coefficients to basic data (cross sections and decay constants), and uncertainties on global radiotoxicity at different times of storing after discharge.
Palmiotti, G.; Salvatores, M. [CEA Centre d`Etudes Nucleaires de Cadarache, 13 - Saint-Paul-lez-Durance (France). Direction des Reacteurs Nucleaires; Hill, R.N. [Argonne National Lab., IL (United States)
Review of BEIS3 Formulation and Consequences Relative to Air Quality Standards: Estimation of Uncertainties in BEIS3 Emission Output s
This report describes estimates of uncertainties for outputs of the Biogenics Emissions Inventory System, Version 3 (BEIS3) model due to uncertainties in model parameters and input variables.
Explicitly integrating parameter, input, and structure uncertainties into Bayesian Neural Networks for probabilistic hydrologic forecasting
Estimating uncertainty of hydrologic forecasting is valuable to water resources and other relevant decision making processes. Recently, Bayesian Neural Networks (BNNs) have been proved powerful tools for quantifying uncertainty of streamflow forecasting. In this study, we propose a Markov Chain Monte Carlo (MCMC) framework to incorporate the uncertainties associated with input, model structure, and parameter into BNNs. This framework allows the structure of the neural networks to change by removing or adding connections between neurons and enables scaling of input data by using rainfall multipliers. The results show that the new BNNs outperform the BNNs that only consider uncertainties associated with parameter and model structure. Critical evaluation of posterior distribution of neural network weights, number of effective connections, rainfall multipliers, and hyper-parameters show that the assumptions held in our BNNs are not well supported. Further understanding of characteristics of different uncertainty sources and including output error into the MCMC framework are expected to enhance the application of neural networks for uncertainty analysis of hydrologic forecasting.
Zhang, Xuesong; Liang, Faming; Yu, Beibei; Zong, Ziliang
Nuclear data uncertainty propagation in a lattice physics code using stochastic sampling
A methodology is presented for 'black box' nuclear data uncertainty propagation in a lattice physics code using stochastic sampling. The methodology has 4 components: i) processing nuclear data variance/covariance matrices including converting the native group structure to a group structure 'compatible' with the lattice physics code, ii) generating (relative) random samples of nuclear data, iii) perturbing the lattice physics code nuclear data according to the random samples, and iv) analyzing the distribution of outputs to estimate the uncertainty. The scheme is described as implemented at PSI, in a modified version of the lattice physics code CASMO-5M, including all relevant practical details. Uncertainty results are presented for a BWR pin-cell at hot zero power conditions and a PWR assembly at hot full power conditions with depletion. Results are presented for uncertainties in eigenvalue, 1-group microscopic cross sections, 2-group macroscopic cross sections, and isotopics. Interesting behavior is observed with burnup, including a minimum uncertainty due to the presence of fertile U-238 and a global effect described as 'synergy', observed when comparing the uncertainty resulting from simultaneous and one-at-a-time variations of nuclear data. (authors)
Wieselquist, W.; Vasiliev, A.; Ferroukhi, H. [Paul Scherrer Institut, 5232 Villigen (Switzerland)
The IAEA Coordinated Research Program on HTGR Reactor Physics, Thermal-hydraulics and Depletion Uncertainty Analysis: Description of the Benchmark Test Cases and Phases
The continued development of High Temperature Gas Cooled Reactors (HTGRs) requires verification of design and safety features with reliable high fidelity physics models and robust, efficient, and accurate codes. The uncertainties in the HTR analysis tools are today typically assessed with sensitivity analysis and then a few important input uncertainties (typically based on a PIRT process) are varied in the analysis to find a spread in the parameter of importance. However, one wish to apply a more fundamental approach to determine the predictive capability and accuracies of coupled neutronics/thermal-hydraulics and depletion simulations used for reactor design and safety assessment. Today there is a broader acceptance of the use of uncertainty analysis even in safety studies and it has been accepted by regulators in some cases to replace the traditional conservative analysis. Finally, there is also a renewed focus in supplying reliable covariance data (nuclear data uncertainties) that can then be used in uncertainty methods. Uncertainty and sensitivity studies are therefore becoming an essential component of any significant effort in data and simulation improvement. In order to address uncertainty in analysis and methods in the HTGR community the IAEA launched a Coordinated Research Project (CRP) on the HTGR Uncertainty Analysis in Modelling early in 2012. The project is built on the experience of the OECD/NEA Light Water Reactor (LWR) Uncertainty Analysis in Best-Estimate Modelling (UAM) benchmark activity, but focuses specifically on the peculiarities of HTGR designs and its simulation requirements. Two benchmark problems were defined with the prismatic type design represented by the MHTGR-350 design from General Atomics (GA) while a 250 MW modular pebble bed design, similar to the INET (China) and indirect-cycle PBMR (South Africa) designs are also included. In the paper more detail on the benchmark cases, the different specific phases and tasks and the latest status and plans are presented.
Frederik Reitsma; Gerhard Strydom; Bismark Tyobeka; Kostadin Ivanov
State-of-the-Art Solar Simulator Reduces Measurement Time and Uncertainty (Fact Sheet)
One-Sun Multisource Solar Simulator (OSMSS) brings accurate energy-rating predictions that account for the nonlinear behavior of multijunction photovoltaic devices. The National Renewable Energy Laboratory (NREL) is one of only a few International Organization for Standardization (ISO)-accredited calibration labs in the world for primary and secondary reference cells and modules. As such, it is critical to seek new horizons in developing simulators and measurement methods. Current solar simulators are not well suited for accurately measuring multijunction devices. To set the electrical current to each junction independently, simulators must precisely tune the spectral content with no overlap between the wavelength regions. Current simulators do not have this capability, and the overlaps lead to large measurement uncertainties of {+-}6%. In collaboration with LabSphere, NREL scientists have designed and implemented the One-Sun Multisource Solar Simulator (OSMSS), which enables automatic spectral adjustment with nine independent wavelength regions. This fiber-optic simulator allows researchers and developers to set the current to each junction independently, reducing errors relating to spectral effects. NREL also developed proprietary software that allows this fully automated simulator to rapidly 'build' a spectrum under which all junctions of a multijunction device are current matched and behave as they would under a reference spectrum. The OSMSS will reduce the measurement uncertainty for multijunction devices, while significantly reducing the current-voltage measurement time from several days to minutes. These features will enable highly accurate energy-rating predictions that take into account the nonlinear behavior of multijunction photovoltaic devices.
Not Available
Stochastic modelling of landfill leachate and biogas production incorporating waste heterogeneity. Model formulation and uncertainty analysis
A mathematical model simulating the hydrological and biochemical processes occurring in landfilled waste is presented and demonstrated. The model combines biochemical and hydrological models into an integrated representation of the landfill environment. Waste decomposition is modelled using traditional biochemical waste decomposition pathways combined with a simplified methodology for representing the rate of decomposition. Water flow through the waste is represented using a statistical velocity model capable of representing the effects of waste heterogeneity on leachate flow through the waste. Given the limitations in data capture from landfill sites, significant emphasis is placed on improving parameter identification and reducing parameter requirements. A sensitivity analysis is performed, highlighting the model's response to changes in input variables. A model test run is also presented, demonstrating the model capabilities. A parameter perturbation model sensitivity analysis was also performed. This has been able to show that although the model is sensitive to certain key parameters, its overall intuitive response provides a good basis for making reasonable predictions of the future state of the landfill system. Finally, due to the high uncertainty associated with landfill data, a tool for handling input data uncertainty is incorporated in the model's structure. It is concluded that the model can be used as a reasonable tool for modelling landfill processes and that further work should be undertaken to assess the model's performance.
Zacharof, A.I.; Butler, A.P
DAKOTA : a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis.
The DAKOTA (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. DAKOTA contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the DAKOTA toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a theoretical manual for selected algorithms implemented within the DAKOTA software. It is not intended as a comprehensive theoretical treatment, since a number of existing texts cover general optimization theory, statistical analysis, and other introductory topics. Rather, this manual is intended to summarize a set of DAKOTA-related research publications in the areas of surrogate-based optimization, uncertainty quantification, and optimization under uncertainty that provide the foundation for many of DAKOTA's iterative analysis capabilities.
Eldred, Michael Scott; Vigil, Dena M.; Dalbey, Keith R.; Bohnhoff, William J.; Adams, Brian M.; Swiler, Laura Painton; Lefantzi, Sophia (Sandia National Laboratories, Livermore, CA); Hough, Patricia Diane (Sandia National Laboratories, Livermore, CA); Eddy, John P.
Report on INL Activities for Uncertainty Reduction Analysis of FY12
The work scope of this project related to the Work Packages of "Uncertainty Reduction Analyses" with the goal of reducing nuclear data uncertainties is to produce a set of improved nuclear data to be used both for a wide range of validated advanced fast reactor design calculations, and for providing guidelines for further improvements of the ENDF/B files (i.e. ENDF/B-VII, and future releases). Recent extensive sensitivity/uncertainty studies, performed within an international OECD-NEA initiative, have quantified for the first time the impact of current nuclear data uncertainties on design parameters of the major FCR&D and GEN-IV systems, and in particular on Na-cooled fast reactors with different fuels (oxide or metal), fuel composition (e.g. different Pu/TRU ratios) and different conversion ratios. These studies have pointed out that present uncertainties on the nuclear data should be significantly reduced, in order to get full benefit from the advanced modeling and simulation initiatives. Nuclear data plays a fundamental role in performance calculations of advanced reactor concepts. Uncertainties in the nuclear data propagate into uncertainties in calculated integral quantities, driving margins and costs in advanced system design, operation and safeguards. This package contributes to the resolution of technical, cost, safety, security and proliferation concerns in a multi-pronged, systematic, science-based R&D approach. The Nuclear Data effort identifies and develops small scale, phenomenon-specific experiments informed by theory and engineering to reduce the number of large, expensive integral experiments. The Nuclear Data activities are leveraged by effective collaborations between experiment and theory, between DOE programs and offices, at national laboratories and universities, both domestic and international. The primary objective is to develop reactor core sensitivity and uncertainty analyses that identify the improvement needs of key nuclear data which would facilitate fast spectrum system optimization and assure safety performance. The inclusion of fast spectrum integral experiment data is key to minimizing the impact of nuclear data uncertainties on reactor core performance calculations, thus providing the best nuclear data needs assessment. This report presents the status of activities performed at INL under the ARC Work Package previously mentioned. As major achievement this year a comprehensive adjustment, including 87 experiments, was carried out. The results of this adjustment provide useful insights and helpful feedback to both nuclear data evaluation and measurer communities. In the following, we will document first the theory that underlines the adjustment methodology, and then we will illustrate the sensitivity coefficient computation and the nuclear data and experiment selection. Subsequently, the adjustment results will be shown, and, finally, conclusions, including future work, will be provided.
G. Palmiotti; M. Salvatores
Accounting for uncertainty in systematic bias in exposure estimates used in relative risk regression
In many epidemiologic studies addressing exposure-response relationships, sources of error that lead to systematic bias in exposure measurements are known to be present, but there is uncertainty in the magnitude and nature of the bias. Two approaches that allow this uncertainty to be reflected in confidence limits and other statistical inferences were developed, and are applicable to both cohort and case-control studies. The first approach is based on a numerical approximation to the likelihood ratio statistic, and the second uses computer simulations based on the score statistic. These approaches were applied to data from a cohort study of workers at the Hanford site (1944-86) exposed occupationally to external radiation; to combined data on workers exposed at Hanford, Oak Ridge National Laboratory, and Rocky Flats Weapons plant; and to artificial data sets created to examine the effects of varying sample size and the magnitude of the risk estimate. For the worker data, sampling uncertainty dominated and accounting for uncertainty in systematic bias did not greatly modify confidence limits. However, with increased sample size, accounting for these uncertainties became more important, and is recommended when there is interest in comparing or combining results from different studies.
Gilbert, E.S.
The objective of this paper is to assess proposed transuranic waste accident analysis guidance and recent software improvements in a Windows-OS version of MACCS2 that allows the inputting of parameter uncertainty. With this guidance and code capability, there is the potential to perform a quantitative uncertainty assessment of unmitigated accident releases with respect to the 25 rem Evaluation Guideline (EG) of DOE-STD-3009-94 CN3 (STD-3009). Historically, the classification of safety systems in a U.S. Department of Energy (DOE) nuclear facility's safety basis has involved how subject matter experts qualitatively view uncertainty in the STD-3009 Appendix A accident analysis methodology. Specifically, whether consequence uncertainty could be larger than previously evaluated so the site-specific accident consequences may challenge the EG. This paper assesses whether a potential uncertainty capability for MACCS2 could provide a stronger technical basis as to when the consequences from a design basis accident (DBA) truly challenges the 25 rem EG.
Palmrose, D E; Yang, J M
Optimization under Uncertainty for Water Consumption in a Pulverized Coal Power Plant
Pulverized coal (PC) power plants are widely recognized as major water consumers whose operability has started to be affected by drought conditions across some regions of the country. Water availability will further restrict the retrofitting of existing PC plants with water-expensive carbon capture technologies. Therefore, national efforts to reduce water withdrawal and consumption have been intensified. Water consumption in PC plants is strongly associated to losses from the cooling water cycle, particularly water evaporation from cooling towers. Accurate estimation of these water losses requires realistic cooling tower models, as well as the inclusion of uncertainties arising from atmospheric conditions. In this work, the cooling tower for a supercritical PC power plant was modeled as a humidification operation and used for optimization under uncertainty. Characterization of the uncertainty (air temperature and humidity) was based on available weather data. Process characteristics including boiler conditions, reactant ratios, and pressure ratios in turbines were calculated to obtain the minimum water consumption under the above mentioned uncertainties. In this study, the calculated conditions predicted up to 12% in reduction in the average water consumption for a 548 MW supercritical PC power plant simulated using Aspen Plus. Optimization under uncertainty for these large-scale PC plants cannot be solved with conventional stochastic programming algorithms because of the computational expenses involved. In this work, we discuss the use of a novel better optimization of nonlinear uncertain systems (BONUS) algorithm which dramatically decreases the computational requirements of the stochastic optimization.
Juan M. Salazar; Stephen E. Zitney; Urmila Diwekar
Optimization Under Uncertainty for Water Consumption in a Pulverized Coal Power Plant
Juan M. Salazara; Stephen E. Zitney; Urmila M. Diwekara
The method of belief scales as a means for dealing with uncertainty in tough regulatory decisions.
Modeling and simulation is playing an increasing role in supporting tough regulatory decisions, which are typically characterized by variabilities and uncertainties in the scenarios, input conditions, failure criteria, model parameters, and even model form. Variability exists when there is a statistically significant database that is fully relevant to the application. Uncertainty, on the other hand, is characterized by some degree of ignorance. A simple algebraic problem was used to illustrate how various risk methodologies address variability and uncertainty in a regulatory context. These traditional risk methodologies include probabilistic methods (including frequensic and Bayesian perspectives) and second-order methods where variabilities and uncertainties are treated separately. Representing uncertainties with (subjective) probability distributions and using probabilistic methods to propagate subjective distributions can lead to results that are not logically consistent with available knowledge and that may not be conservative. The Method of Belief Scales (MBS) is developed as a means to logically aggregate uncertain input information and to propagate that information through the model to a set of results that are scrutable, easily interpretable by the nonexpert, and logically consistent with the available input information. The MBS, particularly in conjunction with sensitivity analyses, has the potential to be more computationally efficient than other risk methodologies. The regulatory language must be tailored to the specific risk methodology if ambiguity and conflict are to be avoided.
Uncertainty Analysis for a Virtual Flow Meter Using an Air-Handling Unit Chilled Water Valve
A virtual water flow meter is developed that uses the chilled water control valve on an air-handling unit as a measurement device. The flow rate of water through the valve is calculated using the differential pressure across the valve and its associated coil, the valve command, and an empirically determined valve characteristic curve. Thus, the probability of error in the measurements is significantly greater than for conventionally manufactured flow meters. In this paper, mathematical models are developed and used to conduct uncertainty analysis for the virtual flow meter, and the results from the virtual meter are compared to measurements made with an ultrasonic flow meter. Theoretical uncertainty analysis shows that the total uncertainty in flow rates from the virtual flow meter is 1.46% with 95% confidence; comparison of virtual flow meter results with measurements from an ultrasonic flow meter yielded anuncertainty of 1.46% with 99% confidence. The comparable results from the theoretical uncertainty analysis and empirical comparison with the ultrasonic flow meter corroborate each other, and tend to validate the approach to computationally estimating uncertainty for virtual sensors introduced in this study.
Song, Li; Wang, Gang; Brambley, Michael R.
Theoretical Uncertainties in the Subgiant--Mass Age Relation and the Absolute Age of Omega Cen
The theoretical uncertainties in the calibration of the relationship between the subgiant mass and age in metal-poor stars are investigated using a Monte Carlo approach. Assuming that the mass and iron abundance of a subgiant star are known exactly, uncertainties in the input physics used to construct stellar evolution models and isochrones lead to a Gaussian 1-sigma uncertainty of +/-2.9% in the derived ages. The theoretical error budget is dominated by the uncertainties in the calculated opacities. Observations of detached double lined eclipsing binary OGLEGC-17 in the globular cluster Omega Cen have found that the primary is on the subgiant branch with a mass of M = 0.809+/-0.012 M_sun and [Fe/H]= -2.29+/-0.15 (Kaluzny et al. 2001). Combining the theoretical uncertainties with the observational errors leads to an age for OGLEGC-17 of 11.10+/-0.67 Gyr. The one-sided, 95% lower limit to the age of OGLEGC-17 is 10.06 Gyr, while the one-sided, 95% upper limit is 12.27 Gyr.
Brian Chaboyer; Lawrence M. Krauss
Survey of sampling-based methods for uncertainty and sensitivity analysis.
Sampling-based methods for uncertainty and sensitivity analysis are reviewed. The following topics are considered: (1) Definition of probability distributions to characterize epistemic uncertainty in analysis inputs, (2) Generation of samples from uncertain analysis inputs, (3) Propagation of sampled inputs through an analysis, (4) Presentation of uncertainty analysis results, and (5) Determination of sensitivity analysis results. Special attention is given to the determination of sensitivity analysis results, with brief descriptions and illustrations given for the following procedures/techniques: examination of scatterplots, correlation analysis, regression analysis, partial correlation analysis, rank transformations, statistical tests for patterns based on gridding, entropy tests for patterns based on gridding, nonparametric regression analysis, squared rank differences/rank correlation coefficient test, two dimensional Kolmogorov-Smirnov test, tests for patterns based on distance measures, top down coefficient of concordance, and variance decomposition.
Johnson, Jay Dean; Helton, Jon Craig; Sallaberry, Cedric J. PhD. (.; .); Storlie, Curt B. (Colorado State University, Fort Collins, CO)
Uncertainty and Sensitivity Analyses Plan. Draft for Peer Review: Hanford Environmental Dose Reconstruction Project
Hanford Environmental Dose Reconstruction (HEDR) Project staff are developing mathematical models to be used to estimate the radiation dose that individuals may have received as a result of emissions since 1944 from the US Department of Energy`s (DOE) Hanford Site near Richland, Washington. An uncertainty and sensitivity analyses plan is essential to understand and interpret the predictions from these mathematical models. This is especially true in the case of the HEDR models where the values of many parameters are unknown. This plan gives a thorough documentation of the uncertainty and hierarchical sensitivity analysis methods recommended for use on all HEDR mathematical models. The documentation includes both technical definitions and examples. In addition, an extensive demonstration of the uncertainty and sensitivity analysis process is provided using actual results from the Hanford Environmental Dose Reconstruction Integrated Codes (HEDRIC). This demonstration shows how the approaches used in the recommended plan can be adapted for all dose predictions in the HEDR Project.
Simpson, J.C.; Ramsdell, J.V. Jr.
Influence of Nuclear Fuel Cycles on Uncertainty of Long Term Performance of
Influence of Nuclear Fuel Cycles on Uncertainty of Long Term Influence of Nuclear Fuel Cycles on Uncertainty of Long Term Performance of Geologic Disposal Systems Influence of Nuclear Fuel Cycles on Uncertainty of Long Term Performance of Geologic Disposal Systems Development and implementation of future advanced fuel cycles including those that recycle fuel materials, use advanced fuels different from current fuels, or partition and transmute actinide radionuclides, will impact the waste management system. The Used Fuel Disposition Campaign can reasonably conclude that advanced fuel cycles, in combination with partitioning and transmutation, which remove actinides, will not materially alter the performance, the spread in dose results around the mean, the modeling effort to include significant features, events, and processes
2012 CERTS R&M Peer Review - Summary: Transmission Investment Under Uncertainty - Ben Hobbs
Investment Assessment Under Uncertainty about Fuel Prices, Investment Assessment Under Uncertainty about Fuel Prices, Technology, Renewables Penetration and Market Responses using a Multi-Stage Stochastic Model Approach with Recourse (Year 2) Project Lead: Hobbs Co-investigators: Schuler, Zimmerman July 20, 2012 1. Project objective To develop and apply a methodology for evaluating the impact of market, technology, and policy uncertainties upon transmission planning on a regional and multi-decadal time scale. The methodology will integrate transmission capacity expansion decisions and OPF methodologies in a decomposition scheme in order to rigorously capture operational constraints such as security constraints, ramp limitations, and transmission flow limits, as well as longer term investment issues. The methodology would address questions such
It is widely recognized that the results of safety assessment calculations provide an important contribution to the safety arguments for a disposal facility, but cannot in themselves adequately demonstrate the safety of the disposal system. The safety assessment and a broader range of arguments and activities need to be considered holistically to justify radioactive waste disposal at any particular site. Many programs are therefore moving towards the production of what has become known as a Safety Case, which includes all of the different activities that are conducted to demonstrate the safety of a disposal concept. Recognizing the growing interest in the concept of a Safety Case, the International Atomic Energy Agency (IAEA) is undertaking an intercomparison and harmonization project called PRISM (Practical Illustration and use of the Safety Case Concept in the Management of Near-surface Disposal). The PRISM project is organized into four Task Groups that address key aspects of the Safety Case concept: Task Group 1 - Understanding the Safety Case; Task Group 2 - Disposal facility design; Task Group 3 - Managing waste acceptance; and Task Group 4 - Managing uncertainty. This paper addresses the work of Task Group 4, which is investigating approaches for managing the uncertainties associated with near-surface disposal of radioactive waste and their consideration in the context of the Safety Case. Emphasis is placed on identifying a wide variety of approaches that can and have been used to manage different types of uncertainties, especially non-quantitative approaches that have not received as much attention in previous IAEA projects. This paper includes discussions of the current results of work on the task on managing uncertainty, including: the different circumstances being considered, the sources/types of uncertainties being addressed and some initial proposals for approaches that can be used to manage different types of uncertainties.
Seitz, R.
Uncertainty Quantification of Composite Laminate Damage with the Generalized Information Theory
This work presents a survey of five theories to assess the uncertainty of projectile impact induced damage on multi-layered carbon-epoxy composite plates. Because the types of uncertainty dealt with in this application are multiple (variability, ambiguity, and conflict) and because the data sets collected are sparse, characterizing the amount of delamination damage with probability theory alone is possible but incomplete. This motivates the exploration of methods contained within a broad Generalized Information Theory (GIT) that rely on less restrictive assumptions than probability theory. Probability, fuzzy sets, possibility, and imprecise probability (probability boxes (p-boxes) and Dempster-Shafer) are used to assess the uncertainty in composite plate damage. Furthermore, this work highlights the usefulness of each theory. The purpose of the study is not to compare directly the different GIT methods but to show that they can be deployed on a practical application and to compare the assumptions upon which these theories are based. The data sets consist of experimental measurements and finite element predictions of the amount of delamination and fiber splitting damage as multilayered composite plates are impacted by a projectile at various velocities. The physical experiments consist of using a gas gun to impact suspended plates with a projectile accelerated to prescribed velocities, then, taking ultrasound images of the resulting delamination. The nonlinear, multiple length-scale numerical simulations couple local crack propagation implemented through cohesive zone modeling to global stress-displacement finite element analysis. The assessment of damage uncertainty is performed in three steps by, first, considering the test data only; then, considering the simulation data only; finally, performing an assessment of total uncertainty where test and simulation data sets are combined. This study leads to practical recommendations for reducing the uncertainty and improving the prediction accuracy of the damage modeling and finite element simulation.
J. Lucero; F. Hemez; T. Ross; K.Kline; J.Hundhausen; T. Tippetts
Epistemic Uncertainty in Evalustion of Evapotranspiration and Net Infiltration Using Analogue Meteorological Data
Uncertainty is typically defined as a potential deficiency in the modeling of a physical process, owing to a lack of knowledge. Uncertainty can be categorized as aleatoric (inherent uncertainty caused by the intrinsic randomness of the system) or epistemic (uncertainty caused by using various model simplifications and their parameters). One of the main reasons for model simplifications is a limited amount of meteorological data. This paper is devoted to the epistemic uncertainty quantification involved in two components of the hydrologic balance-evapotranspiration and net infiltration for interglacial (present day), and future monsoon, glacial transition, and glacial climates at Yucca Mountain, using the data from analogue meteorological stations. In particular, the author analyzes semi-empirical models used for evaluating (1) reference-surface potential evapotranspiration, including temperature-based models (Hargreaves-Samani, Thornthwaite, Hamon, Jensen-Haise, and Turc) and radiation-based models (Priestly-Taylor and Penman), and (2) surface-dependent potential evapotranspiration (Penman-Monteith and Shuttleworth-Wallace models). Evapotranspiration predictions are then used as inputs for the evaluation of net infiltration using the semi-empirical models of Budyko, Fu, Milly, Turc-Pike, and Zhang. Results show that net infiltration ranges are expected to generally increase from the present-day climate to monsoon climate, to glacial transition climate, and then to the glacial climate. The propagation of uncertainties through model predictions for different climates is characterized using statistical measures. Predicted evapotranspiration ranges are reasonably corroborated against the data from Class A pan evaporometers (taking into account evaporation-pan adjustment coefficients), and ranges of net infiltration predictions are corroborated against the geochemical and temperature-based estimates of groundwater recharge and percolation rates through the unsaturated zone obtained at Yucca Mountain.
B. Faybishenko
A computational framework for uncertainty quantification and stochastic optimization in unit commitment with wind power generation.
We present a computational framework for integrating a state-of-the-art numerical weather prediction (NWP) model in stochastic unit commitment/economic dispatch formulations that account for wind power uncertainty. We first enhance the NWP model with an ensemble-based uncertainty quantification strategy implemented in a distributed-memory parallel computing architecture. We discuss computational issues arising in the implementation of the framework and validate the model using real wind-speed data obtained from a set of meteorological stations. We build a simulated power system to demonstrate the developments.
Constantinescu, E. M; Zavala, V. M.; Rocklin, M.; Lee, S.; Anitescu, M. (Mathematics and Computer Science); (Univ. of Chicago); (New York Univ.)
An overview of the risk uncertainty assessment process for the Cassini space mission
The Cassini spacecraft is a deep space probe whose mission is to explore the planet Saturn and its moons. Since the spacecraft`s electrical requirements will be supplied by radioisotope thermoelectric generators (RTGs), the spacecraft designers and mission planners must assure that potential accidents involving the spacecraft do not pose significant human risk. The Cassini risk analysis team is seeking to perform a quantitative uncertainty analysis as a part of the overall mission risk assessment program. This paper describes the uncertainty analysis methodology to be used for the Cassini mission and compares it to the methods that were originally developed for evaluation of commercial nuclear power reactors.
Wyss, G.D. [Sandia National Labs., Albuquerque, NM (United States). Risk Assessment and Systems Modeling Dept.
Uncertainty and sensitivity analyses of ballast life-cycle cost and payback period
The paper introduces an innovative methodology for evaluating the relative significance of energy-efficient technologies applied to fluorescent lamp ballasts. The method involves replacing the point estimates of life cycle cost of the ballasts with uncertainty distributions reflecting the whole spectrum of possible costs, and the assessed probability associated with each value. The results of uncertainty and sensitivity analyses will help analysts reduce effort in data collection and carry on analysis more efficiently. These methods also enable policy makers to gain an insightful understanding of which efficient technology alternatives benefit or cost what fraction of consumers, given the explicit assumptions of the analysis.
McMahon, James E.; Liu, Xiaomin; Turiel, Ike; Hakim, Sajid; Fisher, Diane
A sampling-based computational strategy for the representation of epistemic uncertainty in model predictions with evidence theory.
Evidence theory provides an alternative to probability theory for the representation of epistemic uncertainty in model predictions that derives from epistemic uncertainty in model inputs, where the descriptor epistemic is used to indicate uncertainty that derives from a lack of knowledge with respect to the appropriate values to use for various inputs to the model. The potential benefit, and hence appeal, of evidence theory is that it allows a less restrictive specification of uncertainty than is possible within the axiomatic structure on which probability theory is based. Unfortunately, the propagation of an evidence theory representation for uncertainty through a model is more computationally demanding than the propagation of a probabilistic representation for uncertainty, with this difficulty constituting a serious obstacle to the use of evidence theory in the representation of uncertainty in predictions obtained from computationally intensive models. This presentation describes and illustrates a sampling-based computational strategy for the representation of epistemic uncertainty in model predictions with evidence theory. Preliminary trials indicate that the presented strategy can be used to propagate uncertainty representations based on evidence theory in analysis situations where naive sampling-based (i.e., unsophisticated Monte Carlo) procedures are impracticable due to computational cost.
Johnson, J. D. (Prostat, Mesa, AZ); Oberkampf, William Louis; Helton, Jon Craig (Arizona State University, Tempe, AZ); Storlie, Curtis B. (North Carolina State University, Raleigh, NC)
Uncertainty quantification of few group diffusion theory constants generated by the B1 theory-augmented Monte Carlo method
The purpose of this paper is to quantify uncertainties of fuel pin cell or fuel assembly (FA) homogenized few group diffusion theory constants generated from the B1 theory-augmented Monte Carlo (MC) method. A mathematical formulation of the first kind is presented to quantify uncertainties of the few group constants in terms of the two major sources of the MC method; statistical and nuclear cross section and nuclide number density input data uncertainties. The formulation is incorporated into the Seoul National Univ. MC code McCARD. It is then used to compute the uncertainties of the burnup-dependent homogenized two group constants of a low-enriched UO{sub 2} fuel pin cell and a PWR FA on the condition that nuclear cross section input data of U-235 and U-238 from JENDL 3.3 library and nuclide number densities from the solution to fuel depletion equations have uncertainties. The contribution of the MC input data uncertainties to the uncertainties of the two group constants of the two fuel systems is separated from that of the statistical uncertainties. The utilities of uncertainty quantifications are then discussed from the standpoints of safety analysis of existing power reactors, development of new fuel or reactor system design, and improvement of covariance files of the evaluated nuclear data libraries. (authors)
Park, H. J. [Korea Atomic Energy Research Inst., Daedeokdaero 989-111, Yuseong-gu, Daejeon (Korea, Republic of); Shim, H. J.; Joo, H. G.; Kim, C. H. [Dept. of Nuclear Engineering, Seoul National Univ., 1 Gwanak-ro, Gwanak-gu, Seoul (Korea, Republic of)
Justification shift and uncertainty: why are low-probability near misses underrated against organizational routines?
Knowledge of near misses is helpful in preventing accidents, but it does not always lead to changes in organizational routines or contribute to accident prevention. In this article, the authors argue that low-probability near misses reinforce beliefs ... Keywords: Burden of proof, Justification shift, Near miss, Organizational routine, Risk, Uncertainty
Junko Shimazoe; Richard M. Burton
A Large US Retailer Selects Transportation Carriers Under Diesel Price Uncertainty
A large US retailer that procures transportation services from third-party carriers experienced an unexpected jump in fuel surcharges as the price of diesel fuel skyrocketed in the summer of 2008. As a result, it sought to limit its future exposure to ... Keywords: price uncertainty, risk aversion, service contract, transportation
John Turner; Ben Peterson; Soo-Haeng Cho; Sunder Kekre; Alan Scheller-Wolf
Using Information on Uncertainty to Improve Environmental Fate Modeling: A Case Study on DDT
Present and future concentrations of DDT in the environment are calculated with the global multi-media model CliMoChem. Monte Carlo simulations are used to assess the importance of uncertainties in substance property data, emission rates, and environmental parameters for model results. Uncertainties in the model results, expressed as 95percent confidence intervals of DDT concentrations in various environmental media, in different geographical locations, and at different points in time are typically between one and two orders of magnitude. An analysis of rank correlations between model inputs and predicted DDT concentrations indicates that emission estimates and degradation rate constants, in particular in the atmosphere, are the most influential model inputs. For DDT levels in the Arctic, temperature dependencies of substance properties are also influential parameters. A Bayesian Monte Carlo approach is used to update uncertain model inputs based on measurements of DDT in the field. The updating procedure suggests a lower value for half-life in air and a reduced range of uncertainty for KOW of DDT. As could be expected, the Bayesian updating yields model results that are closer to observations, and model uncertainties have decreased. The combined sensitivity analysis and Bayesian Monte Carlo approach provide new insight into important processes that govern the global fate and persistence of DDT in the environment.
Schenker, Urs; Scheringer, Martin; Sohn, Michael D.; Maddalena, Randy L.; McKone, Thomas E.; Hungerbuhler, Konrad
Real-time deferrable load control: handling the uncertainties of renewable generation
Real-time demand response is essential for handling the uncertainties of renewable generation. Traditionally, demand response has been focused on large industrial and commercial loads, however it is expected that a large number of small residential loads ... Keywords: deferrable load control, demand response, model predictive control, smart grid
Lingwen Gan, Adam Wierman, Ufuk Topcu, Niangjun Chen, Steven H. Low
Analysis of Variability and Uncertainty in Wind Power Forecasting: An International Comparison (Presentation)
This presentation summarizes the work to investigate the uncertainty in wind forecasting at different times of year and compare wind forecast errors in different power systems using large-scale wind power prediction data from six countries: the United States, Finland, Spain, Denmark, Norway, and Germany.
Zhang, J.; Hodge, B.; Miettinen, J.; Holttinen, H.; Gomez-Lozaro, E.; Cutululis, N.; Litong-Palima, M.; Sorensen, P.; Lovholm, A.; Berge, E.; Dobschinski, J.
Impact of Early Forecast Information Sharing on Manufacturers with Capacity Uncertainty
251 Impact of Early Forecast Information Sharing on Manufacturers with Capacity Uncertainty of future demand. Advanced forecast information sharing between buyer and seller about these demand patterns) manufacturers receive an early rough forecast with a deterministic due date, however, forecast revisions
Chinnam, Ratna Babu
Total Measurement Uncertainty (TMU) for Nondestructive Assay of Transuranic (TRU) Waste at the WRAP Facility
This report examines the contributing factors to NDA measurement uncertainty at WRAP. The significance of each factor on the TMU is analyzed, and a final method is given for determining the TMU for NDA measurements at WRAP. As more data becomes available, and WRAP gains in operational experience, this report will be reviewed semi-annually and updated as necessary.
Conservation of Information: A New Approach to Organizing Human-Machine-Robotic Agents under Uncertainty
For many years, social scientists have struggled to make sense of the shift between individual and group perception, the difference between observation and action, and the meaning of interdependence. Interactions between these factors produce stable ... Keywords: autonomous systems, interdependence, observation-action conjugation, quantum model, social interaction, uncertainty
William F. Lawless; Donald A. Sofge; H. T. Goranson
Assessing spatial uncertainty in predictive geomorphological mapping: A multi-modelling approach
Maps of earth surface processes and the potential distribution of landforms make an important contribution to theoretical and applied geomorphology. Because decision making often depends on information based on spatial models, there is a great need to ... Keywords: Accuracy, Modelling, Patterned ground, Periglacial, Uncertainty
Miska Luoto; Mathieu Marmion; Jan Hjort
Phenomenons and Methods: Uncertainty in Internal Symmetry Nets with Backpropagation in Image Processing
Internal Symmetry Nets are a new developed class of cellular neural networks. It originated from internal symmetry quantum physics, and noted by five irreducible representations from group theory. In this paper, uncertainty of the nets in a series of ... Keywords: internal symmetry, back propagation, momentum and learning rate, overfitting, recurrent cycles
Guanzhong Li
Perspective---Cognitive Reactions to Rare Events: Perceptions, Uncertainty, and Learning
Research provides some observations about learning from events that appear to be rare or quite unusual. All learning has uncertain consequences, but learning from rare events is especially problematic. Learners see many idiosyncrasies and exogenous interference, ... Keywords: learning, rare events, reliable data, uncertainty
William H. Starbuck
PABS: A Computer Program to Normalize Emission Probabilities and Calculate Realistic Uncertainties
The program PABS normalizes relative particle emission probabilities to an absolute scale and calculates the relevant uncertainties on this scale. The program is written in Java using the JDK 1.6 library. For additional information about system requirements, the code itself, and compiling from source, see the README file distributed with this program. The mathematical procedures used are given below.
Caron, D. S.; Browne, E.; Norman, E. B.
On the relationship between uncertainties in tropical divergence and the hydrological cycle in global models
A survey of tropical divergence from three GCMs, three global reanalyses and four insitu soundings from field campaigns shows the existence of large uncertainties in the ubiquity of shallow divergent circulation as well as the depth and strength of the deep divergent circulation. More specifically, only two GCMs out of the three GCMs and three global reanalyses show significant shallow divergent circulation, which is present in all in-situ soundings, and of the three GCMs and three global reanalyses, only two global reanalyses have deep divergence profiles that lie within the range of uncertainty of the soundings. The relationships of uncertainties in the shallow and deep divergent circulation to uncertainties in present day and projected strength of the hydrological cycle from the GCMs are assessed. In the tropics and subtropics, deep divergent circulation is the largest contributor to moisture convergence that balances the net precipitation, and inter-model differences in the present day simulations carry over onto the future projections. In comparison to the soundings and reanalyses, the GCMs are found to have deeper and stronger divergent circulation. While these two characteristics of GCM divergence affect the strength of the hydrological cycle, they tend to compensate for each other so that their combined effect is relatively modest.
Hagos, Samson M.; Leung, Lai-Yung R.
Vessel routing and scheduling under uncertainty in the liquefied natural gas business
Liquefied natural gas (LNG) is natural gas transformed into liquid state for the purpose of transportation mainly by specially built LNG vessels. This paper considers a real-life LNG ship routing and scheduling problem where a producer is responsible ... Keywords: Liquefied natural gas, Maritime transportation, Ship routing and scheduling, Simulation, Uncertainty
Elin E. Halvorsen-Weare; Kjetil Fagerholt; Mikael RöNnqvist
Managing uncertainty and ambiguity in frontier R&D projects: A Korean case study
One of the important tasks in planning large, frontier R&D projects is to minimize innate uncertainties and ambiguities in the early stages of the project. This case study is an attempt to provide a framework to handle such problems in R&D planning. ... Keywords: Fuzzy front-end, Knowledge management, O32, R&D planning, Technology management
Yong-Il Song; Dae-Hee Lee; Yong-Gil Lee; Yun-Chul Chung
Validation of statistical estimation of transmission loss in the presence of geoacoustic inversion uncertainty
, these sampled parameters are mapped to the transmission loss domain where a full multidimensional probability, exhaustive grid sampling was used to obtain the geoacoustic uncertainties and map these to the TL domainValidation of statistical estimation of transmission loss in the presence of geoacoustic inversion
Buckingham, Michael
Advances in vehicle modeling and simulation in recent years have led to designs that are safer, easier to handle, and less sensitive to external factors. Yet, the potential of simulation is adversely impacted by its limited ability to predict vehicle dynamics in the presence of uncertainty. A commonly occurring source of uncertainty in vehicle dynamics is the road-tire friction interaction, typically represented through a spatially distributed stochastic friction coefficient. The importance of its variation becomes apparent on roads with ice patches, where if the stochastic attributes of the friction coefficient are correctly factored into real time dynamics simulation, robust control strategies could be designed to improve transportation safety. This work concentrates on correctly accounting in the nonlinear dynamics of a car model for the inherent uncertainty in friction coefficient distribution at the road/tire interface. The outcome of this effort is the ability to quantify the effect of input uncertainty on a vehicle's trajectory and the associated escalation of risk in driving. By using a space dependent Gaussian ? Address all correspondence to this author.
Kyle Schmitt; Justin Madsen; Mihai Anitescu; Dan Negrut
A novel branch and bound algorithm for optimal development of gas fields under uncertainty in reserves
models for planning in the oil and gas exploration and production industry. A major challenge of the available literature that deals with planning of oil and gas field infrastruc- tures uses a deterministicA novel branch and bound algorithm for optimal development of gas fields under uncertainty
Grossmann, Ignacio E.
Impact of risk and uncertainty on sustainable development of Kolubara lignite basin
The paper analyzes the various risks and uncertainties and their possible impact on the future development of the Kolubara lignite basin area (Belgrade metropolitan region). What has been examined are the risks caused by the global financial crisis to ... Keywords: energy policy, lignite coal basin, privatisation, risks, sustainable development, uncertain
Slavka Zekovic; Miodrag Vujosevic
Effect of Epistemic Uncertainty Modeling Approach on Decision-Making: Example using Equipment Performance Indicator
Quantitative risk assessments are an integral part of risk-informed regulation of current and future nuclear plants in the U.S. The Bayesian approach to uncertainty, in which both stochastic and epistemic uncertainties are represented with precise probability distributions, is the standard approach to modeling uncertainties in such quantitative risk assessments. However, there are long-standing criticisms of the Bayesian approach to epistemic uncertainty from many perspectives, and a number of alternative approaches have been proposed. Among these alternatives, the most promising (and most rapidly developing) would appear to be the concept of imprecise probability. In this paper, we employ a performance indicator example to focus the discussion. We first give a short overview of the traditional Bayesian paradigm and review some its controversial aspects, for example, issues with so-called noninformative prior distributions. We then discuss how the imprecise probability approach treats these issues and compare it with two other approaches: sensitivity analysis and hierarchical Bayes modeling. We conclude with some practical implications for risk-informed decision making.
Dana Kelly; Robert Youngblood
A Monte Carlo Assessment of Uncertainties in Heavy Precipitation Frequency Variations
A Monte Carlo analysis was used to assess the effects of missing data and limited station density on the uncertainties in the temporal variations of U.S. heavy precipitation event frequencies observed for 1895–2004 using data from the U.S. ...
Kenneth E. Kunkel; Thomas R. Karl; David R. Easterling
Searching with Uncertainty Cut-o s Yngvi Bjornsson, Tony Marsland,
is that expectations change. Uncertainty in the search results in changes of the principal variation 1 #12;(PV). When not necessarily a ect the reliability of the game value at the root of the search tree. If the pruning is only is a ecting the reliability of the game value, thus giving us the opportunity to correct it by re
Schaeffer, Jonathan
Control of absorption columns in the bioethanol process: Influence of measurement uncertainties
The alcohol lost by evaporation during the bioethanol fermentation process may be collected and recovered using an absorption column. This equipment is also used in the carbonic