Beverley McKeon: Data-driven descriptions of linear & nonlinear interactions in wall turbulence
Time: Tue 2024-05-21 10.30 - 11.30
Participating: Beverley McKeon (Stanford University)
Significant recent progress has been made in flow modeling using both equation-driven and data-driven techniques. We focus here on the intersection of these two approaches, using data to complete the details of known flow dynamics. We utilize the classical approaches and tools of the modern day – theoretical analysis, data-driven methods and machine learning tools – to illuminate features responsible for the sustenance of turbulence associated with nonlinear interactions in the Navier-Stokes equations. Focusing on a spatio-temporal representation of turbulence near walls – an omnipresent phenomenon in large-scale transport and transportation – we identify and quantify key scale interactions. Methods to obtain data-driven representations of both linear and nonlinear dynamics will be discussed, along with some implications for the modeling of wall turbulence. The work has benefited from funding by the US ONR, ARO and AFOSR over a period of years, which is gratefully acknowledged.