Dynamic Substructuring (DS) is an engineering tool used to model and analyse the dynamics of mechanical systems by means of its components or substructures. Using the dynamic substructuring approach one is able to analyse the dynamic behaviour of substructures separately and to later on calculate the assembled dynamics using coupling procedures. Dynamic substructuring has several advantages over the analysis of the fully assembled system: Substructures can be modelled in the domain that is most appropriate, e.g. experimentally obtained substructures can be combined with numerical models. Large and/or complex systems can be optimized on substructure level. Numerical computation load can be reduced as solving several substructures is computationally less demanding than solving one large system. Substructure models of different development groups can be shared and combined without exposing the modelling details. Dynamic substructuring is particularly tailored to simulation of mechanical vibrations, which has implications for many product aspects such as sound / acoustics, fatigue / durability, comfort and safety. Also, dynamic substructuring is applicable to any scale of size and frequency. It is therefore a widely used paradigm in industrial applications ranging from automotive and aerospace engineering to design of wind turbines and high-tech precision machinery. History The roots of dynamic substructuring can be found in the field of domain decomposition. In 1890 the mathematician Hermann Schwarz came up with an iterative procedure for domain decomposition which allows to solve for continuous coupled subdomains. However, many of the analytical models of coupled continuous subdomains do not have closed-form solutions, which led to discretization and approximation techniques such as the Ritz method (which is sometimes called the Rayleigh-Ritz method due to the similarity between Ritz's formulation and the Rayleigh ratio) the boundary element method (BEM) and the finite element method (FEM). These methods can be considered as "first level" domain decomposition techniques. The finite element method proved to be the most efficient method and the invention of the microprocessor made it possible to easily solve a large variety of physical problems. In order to analyse even larger and more complex problems, methods were invented to optimize the efficiency of the discretized calculations. The first step was replacing the direct solvers by iterative solvers such as the conjugate gradient method. The lack of robustness and slow convergence of these solvers did not make them an interesting alternative in the beginning. The rise of parallel computing in the 1980s however sparked their popularity. Complex