On the combination of dynamic substructuring and surrogate modeling for the design and study of complex systems

Abstract

For decades, dynamic substructuring approaches have been successfully utilized for the modeling and simulation of complex systems. More recently, surrogate modeling methods have emerged as a way to reduce the computational cost of simulating for design. This talk will introduce a paradigm in which these two techniques may be combined for the design and study of complex systems. For illustration, a simple example is put forward in which a mass-spring system is broken into subsystems and parameterized. The open-source Python package SMT (Surrogate Modeling Toolbox) developed and maintained by Bouhel et al. is used to construct surrogate models for each subsystem in which the component parameters are taken as inputs and the frequency response functions (FRFs) of the interface degrees of freedom are output. These FRFs are then used to construct the response of the fully coupled system by utilizing a dual Frequency Based Substructuring technique. In many real-world applications, this manner of calculating the effect of varying design parameters on the system response is likely more practical and computationally feasible than the alternative of directly simulating the coupled system response for many parameter sets and training a surrogate model from that output for further design space exploration and optimization. [Work supported by the Office of Naval Research]