Building Gaussian Process Based Metamodels Using Variable-Fidelity Experiments for Dynamic Analysis of Mechanical Systems Available to Purchase

Given the highly nonlinear attribute of the underlying dynamics associated with the time evolution of multibody systems, an open question in mechanical system simulation is how one can reliably replace a model whose simulation is time consuming with a more expeditious one. Pushing this idea to the limit one can all together eliminate the dynamics of the problem using a set of simulations that train a predictor that is later used to provide the time evolution of the dynamic system. This paper investigates a Gaussian Random Function (GRF) based approach that attempts to address these questions. It relies on a framework recently proposed in the Statistical Analysis community that largely deals with the issues of model validation, calibration, and data integration. The approach investigated has several steps that are illustrated with a slider-crank mechanical systems whose time evolution is governed by a nonlinear set of index 3 Differential Algebraic Equations (DAEs). The paper concludes with a set of remarks on the potential of GRFs in the context of time domain analysis of mechanical systems.