Nonintrusive uncertainty quantification for automotive crash problems with VPS/Pamcrash

Abstract

Uncertainty Quantification (UQ) is a key discipline for computational modeling of complex systems, enhancing reliability of engineering simulations. In crashworthiness, having an accurate assessment of the behavior of the model uncertainty allows reducing the number of prototypes and associated costs. Carrying out UQ in this framework is especially challenging because it requires highly expensive simulations. In this context, surrogate models (metamodels) allow drastically reducing the computational cost of Monte Carlo process. Different techniques to describe the metamodel are considered, Ordinary Kriging, Polynomial Response Surfaces and a novel strategy (based on Proper Generalized Decomposition) denoted by Separated Response Surface (SRS). A large number of uncertain input parameters may jeopardize the efficiency of the metamodels. Thus, previous to define a metamodel, kernel Principal Component Analysis (kPCA) is found to be effective to simplify the model outcome description. A benchmark crash test is used to show the efficiency of combining metamodels with kPCA. • Nonintrusive combination of data techniques and surrogates for parametric analysis, design and uncertainty quantification. • Application to VPS/Pamcrash finite elements crashworthiness simulations for automotive industry. • A novel surrogate model, the Separated Response Surface (SRS), based on Proper Generalized Decomposition method. • Application on a benchmark crash model of interest for automotive industry (Volkswagen Group).