Nonintrusive Uncertainty Quantification for automotive crash problems with VPS/Pamcrash

TL;DR

This paper presents a nonintrusive uncertainty quantification approach for automotive crash simulations, combining surrogate models with kernel PCA to reduce computational costs and improve accuracy.

Contribution

It introduces a novel Separated Response Surface method based on Proper Generalized Decomposition and demonstrates its effectiveness with kernel PCA in crashworthiness UQ.

Findings

Metamodels combined with kPCA improve efficiency

Separation strategy enhances surrogate model accuracy

Approach reduces computational costs in crash simulations

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.