Nonintrusive Uncertainty Quantification for automotive crash problems with VPS/Pamcrash
This work addresses the problem of reducing prototype costs in automotive crashworthiness for engineers, but it appears incremental as it builds on existing metamodel techniques with a novel combination.
The paper tackles the challenge of performing uncertainty quantification (UQ) in automotive crash simulations, which are computationally expensive, by proposing a method that combines metamodels with kernel Principal Component Analysis (kPCA) to reduce costs, demonstrating efficiency on a benchmark crash test.
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.