In past decades the simulation results of vehicle impacts have reached high confidence levels. However, impact simulation remains computationally expensive, even though it is commonly used. Surrogate model or response surface based design optimization has been widely adopted as a common process in automotive industry, as large-scale, high fidelity models are often required. However, most surrogate models are built by using a limited number of design points without considering data uncertainty and the selection of surrogate model in the literature is often arbitrary.
This paper presents a Bayesian metric to complement root mean square error for selecting the best surrogate model among several candidates in a library under data uncertainty. A strategy for automatically selecting the best surrogate model and determining a reasonable sample size was proposed for design optimization of large-scale complex problems. modeFRONTIER’s ULH (Uniform Latin Hypercube) DOE algorithm was used for sampling and to guarantee a relatively uniform distribution over each dimension. Lastly, a vehicle example with full-frontal and offset-frontal impacts was presented to demonstrate the proposed methodology.
Comparison of structure deformation between finite element simulation and test. a) Full frontal impact, b) 40% offset impact.