Large-Scale Reliability Based Design Optimization For Vehicle Crash Safety

The focus of this paper is on a large-scale reliability based design optimization of a car body structure for crash safety. The application of multidisciplinary design optimization (MDO) to automotive vehicle structure design has been an interest topic over the past several years (Yang et al. 1994, Schramm et al. 1999). Sobieski et al. (2000) and Kodiyalam et al. (2001) reported a method with very significant reduction in computing time for such large-scale MDO problems. The Finite element based full vehicle structural crash simulation is a design tool commonly used throughout the automotive industry to evaluate vehicle crash performance. In most applications, the optimization and robustness analysis are applied on surrogate models (or response surface models). After evaluating several response surface models for crashworthiness, Yang et al. (2000) recommended to use second order polynomial regression model and moving least square regression for crash safety optimization. Gu (2001) proposed sequential replacement algorithm for polynomial based regression model and evaluated different order of polynomial models. With a limited number of simulations, it was found that the second order polynomial response surface is good enough for many applications of crash safety. Another key element outlined in this paper is that it uses strategic sampling of the full-scale model, which is invoked only sparingly to save effort. The full-scale model is still used as the principal source of information about the system and therefore the computational cost per evaluation does not change. This strategy is effective and suitable to parallel computing.