Better optimization of nonlinear uncertain systems (BONUS) for vehicle structural design

With the continuous improvement of computational performance, vehicle structural design has been addressed using computational methods, resulting in more efficient development of new vehicles. Most simulation-based optimization approaches generate deterministic designs without considering variability effects in modeling, simulation, and/or manufacturing. One of the main reasons for this omission is due to the fact that the computing time of a single crash analysis for vehicle structural design still requires significant computing time using a state-of-the-art computer. This calls for the development and implementation of an efficient optimization under uncertainty method. In this paper, a new integrated stochastic optimization method, which combines the advantages of metamodeling techniques and Better Optimization of Nonlinear Uncertain Systems (BONUS), is developed for vehicle side impact design. Non-linear metamodels are built by using a stepwise regression method to replace the expensive computational model and BONUS is employed to obtain optimal designs under uncertainty. A benchmark problem for vehicle safety design is used to demonstrate the method. The main goal of this case study is to maintain or enhance the vehicle side impact test performance while minimizing the vehicle weight under various uncertainties.