A new study on optimisation under uncertainty for vehicle design

With continuous improvement of powerful computers, vehicle designs have been addressed using computational methods. This results in more efficient development of a new product. Most simulation-based optimisation generates deterministic optimal designs without considering variability effects. This paper presents a hybrid optimisation under uncertainty method for vehicle side impact design. Nonlinear response surface models are employed as the 'real' models for conducting this study. The main goal is to maintain or enhance the vehicle side impact performance while minimising the vehicle weight under uncertainty. The hybrid method alleviates the computational burden by estimating the objective and constraint functions during the optimisation process through a re-weighting approach. It also combines a genetic algorithm and sequential quadratic programming to achieve the global or near global optimal designs. The efficiency and accuracy of this method are demonstrated by solving a vehicle design problem.