A method to reduce design complexity of automotive composite structures with respect to crashworthiness

Composites in automotive structures show great promise to further improve vehicle crashworthiness. However, designing automotive structures for crash with advanced composite materials is challenging. The large amount of design parameters for laminated composites, the complex non-linear material behavior and the discontinuous design space in vehicle design, such for crashworthiness, are the main contributors to this challenge. In this paper, we propose a new design strategy to address this and integrate advanced laminated composite materials in automotive design for crashworthiness. First a computationally efficient physical surrogate is introduced to predict the structural validity of the design options and filter the design space. Secondly a method is introduced which uses Sobol decomposition to derive a design parameter importance hierarchy. Thirdly the physical surrogate is used to derive parameter bounds to increase robustness. A typical S-rail benchmark has been developed to confirm the usefulness of the proposed method. Finally the method provides for a reduced and robust design space which may help to decrease early development time.