Data-driven models for crashworthiness optimisation: intrusive and non-intrusive model order reduction techniques

Abstract To enable multi-query analyses, such as optimisations of large-scale crashworthiness problems, a numerically efficient model is crucial for the development process. Therefore, data-driven Model Order Reduction (MOR) aims at generating low-fidelity models that approximate solution while strongly reducing computational cost. MOR methods became only available in recent years; detailed and comparative assessment their potential still lacking. Hence, this work evaluates advantages drawbacks intrusive non-intrusive projection based framework non-linear structural transient analysis. Both schemes rely on collection full-order training simulations subsequent subspace construction via Singular Value Decomposition. The Galerkin consecutive hyper-reduction step. In work, its inter-and extrapolation abilities are compared to technique, which combines approach with machine learning methods. Moreover, an optimisation analysis incorporating proposed discussed example.