In the automotive industry, building parametric surrogate models is a fundamental tool to evaluate, in real time, the performance of newly designed car components. Such models allow to compute any Quantity of Interest -QoI-, such as a specific safety protocol index, for any choice of material and/or geometrical parameters characterizing the component, within the stringent real time constraint. For instance, they can be exploited to guarantee safer designs (e.g., maximizing energy absorption by the crash boxes) or to reduce manufacturing costs (e.g., minimizing the mass of a specific structure under some safety protocol constraints). In general, these parametric simulation tools allow a significant gain in terms of manufacturing costs and time delays during the investigation phase. In this study, we focus on the vehicle frontal structure system considering its performance in a full-frontal crash scenario. In the front structure system we parameterize the crash boxes (left and right) and the inner/outer side front members (left and right, front and rear) with respect to the part thickness and the material parameters characterizing the Krupkowski plasticity curve. Moreover, Strain Rate Effect is also taken into account via Neural Network based regressions, whose training dataset comes from experimental data. The parametric metamodel is built via Non-Intrusive PGD -NI-PGD- strategies, relying on a sparse sampling of the parametric space, and allowing a quite reduced number of High Fidelity -HiFi- simulations. A novel strategy based on clustering and classification, known as Multi-PGD, is also applied and numerically verified.
Keywords: Crashworthiness; Curves metamodeling; Lightweightness; MOR; Multi-sPGD; PODI; Parametric strain-hardening laws; Vehicle frontal structure; sPGD.
© 2022 The Authors.