Identification of nonlinear behavior with clustering techniques in car crash simulations for better model reduction

Abstract

Background: Car crash simulations need a lot of computation time. Model reduction can be applied in order to gain time-savings. Due to the highly nonlinear nature of a crash, an automatic separation in parts behaving linearly and nonlinearly is valuable for the subsequent model reduction. Methods: We analyze existing preprocessing and clustering methods like k-means and spectral clustering for their suitability in identifying nonlinear behavior. Based on these results, we improve existing and develop new algorithms which are especially suited for crash simulations. They are objectively rated with measures and compared with engineering experience. In future work, this analysis can be used to choose appropriate model reduction techniques for specific parts of a car. A crossmember of a 2001 Ford Taurus finite element model serves as an industrial-sized example. Results: Since a non-intrusive black box approach is assumed, only heuristic approaches are possible. We show that our methods are superior in terms of simplicity, quality and speed. They also free the user from arbitrarily setting parameters in clustering algorithms. Conclusion: Though we improved existing methods by an order of magnitude, preparing them for the use with a full car model, they still remain heuristic approaches that need to supervised by experienced engineers.

CrashNet: an encoder–decoder architecture to predict crash test outcomes

“…3. Predicting the car deformation (Hilmann and Hänschke 2009;Grunert and Fehr 2016;Zhao et al 2010), and the occupant behavior during a crash (Bastien et al 2017;Iwamoto et al 2012;Untaroiu and Adam 2012).…”