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

Dennis Grunert, Jörg Fehr
2016 Advanced Modeling and Simulation in Engineering Sciences  
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
more » ... gorithms 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. which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
doi:10.1186/s40323-016-0072-x fatcat:av2nby32xbgflaktpnu536d6qm