@inproceedings {pub3095,
	title = {Evolutionary Level Set Method for Crashworthiness Topology Optimization},
	author = {Mariusz Bujny AND Nikola Aulig AND Markus Olhofer AND Fabian Duddeck},
	year = {2016},
	month = {June},
	abstract = {Vehicle crashworthiness design belongs to one of the most complex problems considered in the design optimization. Physical phenomena that are taken into account in crash simulations range from complex contact modeling to mechanical failure of materials. This results in high nonlinearity of the optimization problem and involves remarkable amount of numerical noise and discontinuities of the objective functions that are being optimized. Consequently, the sensitivity information can be obtained analytically only for considerably simplified problems, which, in most cases, excludes the use of the gradient-based optimization methods. As a result, in the state-of-the-art methods for crashworthiness Topology Optimization, very strong assumptions about the properties of the optimization problem are made and heuristic approaches are used to optimize structures. As a result, in each of those methods, the optimality criterion is arguable. This problem can be solved with use of Evolutionary Algorithms, where no assumptions about the optimization problem have to be made and which perform very well even for highly nonlinear and discontinuous problems. In this paper we propose an evolutionary optimization approach using a geometric Level-Set Method for an implicit representation of mechanical structures. In order to evaluate the proposed method, an energy maximization problem for a 2D transverse bending of a rectangular beam is considered. The results show that the evolutionary optimization methods can be efficiently used for an optimization of crash-loaded structures. In most experiments, the optimized topologies show many similarities to the designs obtained with the Hybrid Cellular Automata technique.},
	publisher = {European Community on Computational Methods in Applied Sciences},
	booktitle = {ECCOMAS Congress 2016}
}