Investigation of the Hybrid Cellular Automata (HCA) optimization method for the identification of structural topologies with improved crash performance

Abstract

Topology optimization is often seen as a good resort in the development of lightweight and crashworthy structures and is hence used extensively in the automotive and aerospace industry. However, the current topology optimization methods based on gradient and sensitivity analysis are numerically expensive as the modelling of a crash analysis is complex and involves geometrical and material nonlinearities. Furthermore, the computation time increases with an increase in the number of discretization elements in the domain. To develop strategies for efficiently identifying structures with improved crash performance, a novel method named Hybrid Cellular Automata is investigated in this thesis for optimization of the parts of DLR's Urban Modular Vehicle for pedestrian safety.