Load Case Preference Patterns based on Parameterized Pareto-Optimal Vehicle Design Concept Optimization

Classical Topology Optimization (TO) methods aim to optimize the distribution of material within a design space for one given objective function and constraints. However, in the vehicle design process, there are many different load cases and several different objectives. Among them maximizing stiffness of components for regular working conditions, and maximizing energy absorption in exceptional loading conditions, for instance in crash events, are important. Recently, the Scaled Energy Weighting Hybrid Cellular Automata (SEW-HCA) [1], [2] method was adopted in LS-TaSC™. The SEW-HCA is a practical multidisciplinary TO approach for devising concept structures based on the intuitive choice of preferences leading to the desired trade-off between crash performance and stiffness. In this paper, we propose an integration of the SEW-LS-TaSC method into LS-OPT® to perform a design of experiments (DOE) on the load case preference parameters. The integration into LS-OPT® results in a convenient user interface that facilitates application in an industrial development process with non-expert users. This integration enables quick studies on many different concept designs based on preference samples generated by the DOE. The results from the sensitivity analysis provide data for a better understanding of the influence of load case preferences on the design space. By comparing the performance of structures obtained for different load case preferences, the user will be able to find a desired trade-off solution within the concurrent optimization runs. For further development, the proposed LS-OPT® workflow can potentially include 1) NVH load cases as additional discipline and 2) optimizations of other topology optimization hyperparameters for further concept exploration.