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2016

Preference-based Topology Optimization of Body-in-white Structures for Crash and Static Loads
Topology optimization methods are increasingly applied tools for the design of lightweight structural concepts in the automotive design process. Ideally, topology optimization provides the optimum distribution of material within a user-defined design space for a given objective function. In the vehicle design process, two important objectives are to maximize stiffness of components for regular working conditions and to maximize energy absorption in exceptional loading conditions, for instance in crash events. For these objective functions, the Hybrid Cellular Automata algorithm devises efficient structures in case of the separated disciplines, by heuristically aiming for a uniform distribution of energy densities. Recently, it was demonstrated that a concurrent optimization of crash and static load cases can be performed by a linear weighting, in which the user preference is separated from the scaling of the internal energies. In this paper, the approach is applied to the practical example of a vehicle body-in-white design, which is optimized for multiple crash and linear static load cases. By comparing resulting internal energies of different load case settings we demonstrate that the hybrid cellular automata algorithm with scaled energy weighting is capable to find a very good trade-off solution within a single concurrent optimization run.
The LS-TaSC(TM) Multipoint Method for Constrained Topology Optimization
The new multi-point constrained optimization scheme is for the constrained topology design of highly nonlinear structures for which analytical design sensitivity information is hard to compute. These highly nonlinear structures are designed for multiple load cases and multiple constraints, which means that the final design should have load paths for each load case as well as satisfy the constraints. This is done here by using two sets of variables: the local variables describing the part topology on the element level and the global variables consisting of the load case weights and part masses. The two sets of variables are treated differently in the design algorithm: the local variables are computed using a suitable method such as fully stressed design, while the values of the global variables satisfying the constraints are computed using numerical derivatives and mathematical programming.
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