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A major automotive supplier was investigating new concepts for a structural system. In order to more thoroughly explore the concept and shorten the design cycle time, an automated multi-objective Pareto optimization study was performed. The goals of this study were to find the trade-offs between minimizing mass and cost, satisfy regulatory requirements for rear crash events, and meet targets on the natural frequencies of the system. A total of 11 design variables were used, which included shape, material and gauge thickness variables.
Does it matter which optimization technology an engineering team chooses? We think that the answer is unequivocally: yes! All optimization algorithms are not created equal. Many work well only on certain types of problems, and some are very inefficient at finding optimal solutions. The difference between a robust, efficient algorithm and an inferior one can be substantial in terms of real measures such as product cost, mass, and performance. This is paticularly important when optimizing CAE solutions such as CFD and Crash, for which the cost of individual function evaluations is computationally expensive.
Established simulation models for Li-ion batteries are formulated in 0D or 1D (Dualfoil), and rely on assumptions, e.g. well defined particle shapes and absence of diffusion between such particles. Fitted solid conductivities have to be applied to match experiments. The 3D Micro-Structural Electrochemistry model implemented in STAR-CCM+ extends the well known Dualfoil model to 3D, in order to be able to accuratelypredict spatial phenomena.