Aerodynamics Design for Automotive Shape based on Approximate Optimization Algorithm

LIN-LIN WU, YU FU, XIAO-BING BU, XIANG-RONG LI, FENG-LING GAO

Abstract


Since it is difficult to find the optimal solution directly by the traditional CFD optimization method due to its strong dependence on the designer’s experience, an automatic aerodynamic optimization design platform for automotive shape was built based on mesh deformation technology, surrogate model and optimization algorithm in this paper. A parameterized model of an automotive was established. Latin hypercube method was adopted to select sample points. The drag coefficients corresponding to sample points were calculated by CFD simulation, whereby the influence of each parameter on drag coefficient was obtained. By comparing the calculation time, optimization effect and optimization accuracy of 9 combinations of surrogate models and optimization algorithms, the combination of RBF model and NLPQL algorithm was selected as the optimal one which is the most appropriate for the aerodynamic optimization design for automotive shape

Keywords


Automotive shape, Parameterization, Surrogate model, Optimization algorithmText


DOI
10.12783/dteees/peems2019/34005

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