基于modeFRONTIER软件的空气动力学多目标智能优化

Multi Objective Intelligent Optimization of Aerodynamics Based on modeFRONTIER Software

为了使Ahmed模型气动阻力和气动升力同时最优化,研究多目标智能优化方法在汽车空气动力学领域的工程应用价值,文章采用modeFRONTIER软件搭建优化流程。本研究采用优化拉丁超立方试验设计方法对变形参数进行采样,经过CFD仿真后,选择神经网络搭建近似模型建立参数与阻力系数和升力系数的对应关系,然后使用多目标遗传算法进行优化。优化后,Ahmed模型阻力系数降低了46.6%,升力系数降低了36.5%。实践证明,基于modeFRONTIER软件的空气动力学多目标智能优化方法具有较高工程应用价值。

In order to optimize the aerodynamic drag and lift force of Ahmed model at the same time, and estimate the application value of multi-objective intelligent optimization method in the field of automotive aerodynamics, this paper uses the modeFRONTIER software to build the optimization process. The design of experiments（DOE） method of this study uses optimized Latin squares to sample parameters. After CFD simulation, this study chooses Neural Network（NN） to build an approximate model, and establishes the corresponding relationship between parameters and drag coefficient and lift coefficient.On the basis of approximate model, multi objective genetic algorithm（MOGA） is used to optimize. After optimization, the drag coefficient of Ahmed model is reduced by 46.6%, and the lift coefficient is reduced by 36.5%. It has proved that the multi-objective intelligent optimization method of aerodynamics based on modeFRONTIER software has high engineering application value.