以英国汽车工业研究协会(Motor Industry Research Association,MIRA)阶背模型为基本模型,用参数化建模方法建立其纵向对称面的二维模型.运用优化拉丁超立方方法对每组参数化方案生成600组样本点;将MATLAB与Gambit结合,自动快速生成其...以英国汽车工业研究协会(Motor Industry Research Association,MIRA)阶背模型为基本模型,用参数化建模方法建立其纵向对称面的二维模型.运用优化拉丁超立方方法对每组参数化方案生成600组样本点;将MATLAB与Gambit结合,自动快速生成其网格模型;用FLUENT计算每个样本点的气动阻力.建立径向基神经网络(Radial Basis Function Neural Network,RBFNN)近似模型,以阻力最小为优化目标,采用多岛遗传算法优化外形参数;对优化后的结果进行数值模拟,结果表明阻力减少31.9%.三维验证结果表明:二维优化结果不能完全代表三维结果,直接进行三维优化设计的效果更好.展开更多
As the running speed of high-speed trains increases, aerodynamic drag becomes the key factor which limits the further increase of the running speed and energy consumption. Aerodynamic lift of the trailing car also bec...As the running speed of high-speed trains increases, aerodynamic drag becomes the key factor which limits the further increase of the running speed and energy consumption. Aerodynamic lift of the trailing car also becomes the key force which affects the amenity and safety of the train. In the present paper, a simplified CRH380A high-speed train with three carriages is chosen as the model in order to optimize aerodynamic drag of the total train and aerodynamic lift of the trailing car. A constrained mul- ti-objective optimization design of the aerodynamic head shape of high-speed trains based on adaptive non-dominated sorting genetic algorithm is also developed combining local function three-dimensional parametric approach and central Latin hypercube sampling method with maximin criteria based on the iterative local search algorithm. The results show that local function parametric approach can be well applied to optimal design of complex three-dimensional aerodynamic shape, and the adaptive non-dominated sorting genetic algorithm can be more accurate and efficient to find the Pareto front. After optimization the aerodynamic drag of the simplified train with three carriages is reduced by 3.2%, and the lift coefficient of the trailing car by 8.24%, the volume of the streamlined head by 2.16%; the aerodynamic drag of the real prototype CRH380A is reduced by 2.26%, lift coefficient of the trailing car by 19.67%. The variation of aerodynamic performance between the simplified train and the true train is mainly concentrated in the deformation region of the nose cone and tail cone. The optimization approach proposed in the present paper is simple yet efficient, and sheds lights on the constrained multi-objective engineering optimization design of aerodynamic shape of high-speed trains.展开更多
文摘以英国汽车工业研究协会(Motor Industry Research Association,MIRA)阶背模型为基本模型,用参数化建模方法建立其纵向对称面的二维模型.运用优化拉丁超立方方法对每组参数化方案生成600组样本点;将MATLAB与Gambit结合,自动快速生成其网格模型;用FLUENT计算每个样本点的气动阻力.建立径向基神经网络(Radial Basis Function Neural Network,RBFNN)近似模型,以阻力最小为优化目标,采用多岛遗传算法优化外形参数;对优化后的结果进行数值模拟,结果表明阻力减少31.9%.三维验证结果表明:二维优化结果不能完全代表三维结果,直接进行三维优化设计的效果更好.
基金supported by the Major State Basic Research Development Program of China ("973" Program) (Grant No. 2011CB711100) National Key Technology R&D Program (Grant No. 2009BAQG12A03)
文摘As the running speed of high-speed trains increases, aerodynamic drag becomes the key factor which limits the further increase of the running speed and energy consumption. Aerodynamic lift of the trailing car also becomes the key force which affects the amenity and safety of the train. In the present paper, a simplified CRH380A high-speed train with three carriages is chosen as the model in order to optimize aerodynamic drag of the total train and aerodynamic lift of the trailing car. A constrained mul- ti-objective optimization design of the aerodynamic head shape of high-speed trains based on adaptive non-dominated sorting genetic algorithm is also developed combining local function three-dimensional parametric approach and central Latin hypercube sampling method with maximin criteria based on the iterative local search algorithm. The results show that local function parametric approach can be well applied to optimal design of complex three-dimensional aerodynamic shape, and the adaptive non-dominated sorting genetic algorithm can be more accurate and efficient to find the Pareto front. After optimization the aerodynamic drag of the simplified train with three carriages is reduced by 3.2%, and the lift coefficient of the trailing car by 8.24%, the volume of the streamlined head by 2.16%; the aerodynamic drag of the real prototype CRH380A is reduced by 2.26%, lift coefficient of the trailing car by 19.67%. The variation of aerodynamic performance between the simplified train and the true train is mainly concentrated in the deformation region of the nose cone and tail cone. The optimization approach proposed in the present paper is simple yet efficient, and sheds lights on the constrained multi-objective engineering optimization design of aerodynamic shape of high-speed trains.
