Genetically aerodynamic optimization of the nose shape of a high-speed train entering a tunnel
Genetically aerodynamic optimization of the nose shape of a high-speed train entering a tunnel
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DOI：
10.1016/j.jweia.2014.03.005
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发表时间：
2014-07-01
影响因子：
4.8
通讯作者：
Crespo, A.
中科院分区：
文献类型：
--
作者：
Munoz-Paniagua, J.;Garcia, J.;Crespo, A.
利用遗传算法对高速列车进入隧道的机头形状进行了优化。这种优化方法需要将每个最优候选者作为设计向量进行参数化。机头的几何参数是使用三个设计变量定义的，这三个变量包括影响列车入口处产生的压缩波的最具特征的几何因素和列车的气动阻力。对真实列车模型进行了大量的三维、湍流、可压缩、非定常模拟，这些信息已被用来拟合元模型。GA使用元模型来更有效地评估每个最优候选者。使最大压力梯度和气动阻力最小的优化设计与文献结果吻合较好。为了完成这一单目标优化，发展了一个多目标优化，并得到了一个帕累托前沿。元模型的使用使得分析每个设计变量的影响成为可能。(C)2014爱思唯尔有限公司。保留所有权利。
The optimization of the nose shape of a high-speed train entering a tunnel has been performed using genetic algorithms (GA). This optimization method requires the parameterization of each optimal candidate as a design vector. The geometrical parameterization of the nose has been defined using three design variables that include the most characteristic geometrical factors affecting the compression wave generated at the entry of the train and the aerodynamic drag of the train. A large set of three-dimensional, turbulent, compressible, unsteady simulations of realistic train models have been done, and this information has been used to fit a metamodel. The metamodel is used by the GA to evaluate each optimal candidate in a more efficient way. The optimal designs that minimize the maximum pressure gradient and the aerodynamic drag are in good agreement with the literature. To complete this single-objective optimization, a multi-objective optimization has been developed, and a Pareto front has been obtained. The use of metamodels has permitted to analyze the influence of each design variable. (C) 2014 Elsevier Ltd. All rights reserved.