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Multi objective optimization of aerodynamic design of high speed railway windbreaks using Lattice Boltzmann Method and wind tunnel test results

机译:利用莱迪思·玻尔兹曼方法和风洞试验结果对高速铁路防风林的空气动力学设计进行多目标优化

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This article proposes a combination of the Lattice Boltzmann Method (LBM) and wind tunnel test results with Multi Objective Genetic Algorithm (MOGA) on aerodynamic design of high speed railway windbreaks. A two dimensional model of a high speed train with the inclusion of a variety of windbreaks are considered. Optimization methods are considered for the minimization of the aerodynamic coefficients for windbreaks of particular shapes. The searching space for the design parameters include the windbreak geometry and its position in the track. The potential flow solver is based on a modern method in computational fluid dynamics, namely the Lattice Boltzmann Method. 2D simulations based on LBM on a type of high speed train at the presence of a variety of windbreaks are studied. Results are verified through wind tunnel tests on a scaled model of the train. For optimum design, LBM simulations are combined with the Multi Objective Genetic Algorithm.
机译:本文提出了格子Boltzmann方法(LBM)和风洞测试结果与多目标遗传算法(MOGA)的结合,用于高速铁路防风林的空气动力学设计。考虑了包含各种防风林的高速列车的二维模型。为了使特定形状的防风林的空气动力学系数最小化,考虑了优化方法。设计参数的搜索空间包括防风林的几何形状及其在轨道中的位置。势流求解器基于现代的流体动力学计算方法,即格子Boltzmann方法。研究了基于LBM的二维高速列车在各种风挡下的模拟。通过风洞测试对火车的比例模型验证了结果。为了优化设计,将LBM模拟与多目标遗传算法结合在一起。

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