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Drag Optimization of the High-Speed Train Head using the Response Surface Method

机译:使用响应曲面法拖动高速列车头的优化

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In order to reduce the aerodynamic drag force of a high-speed train, an effective automatic optimization design method for the streamlined head of a high-speed train was established, and the optimization design for the drag reduction of the streamlined head of the high-speed train was carried out. The three dimensional parametric model of the streamlined head of the high-speed train was set up and five optimization design variables were extracted. The optimal Latin hypercube design method was used to obtain the uniform sampling points from the design space of the optimization design variables. The corresponding aerodynamic drag forces of the high-speed train were computed using a computational fluid dynamic method. The approximate computational model between the optimization design variables and the aerodynamic drag force of the high-speed train was set up using the response surface method. The error between the actual value and predictive value of the aerodynamic drag force is less than one percent, which meets the requirement of the engineering computational accuracy. In the process of the optimization, the aerodynamic drag force of the high-speed train was computed using the approximate computational model, the automatic update of the optimization design variables were achieved using the multi-island genetic algorithm. The computational time of the optimization is greatly reduced. All optimization design variables and the aerodynamic drag force show a trend of convergence with repeated iterative computation. By contrasting the aerodynamic drag force of the original streamlined head, the aerodynamic drag force of the optimized streamlined head was reduced by 3.643%.
机译:为了减少高速列车的气动阻力,建立了一个高速列车的简化头的有效自动优化设计方法,而优化设计的减阻所述高的流线型头部的高速列车进行。高速列车流线型头部的三维参数化模型成立,并提取的五个优化设计变量。使用了最佳拉丁超立方的设计方法,以获得均匀的从最优化设计变量的设计空间的采样点。高速列车的相应气动阻力,使用计算流体动力学方法计算。优化设计变量和高速列车的气动阻力之间的近似计算模型成立使用响应面法。实际值和气动阻力的预测值之间的误差小于百分之一,满足工程计算精度的要求。在优化的过程中,高速列车的气动阻力使用近似的计算模型计算,优化设计变量的自动更新采用多岛遗传算法得以实现。优化的计算时间大大缩短。所有的优化设计变量和气动阻力表现出收敛的反复迭代计算的趋势。通过对比原始流线型头部的气动阻力,优化的流线型头部的气动阻力是由3.643%降低。

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