Multi-objective Aerodynamic Optimization on Head Shape of High-Speed Train Using Kriging Surrogate Model with Hybrid Infill Criterion
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摘要:
在高速列车多目标气动优化设计中，结合传统加点准则建立的代理模型在初始抽样样本点较少时寻优效率较低. 为提高优化效率，本文融合改善期望加点准则（EIC）及Pareto前沿解加点准则（PIC），提出混合加点准则（HIC）方法，并基于HIC方法建立Kriging代理模型，以高速列车头车气动阻力、尾车气动阻力及尾车气动升力最小为优化目标，开展高速列车头型多目标气动优化研究；并以Branin单目标测试函数和Poloni多目标测试函数为例，对比分析EIC、PIC和HIC 3种代理模型的收敛速度. 结果表明：单目标优化中，相比于EIC和HIC代理模型，HIC代理模型的寻优效率提高50.0%；多目标优化中，与PIC代理模型相比，HIC代理模型的效率提高62.5%；采用HIC代理模型开展头型多目标气动优化得到的最优解模型与原始模型相比，高速列车头车气动阻力、尾车气动阻力和尾车气动升力分别降低1.6%、1.7%和3.0%；最优解模型的鼻尖高度、车钩区域高度及司机室视窗高度都有所降低，2条横向轮廓线内缩.
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关键词:
- 代理模型 /
- 混合加点准则（HIC） /
- 高速列车 /
- 气动优化
Abstract:In the multi-objective aerodynamic optimization design of high-speed trains, the optimization efficiency of the surrogate model established using the traditional infill criterion is low when the initial sample points are few. To this end, a hybrid infill criterion (HIC) was proposed by combining the improved expectation infill criterion (EIC) and the Pareto solution infill criterion (PIC). Meanwhile, a Kriging surrogate model was established using the HIC method, and multi-objective aerodynamic optimization on the head shape of the high-speed train was conducted, with the minimum aerodynamic drag force of the leading car, the minimum aerodynamic drag and lift force of the rear car as the objectives. The single-objective Branin test function and the multi-objective Poloni test function were taken as examples, and the convergence speed of EIC, PIC, and HIC surrogate models was compared. The results show that the optimization efficiency of the HIC surrogate model is improved by 50.0% compared with the EIC and PIC surrogate models in the single-objective optimization. For the multi-objective test function, the efficiency of the HIC surrogate model is improved by 62.5% compared with the PIC surrogate model. Moreover, the HIC surrogate model is used to carry out the multi-objective aerodynamic optimization of the head shape of the high-speed train, and the optimal solution model obtained reduces the above three objectives respectively by 1.6%, 1.7%, and 3.0% compared with the original model. The heights of the nose, the coupler area, and the cab window of the optimal solution are all reduced. Meanwhile, the two lateral contour lines are retracted.
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图 1 单目标Branin函数最优解误差收敛历程
Figure 1. Error convergence process of optimal solution for single-objective Branin function
图 6 列车头型多目标优化收敛历程
Figure 6. Convergence process of multi-objective optimization of high-speed train head shape
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