Due to the complicated geometric shape, it's difficult to precisely obtain the aerodynamic force of high-speed trains. Taking numerical and experimental data as the training data, the present work proposed a data-driven rapid prediction model to solve this problem, which utilized the Support Vector Machine (SVM) model to construct a nonlinear implicit mapping between design variables and aerodynamic forces of high-speed train. Within this framework, it is a key issue to achieve the consistency and auto-extraction of design variables for any given streamlined shape. A general parameterization method for the streamlined shape which adopted the idea of step-by-step modeling has been proposed. Taking aerodynamic drag as the prediction objective, the effectiveness of the model was verified. The results show that the proposed model can be successfully used for performance evaluation of high-speed trains. Keeping a comparable prediction accuracy with numerical simulations, the efficiency of the rapid prediction model can be improved by more than 90%. With the enrichment of data for the training set, the prediction accuracy of the rapid prediction model can be continuously improved. Current study provides a new approach for aerodynamic evaluation of high-speed trains and can be beneficial to corresponding engineering design departments.

由于几何形状复杂，很难精确获得高速列车的气动力。本工作以数值和实验数据为训练数据，提出了一种数据驱动的快速预测模型来解决该问题，该模型利用支持向量机（SVM）模型构建设计变量与高空气动力之间的非线性隐式映射。 -高速列车。在这个框架内，实现任何给定流线型的设计变量的一致性和自动提取是一个关键问题。采用逐步建模的思想，提出了一种通用的流线形参数化方法。以气动阻力为预测目标，验证了模型的有效性。结果表明，所提模型可成功用于高速列车性能评价。在保持与数值模拟相当的预测精度的情况下，快速预测模型的效率可提高90%以上。随着训练集数据的丰富，可以不断提高快速预测模型的预测精度。目前的研究为高速列车的气动评估提供了一种新的途径，对相应的工程设计部门具有一定的借鉴意义。可以不断提高快速预测模型的预测精度。目前的研究为高速列车的气动评估提供了一种新的途径，对相应的工程设计部门具有一定的借鉴意义。可以不断提高快速预测模型的预测精度。目前的研究为高速列车的气动评估提供了一种新的途径，对相应的工程设计部门具有一定的借鉴意义。