Data-driven rapid prediction model for aerodynamic force of high-speed train with arbitrary streamlined head

Abstract

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.