Keywords: surrogate model, sequential infill criterion, gradient information, high-speed train, aerodynamic multi-optimization, shape optimization.

This study proposes a sequential infill criterion (SIC) appropriate for the Kriging surrogate to address this issue. Multi-objective functions are employed to test the feasibility of constructing a surrogate model based on SIC, and the SIC surrogate model then performs multi-objective aerodynamic optimizations on the high-speed train. The findings indicate that the improvement infill criterion (EIC) that fuses the gradient information (PGEIC) surrogate model achieves the lowest generational distance (GD) and prediction error. The performance of EIC for global search, EIC for Pareto front search (PEIC), and infill criterion for Pareto front search using only gradient information (PGIC) is poor. The final PGEIC-SIC surrogate model of train aerodynamics has less than 1% prediction error for the three optimization objectives. The optimal solution reduces the aerodynamic drag force of the head car and the aerodynamic drag and lift force of the tail car by 4.15%, 3.21%, and 3.56%, respectively, compared with the original model. Furthermore, the nose and cab window heights of the optimal model have been reduced, and the lower contour line is concave. Correspondingly, the streamlined shape appears more rounded and slender.

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