Keywords: shape optimization, high-speed train, genetic algorithm, adjoint method cross-wind.

Two advanced optimization methods namely genetic algorithms (GAs) and adjoint methods have been used to optimize the nose shape of a high-speed train when it is subjected to cross-wind. In particular, the minimization of the side force at a 90-degree yaw angle cross-wind has been considered. Both methods have been extensively applied in aircraft or vehicle aerodynamic optimization, but are still in progress in train aerodynamics. A comparison of both methods has been performed to highlight the advantages and disadvantages of each one while, at the same time, to introduce the methodology for each case. Using a GA requires the necessity of constructing a surrogate model to speed up the optimization process. The information obtained from this metamodel can be used to yield insight into the performance of the design space. In the case of the adjoint method, the information obtained from the sensitivity field permits determining the regions of the geometry where a small modification of the shape might introduce a larger improvement in the train performance, and a good agreement between this information and the successive geometry modifications is observed. The same conclusions have been achieved in both cases, stressing the importance of the A-pillar roundness as the main geometrical parameter to reduce the side force.

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