Adjoint Optimization Method for Head Shape of High- ‎Speed Maglev Train

Abstract

The head shape of a high-speed maglev train was optimized in this study, based on the adjoint method, ‎and the aerodynamic drag of four optimized train models were simulated and compared using different ‎control point generation methods. The effectiveness of using the adjoint method to develop a ‎compressible model for a maglev train was verified. The results show that the adjoint matrix optimization ‎method can quickly and effectively capture the shape characteristics of the train head that are sensitive to ‎aerodynamic resistance. When the design variables of the head are not defined separately, the grid control ‎point set and surface control point set can be used to carry out the adjoint closed-loop optimization of the ‎train head shape, and the exchange control point generation method can be used to perform closed-loop ‎optimization. The results of a numerical simulation show that the optimized train model reduces ‎aerodynamic resistance by approximately 4.8%‎‎.