Optimization of High-speed Train Wind Brake Devices using a Dynamic Weighted Hybrid Surrogate Model Based on Local Error Evaluation

With the continuous increase in the speed of high-speed trains (HSTs), traditional friction braking no longer meets the requirement for rapid stopping over short distances. Wind brake devices (WBDs) serve as a crucial supplementary mechanism to enhance braking performance. Therefore, investigating methods to improve their aerodynamic braking force under multifactor influences holds significant importance. This study presents a hybrid surrogate model (HSM) with dynamic weighting based on local error evaluation, which integrates the modeling characteristics of multiple classical surrogate models. The proposed model fulfills the fitting accuracy and adaptability requirements of surrogate models in the optimization of WBDs. Initial sampling points are generated using Optimal Latin Hypercube Sampling (OLHS), and their corresponding geometries are created through the Morph mesh deformation method. The responses of these sampling points are obtained using computational fluid dynamics (CFD) simulations. The accuracy of the numerical calculation method is validated through scaled wind tunnel experiments. Then, the NSGA-II algorithm is employed for optimization. The results indicate that the drag of the head car equipped with WBDs increases by 11.2%, while the lift decreases by 21%. Analyses of the flow field and pressure distribution further demonstrate that the optimized WBDs attenuate the large windward vortex, expand and intensify the leeward low-pressure region, and modify vortex morphology to raise flow separation and reattachment, collectively enhancing braking force and reducing lift. The application of the HSM considerably improves computational accuracy and efficiency.