Optimization Method of Key Parameters for Aerodynamic Effect of High-Speed Railway Tunnel Considering Economy

Taking a high-speed railway line with reserved speed condition of 400 km · h-1 as the research context, based on surrogate models and optimization ideas, the optimization method of key parameters for the aerodynamic effects of high-speed railway tunnels considering economy is proposed from the system design level. Firstly, a numerical model of aerodynamic effects of the tunnel is established by using fluid dynamics software, and the validity of the numerical model is verified based on the measured data of trains passing through the tunnel at a speed of 300 km·h-1. Then, a Kriging surrogate model with a 3 s extreme value of air pressure variation inside the train passing through the tunnel at a speed of 400 km·h-1 is constructed by using the tunnel clearance area and the vehicle dynamic sealing index as variables and relying on the data of the existing 350 km·h-1 high-speed railway. Finally, an optimization model of the key parameters for the aerodynamic effects of high-speed railway tunnels considering economy is constructed with construction cost as the objective function, and a cuckoo search algorithm is designed to solve the optimal tunnel clearance area and vehicle dynamic sealing index that satisfy the comfort criteria. The results show that the relative root-mean-square error of what the surrogate model predicates is 0. 59% and the correlation coefficient is 0. 999 9, which can accurately describe the nonlinear relationship between the 3 s extreme value of air pressure variation inside the vehicle and the dynamic sealing index of the vehicle, as well as the tunnel clearance area of the tunnel. For the 400 km·h-1 high-speed railway, the tunnel clearance area with the lowest construction cost is 100 m2, and the dynamic sealing index of the vehicle is 11 s. At this time, the 3 s extreme value of air pressure variation inside the vehicle is 1 163. 4 Pa, meeting the requirements of aerodynamic comfort. © 2023 Chinese Academy of Railway Sciences. All rights reserved.