Numerical simulation of pressure waves induced by high-speed maglev trains passing through tunnels

Abstract

This paper aims to determine the proper cross-sectional area for tunnels on high-speed maglev lines and support the optimization of the airtightness design for high-speed maglev trains. For these purposes, the pressure fluctuations in the tunnel operation of two maglev trains with the speed up to 650km/h were simulated using the 1D compressible unsteady non-homentropic flow model and the method of characteristics. The simulation results were compared to those from the reduced-scale test in Japan and full-scale test in China. The comparison proves that our 1D model is a rational and efficient tool for pressure wave prediction. Through repeated computations of the 1D model, the critical tunnel lengths were obtained. Then, the effects of tunnel length, tunnel cross-sectional area, train speed and train length on maximum pressure variation were identified one by one. The research findings provide a valuable reference for the tunnel and train design on high-speed maglev lines.

Investigation of Wall Function Effects on Aerodynamic Characteristics of Turbulent Flow Around a Simplified High-Speed Train

“…The aerodynamic moment and the lateral forces for the mentioned two case were illustrated. In 2018, Mei et al [24] investigated the pressure waves generated by a maglev high-speed train passing through tunnels. In the following, the obtained results were compared with the experimental data extracted by various cases.…”

“…According to [24], it is favorable that the grid refinement ratio to be greater than 1.3. In this model, r=1.4 is selected (Eq.…”

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

“…The aerodynamic problems of high-speed trains are closely related to the air flow around the vehicles. In the past decade, several researchers have carried out extensive research on high-speed trains and maglev air flow characteristics under different scenarios (e.g., Hemida and Krajnovic 2010;Baker 2010;Krajnovic et al 2012;Huang et al 2019;Jia and Mei 2018). Once the flow field structure has been measured, it is possible to carry out targeted optimization.…”