Mitigation Effect of Helmholtz Resonator on the Micro-Pressure Wave Amplitude of a 600-km/h Maglev Train Tunnel

Abstract

A 600-km/h maglev train can effectively close the speed gap between civil aviation and rail-based trains, thereby alleviating the conflict between the existing demand and actual capacity. However, the hazards caused by the micro-pressure wave amplitude of the tunnel that occurs when the train is running at higher speeds are also unacceptable. At this stage, mitigation measures to control the amplitude of micro-pressure waves generated by maglev trains at 600 km/h within reasonable limits are urgent to develop new mitigation measures. In this study, a three-dimensional, compressible, unsteady SST K–ω equation turbulence model, and an overlapping grid technique were used to investigate the mechanism and mitigation effect of Helmholtz resonators with different arrangement schemes on the micro-pressure wave amplitude at a tunnel exit in conjunction with a 600-km/h maglev train dynamic model test. The results of the study showed that a pressure wave forms when the train enters the tunnel and passes through the Helmholtz resonator. This in turn leads to resonance of air column at its neck, which causes pressure wave energy dissipation as the incident wave frequency is in the resonator band. This suppresses the rise of the initial compressional wave gradient, resulting in an effective reduction in the micro-pressure wave amplitude at the tunnel exit. Compared to conventional tunnels, the Helmholtz resonator scheme with a 94-cavity new tunnel resulted in a 31.87% reduction in the micro-pressure wave amplitude at 20 m from the tunnel exit but a 16.69% increase in the maximum pressure at the tunnel wall. After the Helmholtz resonators were arranged according to the 72-cavity optimization scheme, the maximum pressure at the tunnel wall decreased by 10.57% when compared with that before optimization. However, the micro-pressure wave mitigation effect at 20 m from the tunnel exit did not significantly differ from that before the optimization.