The interior noise of a high-speed train due to the external flow disturbance is more than ever a major problem for product developers to consider during a design state. Since the external surface pressure field induces wall panel vibration of a high-speed train, which in turn generates the interior sound, the first step for low interior noise design is to characterize the surface pressure fluctuations due to external disturbance. In this study, the external flow field of a high-speed train cruising at a speed of 300 km/h in open-field and tunnel are numerically investigated using high-resolution compressible LES (large eddy simulation) techniques, with a focus on characterizing fluctuating surface pressure field according to surrounding conditions of the cruising train, i.e., open-field and tunnel. First, compressible LES schemes with high-resolution grids were employed to accurately predict the exterior flow and acoustic fields around a high-speed train simultaneously. Then, the predicted fluctuating pressure field on the wall panel surface of a train was decomposed into incompressible and compressible ones using the wavenumber-frequency transform, given that the incompressible pressure wave induced by the turbulent eddies within the boundary layer is transported approximately at the mean flow and the compressible pressure wave propagated at the vector sum of the sound speed and the mean flow velocity. Lastly, the power levels due to each pressure field were computed and compared between open-field and tunnel. It was found that there is no significant difference in the power levels of incompressible surface pressure fluctuations between the two cases. However, the decomposed compressible one in the tunnel case is higher by about 2~10 dB than in the open-field case. This result reveals that the increased interior sound of the high-speed train running in a tunnel is due to the compressible surface pressure field.
Numerical Analysis and Characterization of Surface Pressure Fluctuations of High-Speed Trains Using Wavenumber–Frequency Analysis
