A Framework To Predict The Airborne Noise Inside Railway Vehicles With Application To Rolling Noise

A framework is described for predicting the airborne noise inside railway vehicles which is applied to rolling noise sources. Statistical energy analysis (SEA) is used to predict the interior noise by subdividing the train cabin into several subsystems. The dissipation loss factors are obtained from the measured reverberation time in the train cabin. The power input to the interior SEA model is obtained from the external noise sources by multiplying the incident sound power on the external surfaces with measured transmission coefficients of the train floor and sidewalls. The sound power incident on the train floor is calculated by using an equivalent source model for the wheels and track together with an SEA model of the region below the floor. The incident sound power on the sides is obtained by using a waveguide boundary element (2.5D BE) method. The procedure is applied to a Spanish metro train vehicle running in the open field for which rolling noise is the main external noise source. The procedure is verified by field measurements of sound pressure beneath the carriage, on the sidewalls and inside the vehicle. The sensitivity of the results to changes in interior absorption is also studied, including the effect of passengers. (C) 2021 Elsevier Ltd. All rights reserved.