A modelling approach for noise transmission through extruded panels in railway vehicles. (23rd June 2021)

• Record Type: Journal Article
• Authors: Li, Hui; Squicciarini, Giacomo; Thompson, David; Ryue, Jungsoo; Xiao, Xinbiao; Yao, Dan; Chen, Junlin
• Is Part Of: Journal of sound and vibration. Volume 502(2021)
• Publication Date: 2021-06-23
• Subjects: 2.5D FE method -- SEA -- Extruded panel -- Sound transmission loss -- Radiation efficiency; Sound -- Periodicals; Vibration -- Periodicals; Son -- Périodiques; Vibration -- Périodiques; Sound; Vibration; Periodicals; Electronic journals; 620.205
• DOI: 10.1016/j.jsv.2021.116095
• Languages: English
• ISSNs: 0022-460X

Abstract

Abstract: The noise inside railway vehicles plays an important role for the comfort of passengers and train crew. In determining the interior noise, the noise transmission through the train floor and sidewalls is particularly important. Modern train body structures are often made of extruded aluminium panels and the current work focuses on modelling the noise radiation from, and transmission through such structures. Use is made of a statistical energy analysis (SEA) approach which is established with the support of a waveguide finite element (2.5D FE) model. The latter is used to study the vibration properties of the extruded panel that are then utilized to calibrate simple equivalent analytical models for the various components of the extrusion. These in turn are used to define the input parameters for the SEA model, e.g. the modal densities and the coupling loss factors between subsystems. With the aid of such a calibration, the predictions from the SEA model of the sound transmission loss and of the radiation efficiency of the extruded panel agree well with measurements and numerical models using the 2.5D FE/BE approach and the VA One software. By following the procedures outlined in this paper, SEA models can be established by using a simple analytical representation of different parts of the extruded panel and hence without the need to develop ad-hoc and computationally expensive numerical models.