Railway rolling noise prediction: field validation and sensitivity analysis

Jiang, S., Meehan, P. A., Thompson, D. J. and Jones, C. J. C. (2013) Railway rolling noise prediction: field validation and sensitivity analysis. International Journal of Rail Transportation, 1 1-2: 110-127. doi:10.1080/23248378.2013.788359

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Author Jiang, S.
Meehan, P. A.
Thompson, D. J.
Jones, C. J. C.
Title Railway rolling noise prediction: field validation and sensitivity analysis
Journal name International Journal of Rail Transportation   Check publisher's open access policy
ISSN 2324-8378
Publication date 2013
Sub-type Article (original research)
DOI 10.1080/23248378.2013.788359
Volume 1
Issue 1-2
Start page 110
End page 127
Total pages 18
Place of publication Abingdon, Oxfordshire, United Kingdom
Publisher Taylor & Francis
Collection year 2014
Language eng
Formatted abstract
The Railway Rolling Noise Prediction Software (RRNPS) is a model for predicting the sound pressure levels (SPLs) during a train passage due to wheel/rail roughness, based on vibration dynamics, contact mechanics and sound radiation modules. Similar software has been developed previously, in particular the Track–Wheel Interaction Noise Software (TWINS) model, and some field validation has been done under European and Japanese conditions. In this article, the RRPNS is used to model a typical railway rolling noise situation in Australia and compared with detailed field experimental results for validation purposes. A series of field measurements were taken at a narrow track gauge testing site in Australia. Comparisons between simulations and measurements have shown that this software model gives reliable predictions in terms of overall A-weighted SPL and noise spectrum. In addition, a sensitivity analysis of the model was carried out to investigate the effect of speed, normal load, ballast vertical stiffness, rail pad vertical stiffness and rail cross receptance factor on railway rolling noise. This article extends the range of conditions for which the software model has been validated and gains some confidence in its use. It also provides some insight into model-based methods to control and mitigate railway noise.
Keyword Railway rolling noise prediction
Vibration dynamics
Contact mechanics
Sound radiations
Sensitivity analysis
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Mechanical & Mining Engineering Publications
Official 2014 Collection
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Created: Thu, 30 Jan 2014, 13:28:18 EST by Deanna Mahony on behalf of School of Mechanical and Mining Engineering