Railway rolling noise prediction: field validation and sensitivity analysis
Railway rolling noise prediction: field validation and sensitivity analysis
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.
railway rolling noise prediction, vibration dynamics, contact mechanics, sound radiations, sensitivity analysis
109-127
Jiang, S.
3f5ff968-f1e9-40ac-9495-3f0cd9b0cd79
Meehan, P.A.
3004f4f0-edca-4032-b6de-b6e6fb436b5d
Thompson, D.J.
bca37fd3-d692-4779-b663-5916b01edae5
Jones, C.J.C.
695ac86c-2915-420c-ac72-3a86f98d3301
Jiang, S.
3f5ff968-f1e9-40ac-9495-3f0cd9b0cd79
Meehan, P.A.
3004f4f0-edca-4032-b6de-b6e6fb436b5d
Thompson, D.J.
bca37fd3-d692-4779-b663-5916b01edae5
Jones, C.J.C.
695ac86c-2915-420c-ac72-3a86f98d3301
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), .
(doi:10.1080/23248378.2013.788359).
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.
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More information
Accepted/In Press date: 19 March 2013
e-pub ahead of print date: 29 May 2013
Keywords:
railway rolling noise prediction, vibration dynamics, contact mechanics, sound radiations, sensitivity analysis
Organisations:
Dynamics Group
Identifiers
Local EPrints ID: 381751
URI: http://eprints.soton.ac.uk/id/eprint/381751
ISSN: 2324-8378
PURE UUID: c33a9a19-29ef-4745-8127-ae3599d69c24
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Date deposited: 13 Oct 2015 10:49
Last modified: 15 Mar 2024 02:53
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Author:
S. Jiang
Author:
P.A. Meehan
Author:
C.J.C. Jones
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