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A stochastic model for the speed of leak noise propagation in plastic water pipes

A stochastic model for the speed of leak noise propagation in plastic water pipes
A stochastic model for the speed of leak noise propagation in plastic water pipes
A good estimate of the speed of leak noise propagation is necessary to pinpoint the location of a leak using acoustic correlators. Models currently exist for this purpose, but they do not consider uncertainties in the pipe geometry and the properties of the pipe and soil. Using the fact that leak noise propagates as a predominantly fluid-borne wave, this paper develops a stochastic model of the speed of leak noise propagation in plastic water distribution pipes that can account for these uncertainties. The model provides confidence limits for the estimate of the wave speed related to the leak noise excitation. It is based on the mean and standard deviation of the pipe geometry as well as the pipe and soil material properties, which have strong influence on the speed in which the leak noise propagates in the pipe. Numerical examples, using parameters from water supply systems found in the field, in which the pipe is made from Medium-Density Polyethylene (MDPE) and Polyvinyl Chloride (PVC) are presented to validate the model. Monte Carlo simulations for both in-air and buried pipes are presented to check the 99.7% confidence interval. To verify that the predictions from the stochastic models give realistic results, they are compared with some measurements from different sites, in which nominal properties and tolerances for the pipe and soil properties are assumed.
0022-460X
116057
Scussel, Oscar
2d398c24-0830-4aa7-97df-065a37669cd9
Seçgin, Abdullah
3f337aff-c78b-45c2-8a7a-2511991cfe15
Brennan, M.J.
7f39b4f4-810d-49d5-be90-1656c7b8069a
Muggleton, Jennifer
2298700d-8ec7-4241-828a-1a1c5c36ecb5
Almeida, F.C.L
e892135a-349e-41ed-b116-c92bd581ca85
Scussel, Oscar
2d398c24-0830-4aa7-97df-065a37669cd9
Seçgin, Abdullah
3f337aff-c78b-45c2-8a7a-2511991cfe15
Brennan, M.J.
7f39b4f4-810d-49d5-be90-1656c7b8069a
Muggleton, Jennifer
2298700d-8ec7-4241-828a-1a1c5c36ecb5
Almeida, F.C.L
e892135a-349e-41ed-b116-c92bd581ca85

Scussel, Oscar, Seçgin, Abdullah, Brennan, M.J., Muggleton, Jennifer and Almeida, F.C.L (2021) A stochastic model for the speed of leak noise propagation in plastic water pipes. Journal of Sound and Vibration, 116057. (doi:10.1016/j.jsv.2021.116057).

Record type: Article

Abstract

A good estimate of the speed of leak noise propagation is necessary to pinpoint the location of a leak using acoustic correlators. Models currently exist for this purpose, but they do not consider uncertainties in the pipe geometry and the properties of the pipe and soil. Using the fact that leak noise propagates as a predominantly fluid-borne wave, this paper develops a stochastic model of the speed of leak noise propagation in plastic water distribution pipes that can account for these uncertainties. The model provides confidence limits for the estimate of the wave speed related to the leak noise excitation. It is based on the mean and standard deviation of the pipe geometry as well as the pipe and soil material properties, which have strong influence on the speed in which the leak noise propagates in the pipe. Numerical examples, using parameters from water supply systems found in the field, in which the pipe is made from Medium-Density Polyethylene (MDPE) and Polyvinyl Chloride (PVC) are presented to validate the model. Monte Carlo simulations for both in-air and buried pipes are presented to check the 99.7% confidence interval. To verify that the predictions from the stochastic models give realistic results, they are compared with some measurements from different sites, in which nominal properties and tolerances for the pipe and soil properties are assumed.

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More information

Accepted/In Press date: 1 March 2021
e-pub ahead of print date: 1 March 2021
Published date: 9 June 2021

Identifiers

Local EPrints ID: 447308
URI: http://eprints.soton.ac.uk/id/eprint/447308
ISSN: 0022-460X
PURE UUID: b8895094-7639-4891-8f90-a3c7f86becae

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Date deposited: 09 Mar 2021 17:31
Last modified: 17 Mar 2024 06:23

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Contributors

Author: Oscar Scussel
Author: Abdullah Seçgin
Author: M.J. Brennan
Author: F.C.L Almeida

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