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Mathematical modelling and simulation of leak detection system in crude oil pipeline

A first-order differential leak detection model that accurately detects leaks in a crude oil pipeline is presented. This model incorporates a leak factor K(L) in the axial direction, which is simulated by applying the finite element method of numerical solution using COMSOL multi-physics software. A...

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Detalles Bibliográficos
Autores principales: Oseni, Wasiu Yussuf, Akangbe, Olubukola Adebowale, Abhulimen, Kingsley
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10161642/
https://www.ncbi.nlm.nih.gov/pubmed/37151706
http://dx.doi.org/10.1016/j.heliyon.2023.e15412
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author Oseni, Wasiu Yussuf
Akangbe, Olubukola Adebowale
Abhulimen, Kingsley
author_facet Oseni, Wasiu Yussuf
Akangbe, Olubukola Adebowale
Abhulimen, Kingsley
author_sort Oseni, Wasiu Yussuf
collection PubMed
description A first-order differential leak detection model that accurately detects leaks in a crude oil pipeline is presented. This model incorporates a leak factor K(L) in the axial direction, which is simulated by applying the finite element method of numerical solution using COMSOL multi-physics software. Additionally, the model includes the transport equation for turbulent kinetic energy and the rate of kinetic energy model. Eigenvalues for velocities and pressures were determined and plotted against time for various pipe segments. The system is stable when the Eigenvalue is zero, but a leak is declared when the Eigenvalue for pressure or velocity is less than one. The study shows that pressure measurements are more sensitive parameters for detecting leaks than velocity measurements, and the sinusoidal waveform characterizes leak behaviours for velocity.
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spelling pubmed-101616422023-05-06 Mathematical modelling and simulation of leak detection system in crude oil pipeline Oseni, Wasiu Yussuf Akangbe, Olubukola Adebowale Abhulimen, Kingsley Heliyon Research Article A first-order differential leak detection model that accurately detects leaks in a crude oil pipeline is presented. This model incorporates a leak factor K(L) in the axial direction, which is simulated by applying the finite element method of numerical solution using COMSOL multi-physics software. Additionally, the model includes the transport equation for turbulent kinetic energy and the rate of kinetic energy model. Eigenvalues for velocities and pressures were determined and plotted against time for various pipe segments. The system is stable when the Eigenvalue is zero, but a leak is declared when the Eigenvalue for pressure or velocity is less than one. The study shows that pressure measurements are more sensitive parameters for detecting leaks than velocity measurements, and the sinusoidal waveform characterizes leak behaviours for velocity. Elsevier 2023-04-14 /pmc/articles/PMC10161642/ /pubmed/37151706 http://dx.doi.org/10.1016/j.heliyon.2023.e15412 Text en © 2023 Published by Elsevier Ltd. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Research Article
Oseni, Wasiu Yussuf
Akangbe, Olubukola Adebowale
Abhulimen, Kingsley
Mathematical modelling and simulation of leak detection system in crude oil pipeline
title Mathematical modelling and simulation of leak detection system in crude oil pipeline
title_full Mathematical modelling and simulation of leak detection system in crude oil pipeline
title_fullStr Mathematical modelling and simulation of leak detection system in crude oil pipeline
title_full_unstemmed Mathematical modelling and simulation of leak detection system in crude oil pipeline
title_short Mathematical modelling and simulation of leak detection system in crude oil pipeline
title_sort mathematical modelling and simulation of leak detection system in crude oil pipeline
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10161642/
https://www.ncbi.nlm.nih.gov/pubmed/37151706
http://dx.doi.org/10.1016/j.heliyon.2023.e15412
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