How can the temperature affect the performance of a classical pipeline model when plastic pipes are used?

Begovich, O.; Pizano-Moreno, A.; Garcia-Malacara, E.; Besançon, Gildas

doi:10.1109/iceee.2011.6106606

Cited by 6 publications

(6 citation statements)

References 10 publications

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“…In this study, the effects of temperature changes on fluid properties such as friction factor, water density and bulk modulus were considered. In a related study of Begovich et al [183], the effects of fluid temperature on performance of a classical flow modeling on plastic pipe were also investigated, but their study was only limited to influence of temperature variations in the water viscosity and friction factor. Table 2 provides a summary of the strengths and weaknesses of the interior leak detection approaches.…”

Section: Interior/computational Methodsmentioning

confidence: 99%

“…Environmental uncertainties can affect the properties of fluids in pipelines such as fluid density, viscosity, friction factor, etc. Although, some studies have provided insights for the development of temperature-dependent flow models [182,183], these investigations are only limited to short flow models in which spatial changes of the temperature can be neglected. A robust temperature variation compensation approach will provide additional advantages for fluid flow modeling.…”

Section: Research Gaps and Open Issuesmentioning

confidence: 99%

See 1 more Smart Citation

Recent Advances in Pipeline Monitoring and Oil Leakage Detection Technologies: Principles and Approaches

Adegboye

Fung

Karnik

2019

Sensors

291

144

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Pipelines are widely used for the transportation of hydrocarbon fluids over millions of miles all over the world. The structures of the pipelines are designed to withstand several environmental loading conditions to ensure safe and reliable distribution from point of production to the shore or distribution depot. However, leaks in pipeline networks are one of the major causes of innumerable losses in pipeline operators and nature. Incidents of pipeline failure can result in serious ecological disasters, human casualties and financial loss. In order to avoid such menace and maintain safe and reliable pipeline infrastructure, substantial research efforts have been devoted to implementing pipeline leak detection and localisation using different approaches. This paper discusses pipeline leakage detection technologies and summarises the state-of-the-art achievements. Different leakage detection and localisation in pipeline systems are reviewed and their strengths and weaknesses are highlighted. Comparative performance analysis is performed to provide a guide in determining which leak detection method is appropriate for particular operating settings. In addition, research gaps and open issues for development of reliable pipeline leakage detection systems are discussed.

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Section: Interior/computational Methodsmentioning

confidence: 99%

Section: Research Gaps and Open Issuesmentioning

confidence: 99%

Recent Advances in Pipeline Monitoring and Oil Leakage Detection Technologies: Principles and Approaches

Adegboye

Fung

Karnik

2019

Sensors

291

144

View full text Add to dashboard Cite

show abstract

“…[11]), sıcaklık (Begovich vd. [12]) veya elektro manyetik dalga (Goh vd. [13]) verileri analiz edilmektedir.…”

Section: Sizinti Tespi̇t Yöntemleri̇ (Leakage Detection Methods)unclassified

“…Sızıntı konum tespitinde ses verisine ek olarak basınç değişimlerinin (Ben-Mansour vd. [11]), ısı değişimlerinin (Begovich [12]), fiber optik kablodaki ışığa ait zaman domeni verilerinin (Rajeev vd. [20]), elektromanyetik verilerin analizinin gerçekleştirildiği (Goh vd.…”

Section: Sizinti Tespi̇t Yöntemleri̇ (Leakage Detection Methods)unclassified

Su boru hatlarında sızıntı konum tespiti için genişletilmiş kalman filtresi tabanlı IMU sensör füzyonu uygulaması

Akkaya¹,

Talu²

2017

Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi

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“…Some studies have considered the temperature effect on LDI algorithms, formally discussed in [14], [15] on the basis of simulations. In those works, it has been shown that the time invariant nonlinear model normally used to design LDI algorithms can introduce errors when the pipe in question is a plastic one exposed to temperature changes.…”

Section: Introductionmentioning

confidence: 99%

Leak isolation based on Extended Kalman Filter in a plastic pipeline under temperature variations with real-data validation

Delgado-Aguiñaga

Besançon

Begovich

2015

2015 23rd Mediterranean Conference on Control and Automation (MED)

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The present work is motivated by the purpose of considering a more realistic scenario than in former studies on the problem of leak isolation within a plastic pipeline, when the water can be affected by temperature changes. In order to address this situation, a state observer approach based on a model including temperature effect and an Extended Kalman Filter is proposed. Noting indeed that temperature affects some equivalent straight length of the pipe, which is used in the model, the observer estimates it together with the leak coefficients. This approach only considers head pressure and flow rate measurements coming from pipeline ends, likewise, water temperature measurement at upstream tank. Results with real data obtained from a pipeline prototype are shown in two different ways in order to illustrate the performance of proposed leak isolation system, as compared to the traditional approaches found at literature.

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How can the temperature affect the performance of a classical pipeline model when plastic pipes are used?

Cited by 6 publications

References 10 publications

Recent Advances in Pipeline Monitoring and Oil Leakage Detection Technologies: Principles and Approaches

Recent Advances in Pipeline Monitoring and Oil Leakage Detection Technologies: Principles and Approaches

Su boru hatlarında sızıntı konum tespiti için genişletilmiş kalman filtresi tabanlı IMU sensör füzyonu uygulaması

Leak isolation based on Extended Kalman Filter in a plastic pipeline under temperature variations with real-data validation

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