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    A Generalized Reduced-Order Dynamic Model for Two-Phase Flow in Pipes

    Source: Journal of Fluids Engineering:;2019:;volume( 141 ):;issue: 010::page 101303
    Author:
    Chaari, Majdi
    ,
    Fekih, Afef
    ,
    Seibi, Abdennour C.
    ,
    Ben Hmida, Jalel
    DOI: 10.1115/1.4043858
    Publisher: American Society of Mechanical Engineers (ASME)
    Abstract: Real-time monitoring of pressure and flow in multiphase flow applications is a critical problem given its economic and safety impacts. Using physics-based models has long been computationally expensive due to the spatial–temporal dependency of the variables and the nonlinear nature of the governing equations. This paper proposes a new reduced-order modeling approach for transient gas–liquid flow in pipes. In the proposed approach, artificial neural networks (ANNs) are considered to predict holdup and pressure drop at steady-state from which properties of the two-phase mixture are derived. The dynamic response of the mixture is then estimated using a dissipative distributed-parameter model. The proposed approach encompasses all pipe inclination angles and flow conditions, does not require a spatial discretization of the pipe, and is numerically stable. To validate our model, we compared its dynamic response to that of OLGA©, the leading multiphase flow dynamic simulator. The obtained results showed a good agreement between both models under different pipe inclinations and various levels of gas volume fractions (GVF). In addition, the proposed model reduced the computational time by four- to sixfolds compared to OLGA©. The above attribute makes it ideal for real-time monitoring and fluid flow control applications.
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      A Generalized Reduced-Order Dynamic Model for Two-Phase Flow in Pipes

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    contributor authorChaari, Majdi
    contributor authorFekih, Afef
    contributor authorSeibi, Abdennour C.
    contributor authorBen Hmida, Jalel
    date accessioned2019-09-18T09:02:22Z
    date available2019-09-18T09:02:22Z
    date copyright6/20/2019 12:00:00 AM
    date issued2019
    identifier issn0098-2202
    identifier otherfe_141_10_101303
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4258145
    description abstractReal-time monitoring of pressure and flow in multiphase flow applications is a critical problem given its economic and safety impacts. Using physics-based models has long been computationally expensive due to the spatial–temporal dependency of the variables and the nonlinear nature of the governing equations. This paper proposes a new reduced-order modeling approach for transient gas–liquid flow in pipes. In the proposed approach, artificial neural networks (ANNs) are considered to predict holdup and pressure drop at steady-state from which properties of the two-phase mixture are derived. The dynamic response of the mixture is then estimated using a dissipative distributed-parameter model. The proposed approach encompasses all pipe inclination angles and flow conditions, does not require a spatial discretization of the pipe, and is numerically stable. To validate our model, we compared its dynamic response to that of OLGA©, the leading multiphase flow dynamic simulator. The obtained results showed a good agreement between both models under different pipe inclinations and various levels of gas volume fractions (GVF). In addition, the proposed model reduced the computational time by four- to sixfolds compared to OLGA©. The above attribute makes it ideal for real-time monitoring and fluid flow control applications.
    publisherAmerican Society of Mechanical Engineers (ASME)
    titleA Generalized Reduced-Order Dynamic Model for Two-Phase Flow in Pipes
    typeJournal Paper
    journal volume141
    journal issue10
    journal titleJournal of Fluids Engineering
    identifier doi10.1115/1.4043858
    journal fristpage101303
    journal lastpage101303-18
    treeJournal of Fluids Engineering:;2019:;volume( 141 ):;issue: 010
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
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