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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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