contributor author | Chaari, Majdi | |
contributor author | Fekih, Afef | |
contributor author | Seibi, Abdennour C. | |
contributor author | Ben Hmida, Jalel | |
date accessioned | 2019-09-18T09:02:22Z | |
date available | 2019-09-18T09:02:22Z | |
date copyright | 6/20/2019 12:00:00 AM | |
date issued | 2019 | |
identifier issn | 0098-2202 | |
identifier other | fe_141_10_101303 | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4258145 | |
description 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. | |
publisher | American Society of Mechanical Engineers (ASME) | |
title | A Generalized Reduced-Order Dynamic Model for Two-Phase Flow in Pipes | |
type | Journal Paper | |
journal volume | 141 | |
journal issue | 10 | |
journal title | Journal of Fluids Engineering | |
identifier doi | 10.1115/1.4043858 | |
journal fristpage | 101303 | |
journal lastpage | 101303-18 | |
tree | Journal of Fluids Engineering:;2019:;volume( 141 ):;issue: 010 | |
contenttype | Fulltext | |