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.
A Generalized Reduced-Order Dynamic Model for Two-Phase Flow in Pipes
Contributed by the Fluids Engineering Division of ASME for publication in the JOURNAL OF FLUIDS ENGINEERING. Manuscript received December 30, 2018; final manuscript received May 19, 2019; published online June 20, 2019. Assoc. Editor: Riccardo Mereu.
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Chaari, M., Fekih, A., Seibi, A. C., and Ben Hmida, J. (June 20, 2019). "A Generalized Reduced-Order Dynamic Model for Two-Phase Flow in Pipes." ASME. J. Fluids Eng. October 2019; 141(10): 101303. https://doi.org/10.1115/1.4043858
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