Research Papers: Multiphase Flows

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

[+] Author and Article Information
Majdi Chaari

Department of Electrical and
Computer Engineering,
University of Louisiana at Lafayette,
P.O. Box 43890,
Lafayette, LA 70504-3890
e-mail: Mxc0798@louisiana.edu

Afef Fekih

Department of Electrical and
Computer Engineering,
University of Louisiana at Lafayette,
P.O. Box 43890,
Lafayette, LA 70504-3890
e-mail: afef.fekih@louisiana.edu

Abdennour C. Seibi

Department of Petroleum Engineering,
University of Louisiana at Lafayette,
P.O. Box 44690,
Lafayette, LA 70504
e-mail: Acs9955@louisiana.edu

Jalel Ben Hmida

Department of Mechanical Engineering,
University of Louisiana at Lafayette,
P.O. Box 43678,
Lafayette, LA 70504
e-mail: Jxb9360@louisiana.edu

1Corresponding author.

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.

J. Fluids Eng 141(10), 101303 (Jun 20, 2019) (18 pages) Paper No: FE-18-1872; doi: 10.1115/1.4043858 History: Received December 30, 2018; Revised May 19, 2019

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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Fig. 1

A three-layered perceptron architecture

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Fig. 2

Flowchart of the GA–ANN approach

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Fig. 3

Evolution of the best fitness: (a) holdup and (b) pressure gradient

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Fig. 4

Parity charts of measured and predicted holdup: (a) proposed model, (b) Beggs and Brill's model, and (c) Petalas and Aziz's model

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Fig. 5

Parity charts of measured and predicted pressure gradient: (a) proposed model, (b) Beggs and Brill's model, and (c) Petalas and Aziz's model

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Fig. 6

Transmission line model

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Fig. 7

Low-frequency corrections

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Fig. 8

Lumped turbulent resistance

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Fig. 9

Transcendental transfer functions for various Reynolds numbers

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Fig. 10

Pipe inclination effect on the dynamic behavior

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Fig. 11

Fluid compressibility effect on the dynamic behavior

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Fig. 12

GVF effect on the inlet pressure dynamic behavior

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Fig. 13

Truncation order effect on the inlet pressure transient response

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Fig. 14

Inlet pressure transient response to inlet flow rate step (θ = 0 deg): (a) GVF = 10% and (b) GVF = 20%

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Fig. 15

Inlet pressure transient response to inlet flow rate step (θ = 10 deg): (a) GVF = 10% and (b) GVF = 20%

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Fig. 16

Inlet pressure transient response to inlet flow rate step (θ = 20 deg): (a) GVF = 10% and (b) GVF = 20%

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Fig. 17

Inlet pressure transient response to inlet flow rate step (GVF = 10%, θ = 90 deg)

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Fig. 18

Inlet pressure transient response to inlet flow rate step (GVF = 10%): (a) θ = −10 deg, (b) θ = −20 deg, and (c) θ = −90 deg

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Fig. 19

Computational time versus hyperbolic functions truncation order



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