Study of liquid–gas two-phase flow in horizontal pipes using high speed filming and computational fluid dynamics

López, Jorge; Pineda, Hugo; Bello, David H.; Ratkovich, Nicolás Ríos

doi:10.1016/j.expthermflusci.2016.02.013

Cited by 39 publications

(17 citation statements)

References 18 publications

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“…The R 2 showed a high correlation, i.e., 0.89 for the liquid holdup and 0.98 for the pressure gradient, which was enough to confirm the robustness of the proposed DNN (Table 4). The predictability of the pressure gradients was reliable given the high R 2 value, notwithstanding that they had a wide range from 0 to near 8000 Pa/m in this study. The predictability of the liquid holdup was better or similar to the existing closure relationships [6,8,10,11], e.g., 3.7%-24.5% [6].…”

Section: Number Of Nodes 1 (First Hidden Layer)supporting

confidence: 51%

“…non-homogeneous flow (2) In Equations (1) and (2), the no-slip liquid holdup (λ L ) is the ratio of the liquid volumetric flow rate to the total volumetric flow rate [22]. A P represents the cross-sectional area of pipe and A L is the cross-sectional area occupied by liquid in the pipe.…”

Section: Methodsmentioning

confidence: 99%

“…The experiments validated the numerical simulations with the closure relationships, as well as the mechanistic modules, e.g., OLGA (Schlumberger, Houston, USA) and Ledaflow (Kongsberg, Norway). However, the detailed designs of these numerical simulations become complicated, e.g., computational fluid dynamics, and require a large amount of computing resources and skillful user interactions [1,2].…”

Section: Introductionmentioning

confidence: 99%

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Surrogate Model with a Deep Neural Network to Evaluate Gas–Liquid Flow in a Horizontal Pipe

et al. 2020

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This study developed a data-driven surrogate model based on a deep neural network (DNN) to evaluate gas–liquid multiphase flow occurring in horizontal pipes. It estimated the liquid holdup and pressure gradient under a slip condition and different flow patterns, i.e., slug, annular, stratified flow, etc. The inputs of the surrogate modelling were related to the fluid properties and the dynamic data, e.g., superficial velocities at the inlet, while the outputs were the liquid holdup and pressure gradient observed at the outlet. The case study determined the optimal number of hidden neurons by considering the processing time and the validation error. A total of 350 experimental data were used: 279 for supervised training, 31 for validating the training performance, and 40 unknown data, not used in training and validation, were examined to forecast the liquid holdup and pressure gradient. The liquid holdups were estimated within less than 8.08% of the mean absolute percentage error, while the error of the pressure gradient was 23.76%. The R2 values confirmed the reliability of the developed model, showing 0.89 for liquid holdups and 0.98 for pressure gradients. The DNN-based surrogate model can be applicable to estimate liquid holdup and pressure gradients in a more realistic manner with a small amount of computating resources.

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Section: Number Of Nodes 1 (First Hidden Layer)supporting

confidence: 51%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Surrogate Model with a Deep Neural Network to Evaluate Gas–Liquid Flow in a Horizontal Pipe

et al. 2020

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“…Problems of liquid film flow are quite often encountered in technical applications and are discussed by many authors. Paper [1] presents the results of investigation of films for a gas-liquid two-phase flow in short horizontal pipes. The obtained results are compared with the experimental data.…”

Section: Introductionmentioning

confidence: 99%

Reducing Friction with a Liquid Film on the Body Surface

2018

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A flow of a thin layer of liquid is simulated on a flat surface of a body located in a stream of air. Liquid film on the surface of the body reduces frictional resistance and can be used as a boundary layer control element. The paper presents a mathematical model of the film flow on a half-plane, located at an angle to the horizon. The fluid flow is determined by the force of gravity and friction from the external air current. A model of an incompressible viscous fluid is used in the boundary-layer approximation. The terms of the motion equation are averaged over the film thickness according to the Leibniz rule. In the cross section of the film, a quadratic law is adopted for the distribution of the longitudinal velocity, taking into account friction on the film surface. An analytical solution of the problem is obtained in the form of series in powers of the small parameter for determining the film thickness and the average longitudinal velocity along the length of the plate. It is shown that the friction decreases with flow around a half-plane with a film of liquid on the surface.

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“…For example, work [1] presents film research findings for a two-phase gas-liquid flow in short horizontal pipes. The obtained results are compared to the experimental data.…”

Section: Introductionmentioning

confidence: 99%

Mathematical Model of the Liquid Film Flow on the Flat Surface

Klyuev¹

2017

AJAE

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Abstract:A liquid film on surface of a body decreases frictional resistance and can be used as a boundary layer control element. This article contains a mathematical model of a film flow over a half-plane, directed at an angle to the horizon. Liquid flow depends on gravity and friction with the external air flow. A model of incompressible viscous liquid near the boundary layer is used as the flow model. Summands of motion equation are averaged over the film thickness by the Leibniz rule. The square low is assumed for distribution of longitudinal velocity in the cross-section of the film with regard to the friction at the film's surface. An approximate solution of the problem is received as power series in powers of small parameter. The results are presented in a form diagrams of the film thickness and the average longitudinal velocity over the length of the plate. The mathematical flow model can be used to define flat film flow performance.

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Study of liquid–gas two-phase flow in horizontal pipes using high speed filming and computational fluid dynamics

Cited by 39 publications

References 18 publications

Surrogate Model with a Deep Neural Network to Evaluate Gas–Liquid Flow in a Horizontal Pipe

Surrogate Model with a Deep Neural Network to Evaluate Gas–Liquid Flow in a Horizontal Pipe

Reducing Friction with a Liquid Film on the Body Surface

Mathematical Model of the Liquid Film Flow on the Flat Surface

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