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Research Article

Numerical study on mixing flow behavior in gas-liquid ejector

Xiaodong Wang1( )He Li1Jingliang Dong2Jiaqi Wu1Ji-yuan Tu2
School of Mechanical Engineering & Automation, Northeastern University, Shenyang 110819, China
School of Engineering, RMIT University, Victoria 3083, Australia
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Abstract

A multiphase mathematic model based on realizable k-ε turbulence model for subsonic flow was presented to investigate the mixing flow behaviors between gas and water in the gas-liquid ejector. The simulation was carried out to predict the pumping performance of the ejector by a commercial computational code ANSYS-FLUENT 15.0. General agreements between the predicted results and experimental data validated the present theoretical model. Using the present approach, the pressure, velocity, and turbulence intensity distribution along center-line and contours of gas and liquid volume fraction profiles were predicted. It is found that the mixing process between gas and water in ejector can be divided into three periods, co-axial flow, mixing shock flow, and bubble flow. The prediction results show that the mixing shock is a dominant position in affecting the mixing flow behavior in gas-liquid ejector and the ejector’s performances.

References

 
B. Cai, 2005. Analysis and experimental study on incipient cavitation within jet pumps. Wuhan, China: Wuhan University.
 
P. Cramers,, A. Beenackers, 2001. Influence of the ejector configuration, scale and the gas density on the mass transfer characteristics of gas-liquid ejectors. Chem Eng J, 82: 131-141.
 
M. I. Kim,, O. Sin Kim,, D. H. Lee,, S. Done Kim, 2007. Numerical and experimental investigations of gas-liquid dispersion in an ejector. Chem Eng Sci, 62: 7133-7139.
 
B. E. Launder,, D. B. Spalding, 1974. The numerical computation of turbulent flows. Comp Meth Appl Mech Eng, 3: 269-289.
 
M. Opletal,, P. Novotný,, V. Linek,, T. Moucha,, M. Kordač, 2018. Gas suction and mass transfer in gas-liquid up-flow ejector loop reactors. Effect of nozzle and ejector geometry. Chem Eng J, 353: 436-452.
 
K. Pianthong,, W. Seehanam,, M. Behnia,, T. Sriveerakul,, S. Aphornratana, 2007. Investigation and improvement of ejector refrigeration system using computational fluid dynamics technique. Energ Convers Manage, 48: 2556-2564.
 
F. Rahman,, D. B. Umesh,, D. Subbarao,, M. Ramasamy, 2010. Enhancement of entrainment rates in liquid-gas ejectors. Chem Eng Process, 49: 1128-1135.
 
R. Senthil Kumar,, S. Kumaraswamy,, A. Mani, 2007. Experimental investigations on a two-phase jet pump used in desalination systems. Desalination, 204: 437-447.
 
D. Sharma,, A. Patwardhan,, V. Ranade, 2018. Effect of turbulent dispersion on hydrodynamic characteristics in a liquid jet ejector. Energy, 164: 10-20.
 
T. H. Shih,, W. W. Liou,, A. Shabbir,, Z. Yang,, J. Zhu, 1995. A new k-ε eddy viscosity model for high Reynolds number turbulent flows. Comput Fluids, 24: 227-238.
 
X. Song,, J. H. Park,, S. G. Kim,, Y. C. Park, 2013. Performance comparison and erosion prediction of jet pumps by using a numerical method. Math Comp Model, 57: 245-253.
 
J. Wu, 2017. Multi parameter analysis of gas-liquid jet pump pumping performance. Shenyang, China: Northeastern University.
 
R. L. Yadav,, A. W. Patwardhan, 2008. Design aspects of ejectors: Effects of suction chamber geometry. Chem Eng Sci, 63: 3886-3897.
 
P. Zheng,, B. Li,, J. Qin, 2018. CFD simulation of two-phase ejector performance influenced by different operation conditions. Energy, 155: 1129-1145.
Experimental and Computational Multiphase Flow
Pages 108-112
Cite this article:
Wang X, Li H, Dong J, et al. Numerical study on mixing flow behavior in gas-liquid ejector. Experimental and Computational Multiphase Flow, 2021, 3(2): 108-112. https://doi.org/10.1007/s42757-020-0069-z

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Received: 23 May 2019
Revised: 28 June 2019
Accepted: 08 April 2020
Published: 25 May 2020
© Tsinghua University Press 2020
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