An assessment of gas void fraction prediction models in highly viscous liquid and gas two-phase vertical flows

Ribeiro, Joseph X.F., Liao, Ruiquan, Aliyu, Aliyu M. , Baba, Yahaya D., Archibong-Eso, Archibong, Ehinmowo, Adegboyega, Zilong, Liu, , and , (2020) An assessment of gas void fraction prediction models in highly viscous liquid and gas two-phase vertical flows. Journal of Natural Gas Science and Engineering, 76 . p. 103107. ISSN 1875-5100

Full content URL: https://doi.org/10.1016/j.jngse.2019.103107

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Item Type:Article
Item Status:Live Archive

Abstract

Gas void fraction plays a significant role in determination of several multiphase flow parameters. Good insight of its behaviour coupled with accurate prediction is imperative for design of efficient equipment which has the potential to translate to higher production rates in the petroleum industry. Against the background of the prevalence of higher viscous and imminent application of highly viscous liquids in the petroleum industry, air-water and air-low viscous liquid mixtures dominate gas void fraction research in vertical pipes. In this work, gas-liquid () mixtures are used to investigate the behaviour of gas void fraction in vertical pipes. The influence of superficial phase velocities and liquid viscosity are observed. Further, a combined database consisting of experimental and the reported data of Schmidt et al. (2008) is employed to evaluate the predictions of 100 existing correlations. The results indicate that the Hibiki and Ishii (2003) and Bestion (1990) correlations are the overall best and second-best performing correlations. In the absence good performing correlations for churn and annular flows, two correlations each, based on drift flux and slip ratio, are developed respectively. Predictions from these correlations show good agreement with the database and comparable performance with the overall best correlations.

Keywords:Gas void fraction, Vertical pipes, two-phase flow, Highly viscous flow
Subjects:H Engineering > H141 Fluid Mechanics
Divisions:College of Science > School of Engineering
ID Code:47969
Deposited On:07 Feb 2022 12:00

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