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Article

Interfacial Friction Prediction in a Vertical Annular Two-Phase Flow Based on Support Vector Regression Machine

by 1, 1,2,* and 1
1
Department of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
2
Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou 511458, China
*
Author to whom correspondence should be addressed.
Academic Editors: Celestine Iwendi and Thippa Reddy Gadekallu
Water 2021, 13(24), 3609; https://doi.org/10.3390/w13243609
Received: 9 November 2021 / Revised: 8 December 2021 / Accepted: 9 December 2021 / Published: 15 December 2021
(This article belongs to the Special Issue AI and Deep Learning Applications for Water Management)
Accurate prediction of interfacial friction factor is critical for calculation of pressure drop and investigation of flow mechanism of vertical annular two-phase flows. Theoretical models of interfacial friction factor based on physical insight have been developed; however, these are inconvenient in engineering practice as too many parameters need to be measured. Although many researchers have proposed various empirical correlations to improve computation efficiency, there is no generally accepted simple formula. In this study, an efficient prediction model based on support vector regression machine (SVR) is proposed. Through sensitivity analysis, five factors are determined as the input parameters to train the SVR model, relative liquid film thickness, liquid Reynolds number, gas Reynolds number, liquid Froude number and gas Froude number. The interfacial friction factor is chosen as the output parameter to check the overall performance of the model. With the help of particle swarm algorithm, the optimization process is accelerated considerably, and the optimal model is obtained through iterations. Compared with other correlations, the optimal model shows the lowest average absolute error (AAE of 0.0004), lowest maximum absolute error (MAE of 0.006), lowest root mean square error (RMSE of 0.00076) and highest correlation factor (r of 0.995). The analysis using various data in the literature demonstrates its accuracy and stability in interfacial friction prediction. In summary, the proposed machine learning model is effective and can be applied to a wider range of conditions for vertical annular two-phase flows. View Full-Text
Keywords: vertical annular two-phase flow; interfacial friction factor; support vector regression machine; particle swarm algorithm vertical annular two-phase flow; interfacial friction factor; support vector regression machine; particle swarm algorithm
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MDPI and ACS Style

Liu, Q.; Feng, X.; Chen, J. Interfacial Friction Prediction in a Vertical Annular Two-Phase Flow Based on Support Vector Regression Machine. Water 2021, 13, 3609. https://doi.org/10.3390/w13243609

AMA Style

Liu Q, Feng X, Chen J. Interfacial Friction Prediction in a Vertical Annular Two-Phase Flow Based on Support Vector Regression Machine. Water. 2021; 13(24):3609. https://doi.org/10.3390/w13243609

Chicago/Turabian Style

Liu, Qiang, Xingya Feng, and Junru Chen. 2021. "Interfacial Friction Prediction in a Vertical Annular Two-Phase Flow Based on Support Vector Regression Machine" Water 13, no. 24: 3609. https://doi.org/10.3390/w13243609

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