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

• Published in: Water (Basel) Vol. 13; no. 24; p. 3609
• Main Authors: Liu, Qiang, Feng, Xingya, Chen, Junru
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2021
• Subjects: Algorithms; Correlation coefficients; Film thickness; Flow velocity; Fluid flow; Friction; Friction factor; Froude number; Gas flow; Interface stability; interfacial friction factor; Learning algorithms; Machine learning; Mathematical models; Multiphase flow; Optimization; Parameter sensitivity; particle swarm algorithm; Pipelines; Prediction models; Pressure drop; Reynolds number; Root-mean-square errors; Sensitivity analysis; Shear stress; Support vector machines; support vector regression machine; Two phase flow; vertical annular two-phase flow
• ISSN: 2073-4441; 2073-4441
• DOI: 10.3390/w13243609

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.