Predicting Two Phase Flow Pressure Drop With CFD and ANN

Abstract

In this study, the several well known two-phase viscosity models were used for predicting two-phase flow pressure drop in a smooth tube using Computational Fluid Dynamics (CFD) software at homogenous flow conditions. Pressure drop for two different mass flux values (300 and 650 kg/m2s) for R134a with a saturation temperature of 45 °C in a smooth tube has been modeled according to the homogenous flow model and the results have been compared with the analytical formulas and experimental data from the literature. Three different average viscosity correlations were used. It is seen that the numerical results are in a good agreement with the homogenous flow model and fall in ± 30% band. Also, the results derived from the average viscosity expression are in a good agreement with the results calculated using separated two-phase flow correlations. In addition to this, Artificial Neural Networks (ANNs) were employed for predicting the pressure drop in a horizontal smooth pipe. The trained network gives the best values over the correlations with less than 1% mean relative error.