Deep learning-based assessment of saturated flow boiling heat transfer and two-phase pressure drop for evaporating flow

Abstract

This work proposes new correlations as well as deep learning based modeling of saturated flow boiling heat transfer and two-phase pressure drops for evaporating flow. First, existing saturation flow boiling heat transfer correlations are compared to experimental database (2,500 data points) of numerous refrigerants for tube diameters ranging from 1 to 7 mm. The newly developed correlation for heat transfer outperformed the existing correlations resulting in an MAE =11.28%. For two-phase pressure drop of the evaporating flow, 1,954 measurement data points of 7 refrigerants were used, for the experimental tube diameter ranged from 0.509 to 14 mm. The newly developed correlation for pressure drop outperformed the existing correlations resulting in an MAE =19.07%. An optimal deep learning model (DL) was developed that further improved the accuracy of the prediction in terms of both heat transfer and pressure drop (R2=0.984 and MAE=4.5 % in terms of heat transfer and R2=0.994 and MAE=7.39 % in terms of two-phase pressure drop). The proposed correlations and deep learning models significantly improve microchannel prediction in terms of heat transfer and two phase pressure drop. Besides, explainable artificial intelligence highlights the dependence and interaction between various features affecting the heat transfer and pressure drop.