Machine learning algorithms to predict flow boiling pressure drop in mini/micro-channels based on universal consolidated data

Abstract Two-phase flow in mini/micro-channels can meet the high heat dissipation requirements of many state-of-the-art cooling solutions. However, there is lack of accurate universal methods for predicting parameters like pressure drop in these configurations. Conventional ways of predicting pressure drop employ either Homogeneous Equilibrium Model (HEM) or semi-empirical correlations. This current study leverages the availability of data collected over the past few decades to build several machine learning models to demonstrate the efficacy and ease of building and deploying such models. A consolidated database of 2787 data points for flow boiling pressure drop in mini/micro-channels is amassed from 21 sources that includes 10 working fluid, reduced pressures of 0.0006 –0.7766, hydraulic diameters of 0.15–5.35 mm, mass velocities of 33.1