Pressure drop for subcooled water boiling in microchannels under high heat fluxes: Experiments and predictions from artificial neural networks

Abstract

Subcooled flow boiling in microchannels holds considerable promise in compact cooling applications. This study reports experimental measurements of the pressure drop of subcooled flow boiling of water in microchannels with inner diameters ranging from 0.5875 mm to 2 mm. The experiments are conducted over a range of operating parameters, including q = 3.0–10 MW/m2, p = 3.0–5.0 MPa, and G = 2000–10,000 kg/(m2∙s). The influences of diameter, heated length, and operating parameters are comprehensively analyzed. The results indicate that an increase in heat flux and pressure leads to an advancement of the pressure drop under the same thermodynamic vapor quality. Furthermore, a multi-layer artificial neural networks model optimized by a genetic algorithm is established using over 1000 experimental data. This model is employed to predict the subcooled boiling pressure drop and determine the optimal dimensionless parameter combination for developing a new pressure drop empirical correlation. The optimal combination of parameters includes Ja, ρl/ρg, Lsb/Lsat, Co and We, and the new correlation is proposed to calculate subcooled boiling pressure drop for high-heat-flux and microchannels applications, exhibiting a mean absolute error (MAE) of 10.64%. The results provide a novel approach that integrates machine learning techniques with empirical correlation, aiming to meet the requirements of the design optimization for thermal management solutions.