An optimization approach in the development of a new correlation for two-phase heat transfer coefficient of R744 in a microchannel

Abstract

To date, no single method has been found to satisfactorily predict the two-phase heat transfer coefficient of R744 refrigerant in small channels. Studies are continuously being done to obtain a coefficient with acceptable mean absolute error (MAE) which measures the difference between the predicted and experimentally determined coefficient values. It is important to have available accurate heat transfer coefficient correlation for the two-phase heat transfer coefficient so that a compact heat exchanger that maximizes device performance while reducing cost and energy needs can be designed. In this study, genetic algorithm (GA) is used as an optimization tool to achieve a more accurate correlation for R744 in a microchannel by minimizing the MAE. Over 536 sets of experimental data from previous studies were utilized, optimizing the six constants appearing in the force convective factor, F, and nucleate boiling suppression factor, S, of the selected superposition correlation. The results showed that the MAE between the newly optimized correlation and selected experimental data at all ranges of vapor quality has been successfully reduced from 38.39 to 34.40%. With more available data, the suggested method can be utilized to achieve a more accurate empirical prediction that matches well with the experimental data.