A mechanistic model in annular flow in microchannel tube for predicting heat transfer coefficient and pressure gradient

A mechanistic model that focuses on annular flow is proposed in this work. The new model predicts pressure gradient, void fraction, and heat transfer coefficient in annular flow in a microchannel tube. Six fluids, namely R32, R1234yf, R134a, R1234ze(E), R1233zd(E), and R1336mzz(Z) were tested in previous studies. The tested fluids have a wide range of properties. The results of these six fluids are reviewed and compared to models in the literature. The existing heat transfer models fail to predict the measure-ments accurately. The proposed model starts from the shear balance at the interface of liquid film and vapor core. The new model uses film thickness to calculate the film Reynolds number. The Film Nus-selt number is then calculated. A database approach is adopted to enhance prediction accuracy. Based on the measurements in the database, the model has an MAE of 11.7%, and ME of 0.5% when predicting the heat transfer coefficient. Li and Hrnjak (2022), a flow pattern map based on the same fluids and the same facility, is used to determine the flow pattern. The model is compared to measurements from var-ied sources in the literature, and it also shows good accuracy. The model can also be extended to other flow patterns in a microchannel evaporator.(c) 2022 Elsevier Ltd. All rights reserved.