Robust and general predictive models for condensation heat transfer inside conventional and mini/micro channel heat exchangers

To design, scale-up, and optimize the two-phase flow heat exchangers, accurate information about the heat transfer coefficient (HTC) is required. Here, an extensive database including 11,128 experimental data points from 80 sources, covering 37 condensing fluids over a wide range of operating conditions, were gathered for developing general and accurate models to forecast the HTC in mini/micro and conventional channel heat exchangers. The conventional intelligent approaches, namely, gaussian process regression (GPR), radial basis function (RBF), and hybrid radial basis function (HRBF) and also the least square fitting method (LSFM) were utilized for development of the new models. Firstly, the gathered data were used in evaluation of earlier two-phase HTC models, and these models showed average absolute relative error (AARE) values higher than 29%. Thus, more accurate models are necessary for these systems. Based on Pearson's correlation analysis, eight dimensionless groups were selected as the most important input parameters. The GPR model showed the best results in estimation of Nu number for the test process with AARE of 4.50%. However, RBF and HRBF models estimated the HTC with AARE values 19.41% and 24.36% for testing data points, respectively. In addition, a general explicit correlation was developed by the least square fitting method (LSFM) for estimating the HTC, with acceptable results at an AARE of 23.40% for all analyzed data. The performance of the new general correlation, GPR and the previous models were assessed for different channel sizes, flow regimes, fluid types, and operating conditions. The new models and well-known earlier ones were examined with about 372 additional data samples from four new independent sources. The proposed models showed excellent results for prediction of HTC. Finally, a sensitivity analysis was studied based on the experimental data and the outcomes of the LSFM.