Numerical and experimental research on natural convection condensation heat transfer

Natural convection condensation, with the advantage of high reliability and not requiring complex mechanical drive structures, is broadly used in industrial fields, such as chemical, nuclear power, automotive, etc. This work aims to investigate the heat transfer mechanism and evaluate the performance of natural convection condensation with the artificial neural network (ANN) method, correlation predictions, and the code based on the boundary theory. An empirical correlation was proposed based on the present experimental data with operating conditions in the pressure range of 0.2 MPa -0.6 MPa, subcooled temperature range of 11 K–45 K, and air mass fraction range of 0.0049–0.69. The empirical correlation was validated against a consolidated database, with 91% of the data reproduction falling within the error band of \(\pm\) $\pm$ 30%. An ANN model was put forward with training, validation, and testing using the present experimental data, which yields an error of \(\pm\) $\pm$ 5% in the present test data. When the trained model was utilized to reproduce the additional database, all the data fell within an \(\pm\) $\pm$ 11% error band. Finally, a side-by-side comparison in heat transfer coefficient reproduction was conducted among those rapidly computational methods, and the ANN model turned out to have the best performance.