Generalized neural network correlation for flow boiling heat transfer of R22 and its alternative refrigerants inside horizontal smooth tubes

The correct prediction of refrigerant boiling heat transfer performance is important for the design of evaporators. A generalized neural network correlation for boiling heat transfer coefficient of R22 and its alternative refrigerants R134a, R407C and R410A inside horizontal smooth tubes has been developed in this paper. Four kinds of dimensionless parameter groups from existing generalized correlations are selected as the input of neural network, while the Nusselt number is used as the output. Three-layer perceptron is employed as the universal approximator to build the relationship between the input and output parameters. The neuron number of hidden layer is determined by the performance of model accuracy and the standard sensitivity analysis. The experimental data of the four refrigerants in open literatures are used for correlation. The results show that the input parameter group based on the Gungor–Winterton correlation is better than the other three groups. Compared with the experimental data, the average, mean and root-mean-square deviations of the trained neural network are 2.5%, 13.0% and 20.3%, respectively, and approximately 74% of the deviations are within ±20%, which is much better than that of the existing generalized correlations.