Neural Network Model for Boiling Heat Transfer of R22 and Its Alternative Refrigerants inside Horizontal Smooth Tubes

Neural Network Model for Boiling Heat Transfer of R22 and Its Alternative Refrigerants inside Horizontal Smooth Tubes

Accurate prediction of refrigerant boiling heat transfer coefficients is important for the design of evaporators. The generalized correlations have different forms, and could not provide satisfactory results for R22 and its alternative refrigerants R134a, R407C and R410A. This study proposes to use artificial neural network (ANNs) as a generalized correlation model, selects the input parameters of ANNs on the basis of the dimensionless parameter groups of existing correlations, and correlates the in-tube boiling heat transfer coefficients of the above four refrigerants. The results show that the ANNs model with the input and output based on the Liu-Winterton correlation has the best result. The root-mean-square deviations in training and test are 15.5% and 20. 2% respectively, and approximately 85 % of the deviations are within ± 20%, which is much better than that of the existing generalized correlations.