Prediction of Heat Transfer Coefficient in Flow Boiling over Tube Bundles Using ANFIS

Abstract

The applicability of the artificial intelligence technique called ANFIS (for adaptive neuro fuzzy inference system) to model the flow boiling heat transfer over a tube bundle is studied in this paper. The ANFIS model is trained and validated with the experimental data from literature. The heat flux, mass flux, and row height are taken as input and the flow boiling heat transfer coefficient as output. The developed model performance is evaluated in terms of performance parameters such as root mean square error, mean square error, correlation coefficient, variance accounted for, and computational time. The preceding parameters of the model are then determined for different combinations of type and number of membership functions. The model is found to predict experimental heat transfer coefficient within an error of ±5%. The developed model is also compared with the artificial neural network model and is found to be better in predicting the flow boiling heat transfer coefficient. The developed model is further used to observe the variation of heat transfer coefficient of the individual rows and bundle for intermediate value of parameters such as heat flux and mass flux that are not included in the analysis of experimental data. The analysis is able to provide complete information about variation of heat transfer coefficient of individual rows and the bundle with respect to heat flux and mass flux.