Prediction of thermal and fluid flow characteristics in helically coiled tubes using ANFIS and GA based correlations

This study introduces the ability of Adaptive Neuro-Fuzzy Inference System (ANFIS) and genetic algorithm (GA) based correlations for estimating the hydrodynamics and heat transfer characteristics in coiled tubes. The experimental data related to the heat transfer and pressure drop in helically coiled tubes with deferent geometrical parameters (coil diameter and pitch) were used. In the experiments, hot water was passed in the coiled tubes, which were placed in a cold bath. Two ANFIS models were developed for predicting the Nusselt number (Nu) and friction factor (f) in the coiled tubes and the geometric parameters were employed as input data. Moreover, empirical correlations for estimating the Nu and f were developed by a phenomenological argument in the form of classical power–law correlations and their constants were found using the GA technique. The mean relative errors (MRE) of the developed ANFIS models for estimation of Nu and f are 6.24% and 3.54%, respectively. On the other hand, for empirical correlations, a MRE of 8.06% was found for prediction Nu while MRE of 5.03% was obtained for f. The results show that the ANFIS models can predict Nu and f with the higher accuracy than the developed correlations.