An adaptive neuro-fuzzy inference system (ANFIS) is applied to predict thermal and flow characteristics in serpentine microtubes. Heat transfer rate and pressure drop were experimentally measured for six serpentine microtubes with different geometrical parameters. Thermal and flow characteristics were obtained in various flow conditions. The ANFIS models were trained using the experimental data to predict Nusselt number (Nu) and friction factor (f) in the studied serpentine microtubes as a function of geometric parameters and flow conditions. The model was validated through testing data set, which were not previously introduced to the developed ANFIS. For Nu prediction, the root mean square error (RMSE), mean relative error (MRE), and absolute fraction of variance (R2) between the predicted results and experimental data were found 0.2058, 1.74%, and 0.9987, respectively. The corresponding calculated values for f were 0.0056, 2.98%, and 0.9981, respectively. The prediction accuracy of the ANFIS models was compared with that of corresponding classical power-law correlations and its advantages are illustrated.
