the cfd provides data for adaptive neuro-fuzzy to model the heat transfer in flat and discontinuous fins

In the present study, adaptive neuro-fuzzy inference system (anfis) approach was applied for predicting the heat transfer and air flow pressure drop on flat and discontinuous fins. the heat transfer and friction characteristics were experimentally investigated in four flat and discontinuous fins with different geometric parameters including fin length (r), fin interruption (s), fin pitch (p), and fin thickness (t). two anfis models were developed using the computational fluid dynamic (cfd) results, as validated by the experimental data. the anfis models were applied for prediction of nusselt number (nu) and friction factor (f) as functions of reynolds number (re) and fin geometric parameters including span-wise spacing ratio (p/t) and stream wise spacing ratio (s/r). the low error values for testing data set, which were not employed in the training of the anfis, proved the precision and validity of the model. the root mean square error (rmse) of 0.7343 and mean relative error (mre) of 1.33 % were obtained for nu prediction. in addition, these values for the estimation of f were obtained as 0.0158, 3.32 %, respectively.