Application of intelligent methods for the prediction and optimization of thermal characteristics in a tube equipped with perforated twisted tape

In the present study, a feed-forward artificial neural network (ANN) was developed to estimate the Nusselt number (Nu), friction factor (f), and thermal performance (η) in a tube equipped with perforated twisted tape. The MSE and R2 values of the best network (4-10-3-3) are 0.04 and 0.9965, respectively. In addition, empirical equations were developed using the genetic algorithm. The MSE values of power-law equations for Nu, f and η are 2.7369, 4.13E-06, and 5.09E-05, respectively. Estimation of the developed ANN was found to be superior in comparison with the corresponding power-law equations.