PREDICTION OF HEAT TRANSFER CHARACTERISTICS FOR FORCED CONVECTION PIPE FLOW USING ARTIFICIAL NEURAL NETWORKS

Abstract

This paper investigates the ability of utilizing the artificial neural network (ANN) in calculating the forced convection characteristics coefficients from internal flow of air inside a pipe subjected to constant heat flux. The heat transfer characteristics such as Nusselt number (Nu), Stanton number (St) and friction factor (f) which are calculated using the empirical correlations have high deviation from that obtained from the experiments. So, the ANN method is proposed for predicting these characteristics coefficients more close to the experimental results. The training and testing data for optimizing the ANN structure are based on the experimental data obtained from the experiments performed on a forced convection apparatus. Three training algorithms for the training of the ANN were used and the presented ANN is implemented by using such MATLAB program. For the preferable ANN structure acquired in the current work, an acceptable mean square error was achieved for the training and test data, using the Trainlm algorithm. The results reveal that the estimated results are very close to the experimental data. Also, a new Graphical User Interface (GUI) is implemented for the application of ANN in the calculation of the attempted heat transfer parameters.