Artificial Neural Network Analysis on the Heat Transfer and Friction Factor of the Double Tube with Spring Insert

摘要

The paper focus is the application of artificial neural networks to analyze the heat transfer and friction factor of the horizontal double tube heat exchanger with spring insert. The optimal artificial neural network model for predicting the heat transfer coefficient and friction factor of the double tube with spring insert is considered. The developed artificial neural network model shows the mean square error (MSE) of 0.004 and the correlation coefficient (R) of 0.99885 in modeling of overall experimental dataset. The predicted results obtained from the optimize ANN model are verified with the testing experimental data and good agreement is obtained with errors of ±2.5%, -5%-+7.5% for heat transfer coefficient and friction factor, respectively. In addition, the predicted results are also validated with those from the other correlations in various literatures. The ANN model results are found to be more accurate than the predicted results obtained from the published correlation.