CORRELATION OF HEAT TRANSFER ANDFRICTION IN FIN SPIRAL TUBES USINGARTIFICIAL NEURAL NETWORKS

An artificial neural network approach was used to correlate empirical Colburn j factors and Fanningfriction factors for liquid water flow in straight pipes with internal helical fins. Calculation of heattransfer and friction in spiral pipes is one of the serious challenges for activists in this field. So far,various methods have been presented to answer this problem, including the use of artificialintelligence and algorithms based on it. In this research, experimental data related to the heat transferof spiral tubes have been used to develop a new model based on a neural network to predict theamount of heat transfer and friction in spiral tubes. The performance of neural networks was superiorcompared to the corresponding power law regressions. Subsequently, artificial neural networks wereused to predict data from other researchers, but the results were less accurate. In this regard, due to thehigh capacity of the Reluctant neural network, this structure has been used using real data as input.Finally, to check the efficiency of the neural network model, the results have been compared with thereal sample. The prediction of the increase in heat transfer efficiency with a low error percentage(range ۱-۰.۹۹) for regression in comparison with the experimental sample, indicates the sufficientcompliance of the proposed model with the real model and the efficiency of the network.