A new approach for the prediction of the heat transfer rate of the wire-on-tube type heat exchanger - use of an artificial neural network model

This study presents an application of artificial neural networks (ANNs) to predict the heat transfer rate of the wire-on-tube type heat exchanger. A back propagation algorithm, the most common learning method for ANNs, is used in the training and testing of the network. To solve this algorithm, a computer program was developed by using C++ programming language. The consistence between experimental and ANNs approach results was achieved by a mean absolute relative error <3%. It is suggested that the ANNs model is an easy modeling tool for heat engineers to obtain a quick preliminary assessment of heat transfer rate in response to the engineering modifications to the exchanger. (C) 2002 Elsevier Science Ltd. All rights reserved.