A neural network methodology for heat transfer data analysis

Neural networks have been until very recently a topic of academic research. Recent developments of powerful learning algorithms and the increasing number of applications in a great number of disciplines suggest that neural networks can provide useful tools for modeling and correlating practical heat transfer problems. This paper presents an introduction to computing with neural networks. To evaluate the potential of neural networks for correlating heat transfer data, three different examples are solved, using a three-layer feedforward neural network. Two different learning algorithms, including the traditional backpropagation algorithm, are used to teach the neural network. It is shown that neural networks can be used to adequately correlate heat transfer data.