Simulation of heat exchanger performance by artificial neural networks

The artificial neural network technique was applied to heat transfer through a series of problems of increasing complexity, For the simplest problem of one-dimensional heat conduction with linear activation function, it is possible to give physical meaning to the synaptic weights of the network. A network with sigmoid activation function was used for non-linear representation of convection problems where identification of the weights with physical variables was not possible. Two cases of convective heat transfer with one and two heat transfer coefficients and artificially, generated data were examined. Finally, the method was applied to the analysis of data obtained in the laboratory, for a single-row, fin-tube heat exchanger. It is shown that a better prediction with smaller scatter is obtained in comparison to a conventional power-law correlation for the heat transfer coefficients.