Neural networks analysis of thermal characteristics on plate-fin heat exchangers with limited experimental data

Abstract

In this paper, an application of artificial neural networks (ANNs) was presented to predict the pressure drop and heat transfer characteristics in the plate-fin heat exchangers (PFHEs). First, the thermal performances of five different PFHEs were evaluated experimentally. The Colburn factor j and friction factor f to different type fins were obtained under various experimental conditions. Then, a feed-forward neural network based on back propagation algorithm was developed to model the thermal performance of the PFHEs. The ANNs was trained using the experimental data to predict j and f factors in PFHEs. Different network configurations were also examined for searching a better network for prediction. The predicted values were found to be in good agreement with the actual values from the experiments with mean squared errors (MSE) less than 1.5% for j factor and 1% for f factor, respectively. This demonstrated that the neural network presented can help the engineers and manufacturers predict the thermal characteristics of new type fins in PFHEs under various operating conditions.