Two different 3-layer back propagation (BP) neural network models were established in order to explore the feasibility of applying neural network technology to analyze the parametric effect on performance in heat exchanger (HX). The models were trained and optimized to predict the heat transfer performance and flow resistance of a plate-fin HX. The parameter sensitivity analysis of fin was conducted according to the prediction results. The data of train and test samples were from many wind tunnel tests and simulation results. The models had two and six hidden layer neurons irrespectively after optimized. The transfer functions of hidden and output layers were tansig and purelin function irrespectively, and the train function was based on LevenbergMarquardt （L-M） method. Results show that neural network can predict the effect of fin parameter on HX performance. The results of fin parameter sensitivity analysis accorded well with the engineering experience.
XIAO Bao-lan, YU Xiao-li, HAN Song, LU Guo-dong, XIA Li-feng. Parameter sensitivity analysis of fin based on neural network
in heat exchanger. J4, 2011, 45(1): 122-125.
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