Modeling of convection heat transfer of supercritical carbon dioxide in a vertical tube at low Reynolds numbers using artificial neural network

Today, many researches have been directed on heat transfer of supercritical fluids; however, since the analysis of heat transfer in these fluids founded by a mathematical model based on the effective parameters is complicated, so in this paper, a group method of data handling (GMDH) type artificial neural network are used for calculating local heat transfer coefficient h(x) of supercritical carbon dioxide in a vertical tube with 2 mm diameter at low Reynolds numbers (Re<2500) by empirical results obtained by Jiang et al. [1]. At first, we considered h(x) as target parameter and G, Re, Bo*, x(+) and q(w) as input parameters. Then, we divided empirical data into train and test sections in order to accomplish modeling. We instructed GMDH type neural network by 80% of the empirical data. 20% of primary data which had been considered for testing the appropriateness of the modeling were entered into the GMDH network. Results were compared by two statistical criterions (R-2 and RMSE) with empirical ones. The results obtained by using GMDH type neural network are in excellent agreement with the experimental results. (C) 2010 Elsevier Ltd. All rights reserved.