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OPTIMUM OPERATING CONDITIONS FOR HEAT TRANSFER IN AN AIR−WATER HEAT EXCHANGER

DOI: 10.1615/ICHMT.2008.CHT.1080
15 pages

A. Bassam
Centro de Investigación en Ingenieria y Ciencias Aplicadas (CIICAp) - Universidad Autónoma del Estado de Morelos, Morelos, México

G. Urquiza
Centro de Investigación en Ingenieria y Ciencias Aplicadas (CIICAp) - Universidad Autónoma del Estado de Morelos, Morelos, México

J. Alfredo Hernandez
Centro de Investigación en Ingenieria y Ciencias Aplicadas (CIICAp) - Universidad Autónoma del Estado de Morelos, Morelos, México

Abstract

A predictive model for heat transfer is developed using artificial neural network (ANN) for the air-water heat exchanger. This ANN model takes into account the input and output experimental temperatures for the air and water, as well as the mass flow rate for the air and water (Reynolds number for air and water) and the distance between two tubes. A feedforward network with one hidden layer is used in order to predict the heat transfer rate and Nusselt number for the air and water flow, respectively. For the network, the Levenberg-Marquardt learning algorithm, the hyperbolic tangent sigmoid transfer-function and linear transfer-function were used. The best fitting training database was obtained with five neurons in the hidden layer, which made possible to predict the output variables. On the validation database, simulation and experimental database were in good agreement (R > 0.99). This ANN model can be used to predict the heat transfer and Nusselt number (air-water) when the input parameters are well-known, and can be utilized to obtain prediction of optimal parameters; it can also be used to control on-line the heat exchanger.

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