Please use this identifier to cite or link to this item: http://ir.library.ui.edu.ng/handle/123456789/1904
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dc.contributor.authorFadare, D. A.-
dc.contributor.authorFatona, A. S.-
dc.date.accessioned2018-10-11T08:56:11Z-
dc.date.available2018-10-11T08:56:11Z-
dc.date.issued2008-11-
dc.identifier.issn1551-7624-
dc.identifier.otherui_art_fadare_artificial_2008-
dc.identifier.otherThe Pacific Journal of Science and Technology 9(2), pp. 317-323-
dc.identifier.urihttp://ir.library.ui.edu.ng/handle/123456789/1904-
dc.description.abstractThis paper presents the application of Artificial Neural Network (ANN) in modeling the heat transfer coefficient of a staggered multi-row, multi-column, cross-flow, tube-type heat exchanger. Heat transfer data were obtained experimentally for air flowing over a bank of copper tubes arranged in staggered configuration with 5 rows and 4 columns at different air flow rates with throttle valve openings at 10 - 100%. The Reynolds number and the row number were used as input parameters, while the Nusselt number was used as output parameter in training and testing of the multi-layered, feed-forward, back-propagation neural networks. The network used in this study was designed using the MATLABĀ® Neural Network Toolbox. The results show that the accuracy between the neural networks predictions and experimental values was achieved with Mean Absolute Relative Error (MRE) less than 1 and 4% for the training and testing data sets respectively, suggesting the reliability of the networks as a modeling tool for engineers in preliminary design of heat exchangers.en_US
dc.language.isoenen_US
dc.publisherAkamai Universityen_US
dc.titleArtificial neural network modeling of heat transfer in a staggered cross-flow tube type heat exchangeren_US
dc.typeArticleen_US
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