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DC Field | Value | Language |
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dc.contributor.author | Fadare, D. A. | - |
dc.contributor.author | Fatona, A. S. | - |
dc.date.accessioned | 2018-10-11T08:56:11Z | - |
dc.date.available | 2018-10-11T08:56:11Z | - |
dc.date.issued | 2008-11 | - |
dc.identifier.issn | 1551-7624 | - |
dc.identifier.other | ui_art_fadare_artificial_2008 | - |
dc.identifier.other | The Pacific Journal of Science and Technology 9(2), pp. 317-323 | - |
dc.identifier.uri | http://ir.library.ui.edu.ng/handle/123456789/1904 | - |
dc.description.abstract | This 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.iso | en | en_US |
dc.publisher | Akamai University | en_US |
dc.title | Artificial neural network modeling of heat transfer in a staggered cross-flow tube type heat exchanger | en_US |
dc.type | Article | en_US |
Appears in Collections: | scholarly works |
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File | Description | Size | Format | |
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(6)ui_art_fadare_artificial_2008 (14.pdf | 533.65 kB | Adobe PDF | View/Open |
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