Please use this identifier to cite or link to this item:
http://ir.library.ui.edu.ng/handle/123456789/1904
Title: | Artificial neural network modeling of heat transfer in a staggered cross-flow tube type heat exchanger |
Authors: | Fadare, D. A. Fatona, A. S. |
Issue Date: | Nov-2008 |
Publisher: | Akamai University |
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. |
URI: | http://ir.library.ui.edu.ng/handle/123456789/1904 |
ISSN: | 1551-7624 |
Appears in Collections: | scholarly works |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
(6)ui_art_fadare_artificial_2008 (14.pdf | 533.65 kB | Adobe PDF | View/Open |
Items in UISpace are protected by copyright, with all rights reserved, unless otherwise indicated.