Full Text Available
Note: Clicking the button above will open the full text document at the original institutional repository in a new window.
The application of artificial neural networks to mapping of wind speed profile for energy application in Nigeria
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 stagg...
Saved in:
| Format: | Article |
|---|---|
| Published: |
2010
|
| Tags: |
No Tags, Be the first to tag this record!
|
MARC
| LEADER | 00000njm a2000000a 4500 | ||
|---|---|---|---|
| 001 | oai:repository.ui.edu.ng:123456789/1888 | ||
| 042 | |a dc | ||
| 720 | |a Fadare, D. A. |e author | ||
| 260 | |c 2010 | ||
| 520 | |a 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 MATLAB® 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. | ||
| 024 | 8 | |a 1551-7624 | |
| 024 | 8 | |a ui_art_fadare_artificial_2008 | |
| 024 | 8 | |a The Pacific Journal of Science and Technology 9(2), pp. 317-323 | |
| 024 | 8 | |a http://ir.library.ui.edu.ng/handle/123456789/1888 | |
| 245 | 0 | 0 | |a The application of artificial neural networks to mapping of wind speed profile for energy application in Nigeria |