Performance Characterization of a Solar Cavity Collector Using Artificial Neural Network
It is mandatory to improve the design of the flat plate collector (FPC) used for solar thermal applications to perform well. One way to improve the performance characteristics of FPC is to retain the heat energy available inside the collector. That is, a collector should be capable to give more heat...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Modelling and Simulation in Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/7129833 |
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| author | B. Lakshmipathy K. Sivakumar M. Senthilkumar A. Kajavali S. Christopher Ezhil Singh Sivaraj Murugan |
| author_facet | B. Lakshmipathy K. Sivakumar M. Senthilkumar A. Kajavali S. Christopher Ezhil Singh Sivaraj Murugan |
| author_sort | B. Lakshmipathy |
| collection | DOAJ |
| description | It is mandatory to improve the design of the flat plate collector (FPC) used for solar thermal applications to perform well. One way to improve the performance characteristics of FPC is to retain the heat energy available inside the collector. That is, a collector should be capable to give more heat energy to working fluid for a longer duration. It has been implemented in such a way in an entertained and improved model which is known as solar cavity collector (SCC). It consists of 5 numbers of cavities equipped with inlet and outlet tubes. The same having with an enclosure has been constructed and investigated to find the optimal performance. In general, the physical dimensions of the collector influence more the functioning behaviors of SCC. The performance variables that are considered for the present study are the comparison between 5 and 7 numbers of cavities and the effect of aperture entry. Collector angle of tilt, two types of flow mode, and water mass flow rates are the other performance variables that are also considered. The data from the experimentations are trained, tested, and validated with the help of the artificial neural network (ANN). The accuracy of the model is 96%, and the end results revealed the same trend followed by both experimental and ANN simulation results. Also, the variations that occur between ANN and experimented results are ±4%. |
| format | Article |
| id | doaj-art-d9e9308f9fe349cb9fd123c976fbac33 |
| institution | Kabale University |
| issn | 1687-5605 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Modelling and Simulation in Engineering |
| spelling | doaj-art-d9e9308f9fe349cb9fd123c976fbac332025-08-20T03:24:24ZengWileyModelling and Simulation in Engineering1687-56052022-01-01202210.1155/2022/7129833Performance Characterization of a Solar Cavity Collector Using Artificial Neural NetworkB. Lakshmipathy0K. Sivakumar1M. Senthilkumar2A. Kajavali3S. Christopher Ezhil Singh4Sivaraj Murugan5Department of Mechanical EngineeringDepartment of Mechanical EngineeringDepartment of Mechanical EngineeringDepartment of Mechanical EngineeringDepartment of Mechanical EngineeringFaculty of ManufacturingIt is mandatory to improve the design of the flat plate collector (FPC) used for solar thermal applications to perform well. One way to improve the performance characteristics of FPC is to retain the heat energy available inside the collector. That is, a collector should be capable to give more heat energy to working fluid for a longer duration. It has been implemented in such a way in an entertained and improved model which is known as solar cavity collector (SCC). It consists of 5 numbers of cavities equipped with inlet and outlet tubes. The same having with an enclosure has been constructed and investigated to find the optimal performance. In general, the physical dimensions of the collector influence more the functioning behaviors of SCC. The performance variables that are considered for the present study are the comparison between 5 and 7 numbers of cavities and the effect of aperture entry. Collector angle of tilt, two types of flow mode, and water mass flow rates are the other performance variables that are also considered. The data from the experimentations are trained, tested, and validated with the help of the artificial neural network (ANN). The accuracy of the model is 96%, and the end results revealed the same trend followed by both experimental and ANN simulation results. Also, the variations that occur between ANN and experimented results are ±4%.http://dx.doi.org/10.1155/2022/7129833 |
| spellingShingle | B. Lakshmipathy K. Sivakumar M. Senthilkumar A. Kajavali S. Christopher Ezhil Singh Sivaraj Murugan Performance Characterization of a Solar Cavity Collector Using Artificial Neural Network Modelling and Simulation in Engineering |
| title | Performance Characterization of a Solar Cavity Collector Using Artificial Neural Network |
| title_full | Performance Characterization of a Solar Cavity Collector Using Artificial Neural Network |
| title_fullStr | Performance Characterization of a Solar Cavity Collector Using Artificial Neural Network |
| title_full_unstemmed | Performance Characterization of a Solar Cavity Collector Using Artificial Neural Network |
| title_short | Performance Characterization of a Solar Cavity Collector Using Artificial Neural Network |
| title_sort | performance characterization of a solar cavity collector using artificial neural network |
| url | http://dx.doi.org/10.1155/2022/7129833 |
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