Second law efficiency and thermal entropy generation of 30:70% of glycerol + water based SiO2 nanofluids in a thermosyphon flat plate collector: Experimental and Bayesian artificial neural network algorithm
This research examines the thermal entropy generation, frictional entropy generation, entropy generation number, and energy efficiency, which were experimentally assessed for a flat plate collector functioning under thermosyphon conditions utilizing a SiO2/30:70% glycerol and water nanofluid mixture...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | International Journal of Thermofluids |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S266620272400452X |
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| author | L.S. Sundar Sérgio M.O. Tavares Korada V Sharma António M.B. Pereira |
| author_facet | L.S. Sundar Sérgio M.O. Tavares Korada V Sharma António M.B. Pereira |
| author_sort | L.S. Sundar |
| collection | DOAJ |
| description | This research examines the thermal entropy generation, frictional entropy generation, entropy generation number, and energy efficiency, which were experimentally assessed for a flat plate collector functioning under thermosyphon conditions utilizing a SiO2/30:70% glycerol and water nanofluid mixture. The artificial neural network with Bayesian regularization was employed to predict the gathered data. The research was conducted between 09:00 and 16:30 hours, with volume loadings varying from 0.25 to 1.0%. The time intervals of period-1 (09:00 to 13:00 hours) and period-2 (13:00 to 16:30 hours) were considered for clarity. The optimal boost for all parameters occurred at mid-day (13:00 hrs), as indicated by the data. The thermal entropy generation diminished to 2.72%, however the frictional entropy generation and exergy efficiency improved to 71.81% and 333.21%, respectively, with 1.0% volume of nanofluid at a Reynolds number of 718.36, compared to the base fluid. At a nanofluid concentration of 1.0% vol. and a Reynolds number of 718.36, the entropy generation number is similarly diminished to 1.05%. SiO2 nanofluids were employed to diminish irreversibilities and consequently enhance second law energy efficiency. The Bayesian regularization program utilizes the gathered data to provide very accurate estimations. The determined correlation coefficient values for thermal entropy generation, thermal exergy destruction, frictional entropy generation, frictional exergy destruction, exergy efficiency, and entropy generation number are 0.86977, 0.87323, 0.99584, 0.99714, 0.99015, and 0.99022, respectively. Multi linear regression correlations were also proposed based on the experimental data. |
| format | Article |
| id | doaj-art-eaa1329d0a294127a229dfd9707c1051 |
| institution | Kabale University |
| issn | 2666-2027 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Thermofluids |
| spelling | doaj-art-eaa1329d0a294127a229dfd9707c10512025-01-08T04:53:36ZengElsevierInternational Journal of Thermofluids2666-20272025-01-0125101013Second law efficiency and thermal entropy generation of 30:70% of glycerol + water based SiO2 nanofluids in a thermosyphon flat plate collector: Experimental and Bayesian artificial neural network algorithmL.S. Sundar0Sérgio M.O. Tavares1Korada V Sharma2António M.B. Pereira3Centre for Mechanical Technology and Automation, Department of Mechanical Engineering, University of Aveiro, 3810-131 Aveiro, Portugal; Corresponding author.Centre for Mechanical Technology and Automation, Department of Mechanical Engineering, University of Aveiro, 3810-131 Aveiro, PortugalCenter for Energy Studies, Department of Mechanical Engineering, Jawaharlal Nehru Technological University Hyderabad, Kukatpally, Hyderabad, IndiaCentre for Mechanical Technology and Automation, Department of Mechanical Engineering, University of Aveiro, 3810-131 Aveiro, PortugalThis research examines the thermal entropy generation, frictional entropy generation, entropy generation number, and energy efficiency, which were experimentally assessed for a flat plate collector functioning under thermosyphon conditions utilizing a SiO2/30:70% glycerol and water nanofluid mixture. The artificial neural network with Bayesian regularization was employed to predict the gathered data. The research was conducted between 09:00 and 16:30 hours, with volume loadings varying from 0.25 to 1.0%. The time intervals of period-1 (09:00 to 13:00 hours) and period-2 (13:00 to 16:30 hours) were considered for clarity. The optimal boost for all parameters occurred at mid-day (13:00 hrs), as indicated by the data. The thermal entropy generation diminished to 2.72%, however the frictional entropy generation and exergy efficiency improved to 71.81% and 333.21%, respectively, with 1.0% volume of nanofluid at a Reynolds number of 718.36, compared to the base fluid. At a nanofluid concentration of 1.0% vol. and a Reynolds number of 718.36, the entropy generation number is similarly diminished to 1.05%. SiO2 nanofluids were employed to diminish irreversibilities and consequently enhance second law energy efficiency. The Bayesian regularization program utilizes the gathered data to provide very accurate estimations. The determined correlation coefficient values for thermal entropy generation, thermal exergy destruction, frictional entropy generation, frictional exergy destruction, exergy efficiency, and entropy generation number are 0.86977, 0.87323, 0.99584, 0.99714, 0.99015, and 0.99022, respectively. Multi linear regression correlations were also proposed based on the experimental data.http://www.sciencedirect.com/science/article/pii/S266620272400452XFlat plate collectorNusselt numberNatural flowSiO2 nanofluidBayesian regularization |
| spellingShingle | L.S. Sundar Sérgio M.O. Tavares Korada V Sharma António M.B. Pereira Second law efficiency and thermal entropy generation of 30:70% of glycerol + water based SiO2 nanofluids in a thermosyphon flat plate collector: Experimental and Bayesian artificial neural network algorithm International Journal of Thermofluids Flat plate collector Nusselt number Natural flow SiO2 nanofluid Bayesian regularization |
| title | Second law efficiency and thermal entropy generation of 30:70% of glycerol + water based SiO2 nanofluids in a thermosyphon flat plate collector: Experimental and Bayesian artificial neural network algorithm |
| title_full | Second law efficiency and thermal entropy generation of 30:70% of glycerol + water based SiO2 nanofluids in a thermosyphon flat plate collector: Experimental and Bayesian artificial neural network algorithm |
| title_fullStr | Second law efficiency and thermal entropy generation of 30:70% of glycerol + water based SiO2 nanofluids in a thermosyphon flat plate collector: Experimental and Bayesian artificial neural network algorithm |
| title_full_unstemmed | Second law efficiency and thermal entropy generation of 30:70% of glycerol + water based SiO2 nanofluids in a thermosyphon flat plate collector: Experimental and Bayesian artificial neural network algorithm |
| title_short | Second law efficiency and thermal entropy generation of 30:70% of glycerol + water based SiO2 nanofluids in a thermosyphon flat plate collector: Experimental and Bayesian artificial neural network algorithm |
| title_sort | second law efficiency and thermal entropy generation of 30 70 of glycerol water based sio2 nanofluids in a thermosyphon flat plate collector experimental and bayesian artificial neural network algorithm |
| topic | Flat plate collector Nusselt number Natural flow SiO2 nanofluid Bayesian regularization |
| url | http://www.sciencedirect.com/science/article/pii/S266620272400452X |
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