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 - L.S. Sundar; Sérgio M.O. Tavares; Korada V Sharma; António M.B. Pereira (International Journal of Thermofluids)
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.