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Computational fluid dynamics analysis and design exploration of water-cooled photovoltaics subjected to various operating conditions (CROSBI ID 678521)

Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija

Priegl, Domagoj ; Jurčević, Mišo ; Penga, Željko ; Nižetić, Sandro Computational fluid dynamics analysis and design exploration of water-cooled photovoltaics subjected to various operating conditions // SpliTech2019. 2019. str. pdf-pdf

Podaci o odgovornosti

Priegl, Domagoj ; Jurčević, Mišo ; Penga, Željko ; Nižetić, Sandro

engleski

Computational fluid dynamics analysis and design exploration of water-cooled photovoltaics subjected to various operating conditions

Abstract —The Sun is the most prominent renewable energy source, while photovoltaics (PV) are the most popular solar energy harvesting product. A small part of solar radiation is converted to electrical energy, with the rest reflected, transmitted and converted to heat, respectively resulting in heat generation in the PV module. The efficiency of the photovoltaic module decreases due to higher operating temperatures. In order to increase the overall efficiency of the PV module, the active cooling technique is incorporated. On the back of the photovoltaic, there is a fixed tank through which the water coolant flows. Three-dimensional computational fluid dynamics (CFD) model for steady-state heat transfer is developed and tested. This numerical analysis deals with the heat transfer through the PV module and provides insight into the intensity of heat extraction from the module thanks to the active cooling system. The developed numerical model predicts output parameters based on three input parameters that can be varied as desired without triggering CFD simulation. The heat source, the coolant inlet velocity, and the ambient temperature are the input parameters that are varied while the coolant inlet temperature is fixed at 290 K. The average PV-module temperature, the pressure drop in the tank and the coolant outlet temperature are output parameters. To create a Response Surface, 30 design points are created, each point representing a unique combination of the input parameters. The predictions of the output parameters are made from the generated Response Surface and compared to the simulation results for 30 design points. The average deviation of the predictions, generated from the Response Surface, is less than 1% when compared to the simulation results for the design points.

Computational Fluid Dynamics ; Numerical Analysis ; Photovoltaic ; Efficiency ; Response Surface, Active Cooling.

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Podaci o prilogu

pdf-pdf.

2019.

objavljeno

Podaci o matičnoj publikaciji

SpliTech2019

Podaci o skupu

4th International Conference on Smart and Sustainable Technologies - SpliTech2019

predavanje

18.06.2019-21.06.2019

Split, Hrvatska; Bol, Hrvatska

Povezanost rada

Strojarstvo