Comparative study on different cooling techniques for photovoltaic thermal management: Hollow fins, wavy channel and insertion of porous object with hybrid nanofluids. (25th June 2023)

• Record Type: Journal Article
• Authors: Selimefendigil, Fatih; Öztop, Hakan F.
• Is Part Of: Applied thermal engineering. Volume 228(2023)
• Publication Date: 2023-06-25
• Subjects: PV cooling -- Wavy channel -- Porous layer -- Hybrid nanofluid -- Finite volume method -- Hollow fins -- Artificial neural network; Heat engineering -- Periodicals; Heating -- Equipment and supplies -- Periodicals; Periodicals; 621.40205
• DOI: 10.1016/j.applthermaleng.2023.120458
• Languages: English
• ISSNs: 1359-4311

Abstract

Abstract: The present work considers performance of different cooling methods applicable to photovoltaic (PV) panels. Hollow fins and two different channel cooling systems are considered. Flat and hollow fins types are used while cooling channels with wavy wall and with porous layer insert are compared. Effects of different fin numbers (between 2 and 16), hollow fin radius (between 0.8h and 3.2h, h-panel height), Reynolds number (between 100 and 500), wavy channel amplitude (between 0.01H and 0.5H, H-channel height), porous insert permeability (Darcy number between 1 0 − 6 and 1 0 − 1 ), porous layer size (between 0.1L and 0.7L, L-channel length) and nanoparticle volume fraction (between 0 and 0.02) on the cooling performance are numerically assessed. When nanofluid is used in the cooling channels, additional improvements in the cooling performance are obtained while temperature drop of 2.5 °C is achieved at the highest loading. When different cooling systems are compared, channel cooling with porous layer and wavy walls provide the highest cooling performance followed by hollow fin and flat fin systems while 37.6 °C, 37 °C, 33.6 °C and 29 °C temperature drops are obtained as compared to reference panel case without cooling system. When compared to flat fins, hollow fins perform better and additional temperature drop of 8.5 °C can be obtained by using 16 number of fins. It also provides a lightweight design as less material is used. Artificial neural networks are successfully used for performance estimation of wavy cooling channel system with 15 neurons in the hidden layer. The outcomes of this work is useful in further optimization and system development of PV cooling technologies as the need for thermal management in renewable energy sources is increasing due to the energy cost and environmental side concerns. Highlights: Hollow fins provide additional temperature drop of 8.5 °C as compared to flat fins with N = 16. Channel+porous layer gives 37.6 °C temperature drop as compared to system without cooling. Channel+sinusoidal walls provides 37 °C temperature drop as compared to system without cooling. Additional temperature drop of 2.5 °C is obtained when using nanofluids at the highest loading. Fast and accurate performance estimations were made by using ANN with 15 neurons.