Cooling of double PV-TEG combined units by using a T-shaped branching channel equipped with an inclined elastic fin

被引:2
作者
Selimefendigil, Fatih [1 ]
Oztop, Hakan F. [2 ]
机构
[1] Manisa Celal Bayar Univ, Dept Mech Engn, TR-45140 Manisa, Turkiye
[2] Firat Univ, Technol Fac, Dept Mech Engn, TR-23119 Elazig, Turkiye
关键词
Elastic fin; Branching channel; Double PV-TEG; ANN; FEM; FLUID-STRUCTURE INTERACTION; HEAT-TRANSFER; MIXED CONVECTION; PERFORMANCE ANALYSIS; NATURAL-CONVECTION; FLOW; CAVITY; ENHANCEMENT; NANOFLUID; WALL;
D O I
10.1016/j.energy.2025.135030
中图分类号
O414.1 [热力学];
学科分类号
摘要
Modern energy technology systems including batteries, hydrogen storage units, electronic equipments and photovoltaic (PV) modules require effective cooling methods and thermal management techniques for performance improvements and safety of operation. In this study, a novel thermal management system for double PV units is proposed by using combined effects of inclined elastic fin in the T-shaped branching cooling channel and thermoelectric generator (TEG) modules. The FEM based numerical analysis is carried out for different Reynolds numbers (between 200 to 1200), fin lengths (between 0 and H), fin tilt (between 10 and 45), and fin position (yf between-H and H) where both rigid or elastic fin configurations are considered. Cell temperature drops of 14 degrees C and 15.48 degrees C are seen in PV1 and PV2 when Reynolds number (Re) is raised from 200 to 1200 using a rigid fin while average temperatures become 2 degrees C and 0.5 degrees C higher at the highest Re when elastic finis used. Poor thermal transport is observed at the fin location of yf=-H. Fins and higher Re significantly lower the PV surface temperature of both PVs in double PV-TEG combined system. When elastic and rigid fins are used at the highest Re, the temperature of PV1 is lowered by about 14.5 degrees C and 16.7 degrees C compared to the reference configuration of the no-fin case at Re=200, while the temperature of PV2 is lowered by about 12 degrees C and 11.2 degrees C. PV's performance is estimated using artificial neural network model for different flow rates, fin lengths, and fin inclinations (both elastic and rigid scenarios).
引用
收藏
页数:20
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