Optimization of unsteady hybrid photovoltaic-thermal system using nanofluids and genetic algorithm based on second law analysis
摘要
Energy and exergy analyses were performed for hybrid photovoltaic-thermal (PVT) system using CuO, TiO2 and Al2O3 in water as coolants, and considering spherical, blade, brick, cylindrical and platelet shapes of nanoparticles. The aim was to detect working conditions that guarantee the least loss of useful energy and the greatest extraction of heat from photovoltaic panel. For this purpose, a detailed transient model was obtained and solved numerically using a developed code with Runge–Kutta-Fehlberg 4-5th order method in MATLAB. The simulation results were validated with experimental results of previous literature and proper conformity was reached between them. The PVT performance using CuO/water was significantly better compared to the other coolants investigated. Thus, for CuO/water, the refrigerant output temperature, power gain, thermal energy efficiency and overall exergy efficiency reached their highest values of 52 °C, 451W, 62.14% and 17%, respectively. When CuO/water nanofluid was used, the maximum value of thermal energy efficiency increased by 4.74%, 6.65% and 11.86% compared to TiO2/water, Al2O3/water and pure water, respectively, while the maximum overall exergy efficiency increased by 5.35%, 8.84% and 20.17%. Also, for blade-shape nanoparticles, both total energy and total exergy efficiencies were greater than those for other shapes due to their larger contact area at the particle–fluid interface which favored the nanoparticle motion in the base fluid, which decreased thermal resistance and improved heat transport. Finally, for the present study conditions, the genetic algorithm optimization was used to achieve a combination of optimum values for coolant flow of 0.0019 kg/s, absorber thickness of 0.001 m, flow channel height of 0.0157 m, and panel surface of 0.77 m2 with minimum irreversibilities and an increment in maximum value of overall exergy efficiency of 27%.