Multi-parameter study and genetic algorithm integrated optimization for a nanofluid-based photovoltaic/thermal system

Analyzing and optimizing the joint effect of multiple-parameters can help improve the performance for the nanofluid spectrum splitting photovoltaic/thermal system. This study developed and detailedly validated a steady-state numerical model of a double-pass nanofluid spectrum splitting photovoltaic/thermal system. Using this model, the influence of multiple parameters on the overall exergy efficiency was analyzed, including the nanofluid parameters (particle size, concentration, and thickness of the nanofluid channel) and the system operating parameters (volume flow rate and concentration ratio). Moreover, using the Genetic Algorithm, optimizations were performed to achieve the optimal exergy efficiency with different temperature constraints. In the thermal and electrical common demand-oriented cases, the results demonstrate that smaller particle sizes and higher concentrations are required under low-concentrated illumination conditions, compared with the non-concentrated condition. And in the optimization results for low concentration ratios (2–7), the particle sizes are mainly between 30 and 60 nm, the concentrations are mainly between 50 and 150 ppm, and the thicknesses are mainly in the range of 0.005–0.01 m.