Thermodynamic modeling and performance optimization of nanofluid-based photovoltaic/thermal system using central composite design scheme of response surface methodology

This study develops and validates a programming code to demonstrate an iterative resolution analytical thermodynamic modeling method for nanofluid-based photovoltaic/thermal (PV/T) systems. The experimental validation of the model is conducted by constructing and testing a novel PV/T collector that incorporates a flow channel with a double-loop rectangular spiral design. Four different operating independent factors, including solar irradiation (G = 300–900 W/m2), ambient temperature as per climate condition of Jalandhar city of India in July (Ta = 283.15–313.15 K), the CuO-water nanofluid concentration (φ = 0.02–0.08%) and mass flow rate of nanofluid (ṁnf = 201–241 kg/h) are used for thermodynamic performance prediction. Mathematical correlations are developed for each of the dependent response factors, including energetic thermal power output, energetic electrical power output, exergetic thermal power output, and entropy generation. Correlations are based on operating independent factors and their interaction on proposed system performance. Different single and multiple optimization approaches aim to predict optimal dependent and independent factor values. The regression model proposed in this study recommends choosing G = 855.353 W/m2, Ta = 313.148 K, φ = 0.08%, and ṁnf = 201 kg/h in order to optimize the energetic and exergetic electrical and thermal power output while minimizing entropy generation.