Maximizing photovoltaic thermal system through computational fluid dynamics-driven multi-factor parametric optimization: A Taguchi-grey relational analysis method to enhancing electrical output and cooling efficiency for sustainable energy - Natnale Sitotaw Asefa; Kiran Shahapurkar; Tilahun Nigussie; Abdulkadir Aman Hassen; Manzoore Elahi M. Soudagar; Yasser Fouad; Irfan Ali; Sagar Shelare; Shubham Sharma; V.K. Bupesh Raja; Abinash Mahapatro; Sarabjit Singh; et al. (Case Studies in Thermal Engineering)
This study presents a computational fluid dynamics (CFD)-based grey relational analysis (GRA) using the Taguchi method for the parametric optimisation of a photovoltaic thermal (PVT) system. The objective of the study is to use the excess thermal energy to increase the amount of electricity produced by the PV systems. This study is distinguished by design factors such as tube configuration and geometry, operational conditions including mass flow rate and fluid inlet temperature, and external environmental parameters including ambient temperature and solar irradiation. This study employs a distinctive integration of CFD simulations with Grey Relational Analysis (GRA) to optimise photovoltaic thermal (PV/T) systems via multi-factor analysis. This approach optimises electrical and thermal efficiency, tackling essential performance compromises overlooked by traditional methods. This strategy enhances system efficiency and guides the design of modern, sustainable PVT systems. The research additionally highlights the need of mitigating hotspots in the photovoltaic panel and increasing the temperature of the cooling water outlet. The results indicate the significant impact of multiple factors on the GRA analysis of variance: flow pattern (6.52 %), tube area (0.53 %), mass flow rate (15.68 %), ambient temperature (4.42 %), and radiation (42.27 %). The Taguchi projected optimal configuration (A1B3C3D3E3F1) attained a GRG of 0.791, which closely corresponds with the simulated GRG of 0.752, thus illustrating the trustworthiness of the predictive model. Furthermore, the PV thermal system exhibited a significant improvement in multi-factor performance. The system attained a 16.19 % enhancement in thermal efficiency, a 17.34 % decrease in PV mean variation (indicating substantial hotspot mitigation), and a large temperature output increase of 7.99 °C. These enhancements highlight the system's ingenuity and its capacity to elevate photovoltaic thermal efficacy. This unique work integrates CFD simulations with Grey Relational Analysis (GRA) and the Taguchi technique to create a multi-objective optimisation strategy that properly forecasts the most important PVT system performance parameters. Results show great computational precision (uncertainty <10−4) and 95.1 % agreement between predicted and simulated results, offering a reliable foundation for optimising PVT system thermal and electrical efficiency.