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With the growing demand for power and the pressing need to shift towardsrenewable energy sources, wind power stands as a vital component of the energytransition. To optimize energy production, researchers have focused on designoptimization of Savonius-type vertical axis wind turbines (VAWTs). The currentstudy utilizes Unsteady Reynolds-Averaged Navier Stokes (URANS) simulationsusing the sliding mesh technique to obtain flow field data and powercoefficients. A Kriging Surrogate model was trained on the numerical data ofrandomly initialized data points to construct a response surface model. ThenGrey Wolf Optimization (GWO) algorithm was utilized to achieve global maxima onthis surface, using the turbine's power coefficient as the objective function.A comparative analysis was carried out between simulation and experimental datafrom prior studies to validate the accuracy of the numerical model. Theoptimized turbine-deflector configuration showed a maximum improvement of34.24% in power coefficient. Additionally, the GWO algorithm's effectivenesswas compared with Particle Swarm Optimization (PSO) and was found to be betterin most cases, converging towards the global maxima faster. This study exploresa relatively unexplored realm of metaheuristic optimization of wind turbines byusing deflectors, for efficient energy harvesting, presenting promisingprospects for enhancing renewable power generation.

Maximizing Savonius Turbine Performance using Kriging Surrogate Model and Grey Wolf-Driven Cylindrical Deflector Optimization