  With the growing demand for power and the pressing need to shift towards
renewable energy sources, wind power stands as a vital component of the energy
transition. To optimize energy production, researchers have focused on design
optimization of Savonius-type vertical axis wind turbines (VAWTs). The current
study utilizes Unsteady Reynolds-Averaged Navier Stokes (URANS) simulations
using the sliding mesh technique to obtain flow field data and power
coefficients. A Kriging Surrogate model was trained on the numerical data of
randomly initialized data points to construct a response surface model. Then
Grey Wolf Optimization (GWO) algorithm was utilized to achieve global maxima on
this surface, using the turbine's power coefficient as the objective function.
A comparative analysis was carried out between simulation and experimental data
from prior studies to validate the accuracy of the numerical model. The
optimized turbine-deflector configuration showed a maximum improvement of
34.24% in power coefficient. Additionally, the GWO algorithm's effectiveness
was compared with Particle Swarm Optimization (PSO) and was found to be better
in most cases, converging towards the global maxima faster. This study explores
a relatively unexplored realm of metaheuristic optimization of wind turbines by
using deflectors, for efficient energy harvesting, presenting promising
prospects for enhancing renewable power generation.
