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With the growing power demand and the imperative for 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 is trained on the numerical data of randomly initialized data points to construct a response surface model. The grey wolf optimization (GWO) algorithm is then utilized to achieve the global maxima on this surface, using the turbine’s power coefficient as the objective function. A comparative analysis is carried out between simulation and experimental data from prior studies to validate the accuracy of the numerical model. The optimized turbine–deflector configuration shows an improvement of 34.24% in power coefficient. Additionally, the GWO algorithm’s effectiveness is compared to Particle Swarm Optimization (PSO) and is found to be better in most cases, converging toward the global maxima faster. This study explores a relatively unexplored realm of metaheuristic optimization of wind turbines using deflectors, for efficient energy harvesting, presenting promising prospects for enhancing renewable sources.

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

  • 会议论文

  • 发表日期：

    2025年

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  • Paras Singh,
  • Vishal Jaiswal,
  • Subhrajit Roy,
  • Raj Kumar Singh

摘要

With the growing power demand and the imperative for 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 is trained on the numerical data of randomly initialized data points to construct a response surface model. The grey wolf optimization (GWO) algorithm is then utilized to achieve the global maxima on this surface, using the turbine’s power coefficient as the objective function. A comparative analysis is carried out between simulation and experimental data from prior studies to validate the accuracy of the numerical model. The optimized turbine–deflector configuration shows an improvement of 34.24% in power coefficient. Additionally, the GWO algorithm’s effectiveness is compared to Particle Swarm Optimization (PSO) and is found to be better in most cases, converging toward the global maxima faster. This study explores a relatively unexplored realm of metaheuristic optimization of wind turbines using deflectors, for efficient energy harvesting, presenting promising prospects for enhancing renewable sources.