Delhi Technological University Enhances Savonius Wind Turbine Efficiency by 26.94% with Novel Design

Researchers from Delhi Technological University have developed a design optimization framework to improve the efficiency of Savonius wind turbines. The team used a Quantum-based Salp Swarm Optimization (QSSO) algorithm to determine the optimal design parameters for a cylindrical deflector system, which directs airflow to reduce opposing torque on the turbine’s returning blade. The optimized system showed a 26.94% improvement in power coefficient at a Tip Speed Ratio of 0.9. This research could be crucial in optimizing wind turbine systems for efficient energy production, a goal shared by industry and academia alike.

What is the Optimization of Savonius Wind Turbine Cylindrical Deflector System?

The Savonius wind turbine, commonly used in small-scale wind turbine applications, often faces limitations due to opposing torque on the returning blade, which hinders high efficiency. A potential solution to this problem involves directing incoming airflow through a cylindrical deflector. However, the effectiveness of this flow control is highly dependent on the location and size of the cylindrical deflector and its angular velocity.

A team of researchers from the Department of Mechanical Engineering at Delhi Technological University in India has introduced a novel design optimization framework aimed at enhancing the performance of the turbine-deflector system. The team used surrogate models for computational efficiency and assessed six different models. The Kriging model was selected for further analysis due to its superior performance in approximating the relationship between design parameters and the objective function.

The training data for the surrogate model and the flow field data around the system were obtained through Unsteady Reynolds-Averaged Navier Stokes (URANS) simulations using a sliding mesh technique. The team then used an in-house code for the Quantum-based Salp Swarm Optimization (QSSO) algorithm to obtain design parameters corresponding to the peak power coefficient (Cp) for the stationary deflector-turbine system.

How Does the Quantum-Based Salp Swarm Algorithm Compare to Other Algorithms?

The QSSO algorithm was quantitatively compared with nine other competing algorithms. The optimized stationary deflector-turbine system showed an improvement of 26.94% in Cp at a Tip Speed Ratio (TSR) of 0.9 compared to the baseline case.

Further investigation into the effect of deflector rotational velocity (ωd) revealed significant improvements. There was a 40.98% and 11.33% enhancement at ωd 3rads, and 51.23% and 19.42% at ωd 40 rads, compared to configurations without a deflector and with the optimized stationary deflector, respectively, at a TSR of 0.9.

What is the Significance of this Study?

This study introduces a robust optimization framework that not only improves the performance of Savonius turbines but also underscores the potential of surrogate modeling and advanced optimization algorithms in addressing aerodynamic challenges within wind turbine design.

Importantly, the framework’s applicability extends beyond this study, offering opportunities for multiparameter optimization of components across the energy sector. This research could be instrumental in the ongoing efforts to optimize wind turbine systems for efficient energy production, a goal shared by both industry giants and academic researchers.

How Does Wind Energy Fit into the Global Energy Landscape?

The immense potential of clean energy sources such as wind, hydropower, geothermal, solar, and biomass energy has started to receive a lot of attention due to the growing energy needs of the world. The energy crisis has made it essential to find alternative energy sources and optimize the use of existing ones.

Wind energy is among the potential inexhaustible energy sources and is an increasingly developing alternative energy production method. The USA anticipates wind to provide 20% of its total electricity production by 2030. This scenario can only be achieved by addressing the fundamental challenges identified in the context of harvesting wind energy.

What are the Future Prospects for Wind Energy?

According to the Global Wind Energy Council (GWEC), countries investing in wind energy have collectively reached a record-high installed capacity of 906 GW in 2022. An additional 100 GW of new capacity is expected to have been added in 2023.

Despite the economic difficulties and resource limitations faced by wind turbine supply chains due to the pandemic, the GWEC reports an increment of 9% compared to the previous year in the wind power capacity in 2022.

Wind turbine design is an interdisciplinary research field that uses the fundamentals of fields such as turbomachinery, vibrations, aerodynamics, and material science to create efficient and sustainable energy solutions. Large companies like Vestas and Siemens are actively trying to optimize wind turbine systems for efficient energy production. In academia, many researchers have proposed different optimization objectives, algorithms, constraints, and tools to improve wind turbine performance.