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Aerodynamic performance improvement analysis of Savonius Vertical Axis Wind Turbine utilizing plasma excitation flow control

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  • Xu, Wen
  • Li, Cheng-cheng
  • Huang, Sheng-xian
  • Wang, Ying

Abstract

Drag type Savonius Vertical Axis Wind Turbine (VAWT) has the advantages of the low total cost, simple manufacture and maintenance. Although the efficiency is low, it is suitable for residential areas with low wind speed. This article intends to use flow control methods to improve the efficiency of Savonius VAWT. Plasma excitation flow control is a relatively new type of active flow control technology. Therefore, this article uses the User-Defined Function (UDF) in ANSYS Fluent to load the plasma excitation sources on the Savonius VAWT blades. This paper numerically studies the influence of plasma flow control on the aerodynamic performance of a two-blade Savonius VAWT. Firstly, the changes in wind turbine performance under different excitation parameters are analyzed. The research results show that adding plasma exciter at a reasonable position can effectively improve the performance of the wind turbine, and the electric field force distribution in different directions will effect its range of action. Meanwhile, the voltage excitation frequency is also an influencing factor. Secondly, the principle and process of plasma excitation on the Savonius VAWT are analyzed. Finally, the relationship between the power consumed by the plasma and the increased power to drive the wind turbine is analyzed. Besides, the influence of plasma excitation on the overall performance of the wind turbine is summarized.

Suggested Citation

  • Xu, Wen & Li, Cheng-cheng & Huang, Sheng-xian & Wang, Ying, 2022. "Aerodynamic performance improvement analysis of Savonius Vertical Axis Wind Turbine utilizing plasma excitation flow control," Energy, Elsevier, vol. 239(PD).
    • Handle: RePEc:eee:energy:v:239:y:2022:i:pd:s0360544221023811
    DOI: 10.1016/j.energy.2021.122133
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    References listed on IDEAS

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    2. Gao, Xiaoxia & Zhang, Shaohai & Li, Luqing & Xu, Shinai & Chen, Yao & Zhu, Xiaoxun & Sun, Haiying & Wang, Yu & Lu, Hao, 2022. "Quantification of 3D spatiotemporal inhomogeneity for wake characteristics with validations from field measurement and wind tunnel test," Energy, Elsevier, vol. 254(PA).

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