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Investigation of aerodynamic forces and flow field of an H-type vertical axis wind turbine based on bionic airfoil

Author

Listed:
  • Zhang, Yanfeng
  • Guo, Zhiping
  • Zhu, Xinyu
  • Li, Yuan
  • Song, Xiaowen
  • Cai, Chang
  • Kamada, Yasunari
  • Maeda, Takao
  • Li, Qing’an

Abstract

This paper investigates the effect of bionic airfoil on the aerodynamic characteristic and flow field of an H-type vertical axis wind turbine (VAWT). The pressure acting on the blade surface and flow field around the blade were predicted at different tip speed ratios (TSRs) and the angles of attack were predicted by numerical simulation. As a result, it was showed that the maximum value of lift force coefficient was 0.723 for baseline airfoil at the angle of attack 12° and was 0.743 for bionic airfoil at the angle of attack 14°. When the angle of attack was equal or greater than 14°, the static bionic airfoil showed better lift characteristic. Moreover, the maximum value of power coefficient occurred at TSR = 2.58 for the simulation, but the maximum value of torque coefficient was identified at TSR = 2.19. Meanwhile, the growth rates of the power coefficient were 7.02% at TSR = 1.38, 7.35% at TSR = 2.19, and 3.42% at TSR = 2.58 for simulation between baseline blades and bionic blades. The power performance of VAWT improved in dynamic stall for bionic airfoil by delaying stall on the blade's surface and promoting the laminar-to-turbulent transition to improve the power performance of wind turbine at the low TSRs for the mainstream wind velocity of 8.0 m/s. The research results were helpful to predict the performance of VAWTs under various wind environments and to improve the aerodynamic performance of VAWTs.

Suggested Citation

  • Zhang, Yanfeng & Guo, Zhiping & Zhu, Xinyu & Li, Yuan & Song, Xiaowen & Cai, Chang & Kamada, Yasunari & Maeda, Takao & Li, Qing’an, 2022. "Investigation of aerodynamic forces and flow field of an H-type vertical axis wind turbine based on bionic airfoil," Energy, Elsevier, vol. 242(C).
    • Handle: RePEc:eee:energy:v:242:y:2022:i:c:s0360544221032485
    DOI: 10.1016/j.energy.2021.122999
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    References listed on IDEAS

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    Cited by:

    1. Shen, He & Ruiz, Alexis & Li, Ni, 2023. "Fast online reinforcement learning control of small lift-driven vertical axis wind turbines with an active programmable four bar linkage mechanism," Energy, Elsevier, vol. 262(PA).

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