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Airfoil design for large horizontal axis wind turbines in low wind speed regions

Author

Listed:
  • Li, Xingxing
  • Zhang, Lei
  • Song, Juanjuan
  • Bian, Fengjiao
  • Yang, Ke

Abstract

Low wind speed technology helps to reduce the cost of energy, but also creates huge challenge on the wind turbine blade design. To essentially address the particular blade requirements in low wind speed regions with high inflow turbulence, this study extends previous airfoil design optimization methods. Firstly, special design criteria were proposed besides traditional considerations, concerning efficiency, loads, noise and in particular the high inflow turbulence effects on the enlarged blades. Then through improved mathematic models and modified auto optimization platform, an extended airfoil design optimization framework dedicated to low wind speed sites was established. The case design results show that key features of the new airfoil tailed to low wind speed sites are effectively enhanced: the design lift coefficient and lift-to-drag ratio are fairly increased, and the maximum lift coefficient is well constrained. In addition, the acoustic parameter of the new airfoil is successfully reduced. More important is that the airfoil performance sensitivity to surface roughness and inflow turbulence intensity are evidently eliminated. These finally contribute to an improved overall performance. The rotor blade performance evaluation with the new design airfoil in further verified the case design. Results indicate the proposed framework is able to design special airfoils suited for site-specific blade requirements, contributed by the customized considerations, parameterization model, robust calculation method and global algorithms.

Suggested Citation

  • Li, Xingxing & Zhang, Lei & Song, Juanjuan & Bian, Fengjiao & Yang, Ke, 2020. "Airfoil design for large horizontal axis wind turbines in low wind speed regions," Renewable Energy, Elsevier, vol. 145(C), pages 2345-2357.
  • Handle: RePEc:eee:renene:v:145:y:2020:i:c:p:2345-2357
    DOI: 10.1016/j.renene.2019.07.163
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    Cited by:

    1. Wang, Yuqi & Liu, Tianyuan & Meng, Yue & Zhang, Di & Xie, Yonghui, 2022. "Integrated optimization for design and operation of turbomachinery in a solar-based Brayton cycle based on deep learning techniques," Energy, Elsevier, vol. 252(C).

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