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A novel superposition method for streamwise turbulence intensity of wind-turbine wakes

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  • Li, Li
  • Wang, Bing
  • Ge, Mingwei
  • Huang, Zhi
  • Li, Xintao
  • Liu, Yongqian

Abstract

The prediction of the streamwise turbulence intensity distribution in a wind farm is of great significance for wind turbine micrositing. However, most studies only focus on the superposition methods of velocity deficit in turbine wakes, and high-precision superposition models for turbulence intensity are still lacking. To address this issue, wakes of a column of aligned wind turbines with different ground roughness heights are studied via high-fidelity large-eddy simulation. It is found that the variation of added streamwise turbulence intensity with streamwise direction is very similar to that of velocity deficit; however, there are distinct differences regarding recovery rate and incidence. Four common superposition methods extended from wake velocity are examined for turbulence intensity, and they all fail to accurately predict the superposition effect of turbulence intensity. To remedy this, a normalized superposition formula of turbulence intensity is proposed. In this formula, the parameter can be adjusted to alter the superposition ratio for downstream turbines, and it can encompass existing common superposition models. Both large-eddy simulations and wind tunnel experiments demonstrate that the 2.5 times superposition (model parameter = 2.5) method can accurately predict the distribution of streamwise turbulence intensity for aligned turbines, providing superior prediction accuracy compared to existing models.

Suggested Citation

  • Li, Li & Wang, Bing & Ge, Mingwei & Huang, Zhi & Li, Xintao & Liu, Yongqian, 2023. "A novel superposition method for streamwise turbulence intensity of wind-turbine wakes," Energy, Elsevier, vol. 276(C).
    • Handle: RePEc:eee:energy:v:276:y:2023:i:c:s036054422300885x
    DOI: 10.1016/j.energy.2023.127491
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    References listed on IDEAS

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    1. Wu, Yu-Ting & Porté-Agel, Fernando, 2015. "Modeling turbine wakes and power losses within a wind farm using LES: An application to the Horns Rev offshore wind farm," Renewable Energy, Elsevier, vol. 75(C), pages 945-955.
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    8. Ge, Mingwei & Wu, Ying & Liu, Yongqian & Yang, Xiang I.A., 2019. "A two-dimensional Jensen model with a Gaussian-shaped velocity deficit," Renewable Energy, Elsevier, vol. 141(C), pages 46-56.
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    Cited by:

    1. Aditya H. Bhatt & Mireille Rodrigues & Federico Bernardoni & Stefano Leonardi & Armin Zare, 2023. "Stochastic Dynamical Modeling of Wind Farm Turbulence," Energies, MDPI, vol. 16(19), pages 1-24, September.

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