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A novel three-dimensional analytical model of the added streamwise turbulence intensity for wind-turbine wakes

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  • Li, Li
  • Huang, Zhi
  • Ge, Mingwei
  • Zhang, Qiying

Abstract

Accurate prediction of the streamwise turbulence intensity in turbine wakes is of great significance to the layout of wind turbines. However, significant knowledge gaps exist on how the streamwise turbulence intensity changes in the turbine wakes, and we still lack an accurate model to predict the added turbulent intensity in the wakes. To this end, the wakes of a single turbine with different thrust coefficients and ground roughness heights are studied via large-eddy simulation under neutral atmospheric condition. To the best of our knowledge, it is the first time that we find that, like the velocity deficit of the wakes, the added streamwise turbulence intensity (ΔIu) also exhibits a self-similar characteristic and expands linearly with the downstream. Based on the findings, a novel three-dimensional analytical model of ΔIu is proposed. Considering the ground effect, a new correction function is also constructed to superpose on the proposed model. Data from both wind tunnel experiments and large-eddy simulations are introduced to validate the proposed model. The results demonstrate that the proposed model can accurately predict the variation of the streamwise turbulence intensity in both the vertical and horizontal directions, and the prediction accuracy is higher than that of the existing models.

Suggested Citation

  • Li, Li & Huang, Zhi & Ge, Mingwei & Zhang, Qiying, 2022. "A novel three-dimensional analytical model of the added streamwise turbulence intensity for wind-turbine wakes," Energy, Elsevier, vol. 238(PB).
    • Handle: RePEc:eee:energy:v:238:y:2022:i:pb:s0360544221020545
    DOI: 10.1016/j.energy.2021.121806
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    References listed on IDEAS

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    Cited by:

    1. Cao, Lichao & Ge, Mingwei & Gao, Xiaoxia & Du, Bowen & Li, Baoliang & Huang, Zhi & Liu, Yongqian, 2022. "Wind farm layout optimization to minimize the wake induced turbulence effect on wind turbines," Applied Energy, Elsevier, vol. 323(C).
    2. Xiaohao Liu & Zhaobin Li & Xiaolei Yang & Duo Xu & Seokkoo Kang & Ali Khosronejad, 2022. "Large-Eddy Simulation of Wakes of Waked Wind Turbines," Energies, MDPI, vol. 15(8), pages 1-26, April.
    3. 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).
    4. Arslan Salim Dar & Fernando Porté-Agel, 2022. "An Analytical Model for Wind Turbine Wakes under Pressure Gradient," Energies, MDPI, vol. 15(15), pages 1-13, July.
    5. 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).
    6. 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.
    7. Zhang, Yi & Li, Zhaobin & Liu, Xiaohao & Sotiropoulos, Fotis & Yang, Xiaolei, 2023. "Turbulence in waked wind turbine wakes: Similarity and empirical formulae," Renewable Energy, Elsevier, vol. 209(C), pages 27-41.
    8. Wang, Tengyuan & Cai, Chang & Wang, Xinbao & Wang, Zekun & Chen, Yewen & Song, Juanjuan & Xu, Jianzhong & Zhang, Yuning & Li, Qingan, 2023. "A new Gaussian analytical wake model validated by wind tunnel experiment and LiDAR field measurements under different turbulent flow," Energy, Elsevier, vol. 271(C).

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