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A two-dimensional Jensen model with a Gaussian-shaped velocity deficit

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  • Ge, Mingwei
  • Wu, Ying
  • Liu, Yongqian
  • Yang, Xiang I.A.

Abstract

The one-dimensional (1D) Jensen model is probably the most often used model for engineering analysis of wind turbine wakes. Identifying a more realistic shape function for the near and far wakes behind a wind turbine and incorporating the identified shape function into a wake model can significantly improve the accuracy of wake modelling. The conventional approach is to first solve the 1D Jensen model and subsequently redistribute the wake using a specified shape function. The above procedure conserves mass globally and is useful in wake modelling. However, it needs to solve a top-hat wake using Jensen model first, which inevitably violates the local mass conservation. In this work, we propose a two-dimensional (2D) wake model that conserves mass locally and globally. The model is a direct extension of Jensen model, and the wake decay rate is the only model parameter. In addition, by accounting for the pressure recovery region, which is often neglected in wake models, the present model can provide accurate prediction of the velocity deficit behind a wind turbine. The present model is compared with the high-fidelity simulations, wind-tunnel measurements, and field observations. A reasonably good agreement is found between the model and the validation data.

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  • 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.
    • Handle: RePEc:eee:renene:v:141:y:2019:i:c:p:46-56
    DOI: 10.1016/j.renene.2019.03.127
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    Cited by:

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    6. 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).
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    9. Ge, Mingwei & Gayme, Dennice F. & Meneveau, Charles, 2021. "Large-eddy simulation of wind turbines immersed in the wake of a cube-shaped building," Renewable Energy, Elsevier, vol. 163(C), pages 1063-1077.
    10. Carl R. Shapiro & Genevieve M. Starke & Charles Meneveau & Dennice F. Gayme, 2019. "A Wake Modeling Paradigm for Wind Farm Design and Control," Energies, MDPI, vol. 12(15), pages 1-19, August.
    11. 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).
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    14. Fan, Xiantao & Ge, Mingwei & Tan, Wei & Li, Qi, 2021. "Impacts of coexisting buildings and trees on the performance of rooftop wind turbines: An idealized numerical study," Renewable Energy, Elsevier, vol. 177(C), pages 164-180.
    15. Li, Xuyang & Qiu, Yingning & Feng, Yanhui & Wang, Zheng, 2021. "Wind turbine power prediction considering wake effects with dual laser beam LiDAR measured yaw misalignment," Applied Energy, Elsevier, vol. 299(C).
    16. Xiong, Xue-Lu & Lyu, Pin & Chen, Wen-Li & Li, Hui, 2020. "Self-similarity in the wake of a semi-submersible offshore wind turbine considering the interaction with the wake of supporting platform," Renewable Energy, Elsevier, vol. 156(C), pages 328-341.
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    18. Lin, Jian Wei & Zhu, Wei Jun & Shen, Wen Zhong, 2022. "New engineering wake model for wind farm applications," Renewable Energy, Elsevier, vol. 198(C), pages 1354-1363.
    19. 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).
    20. Zhou, Huanyu & Qiu, Yingning & Feng, Yanhui & Liu, Jing, 2022. "Power prediction of wind turbine in the wake using hybrid physical process and machine learning models," Renewable Energy, Elsevier, vol. 198(C), pages 568-586.

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