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Analysis of the anisotropy aerodynamic characteristics of downstream wind turbine considering the 3D wake expansion based on coupling method

Author

Listed:
  • Xu, Zongyuan
  • Gao, Xiaoxia
  • Zhang, Huanqiang
  • Lv, Tao
  • Han, Zhonghe
  • Zhu, Xiaoxun
  • Wang, Yu

Abstract

The inhomogeneous wake velocity distribution caused by the variation of relative position has a great impact on aerodynamic anisotropy characteristics of downstream wind turbine (WT). To accurately and efficiently evaluate the aerodynamic anisotropy of the downstream WT under wake inflow, an aero-elastic-servo model combined with the 3D Jensen-Gaussian (3DJG) wake model and OpenFAST was proposed in this paper. The accuracy of the model was verified by comparing with Jonkman's research. The effect of the relative installation position of the two 5 MW turbines on the aerodynamic anisotropy of the downstream WT was analyzed. Results indicate that the power loss of the downstream WT can be up to 17.47%–51.53% in the case investigated in this study. Due to the 3D wake expansion and the momentum mixing effect, relative axial distance and relative radial distance alternately become the dominant factor for rotor power recovery when y/R < 1 and y/R ≥ 1. The phenomenon mentioned in rotor power is also indicated in downstream rotor torque and thrust. Due to the inhomogeneous wind velocity distribution of the wake inflow, the standard deviation and average value of the blade root flapwise moment (BRFM) suffered by downstream WT are 10.84% and 24.30% larger than that suffered by upstream WT respectively. This paper provides better guidance for optimizing the WT layout and quantification of the wake effect on downstream WT.

Suggested Citation

  • Xu, Zongyuan & Gao, Xiaoxia & Zhang, Huanqiang & Lv, Tao & Han, Zhonghe & Zhu, Xiaoxun & Wang, Yu, 2023. "Analysis of the anisotropy aerodynamic characteristics of downstream wind turbine considering the 3D wake expansion based on coupling method," Energy, Elsevier, vol. 263(PD).
    • Handle: RePEc:eee:energy:v:263:y:2023:i:pd:s0360544222028481
    DOI: 10.1016/j.energy.2022.125962
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    as
    1. Li, Qing'an & Murata, Junsuke & Endo, Masayuki & Maeda, Takao & Kamada, Yasunari, 2016. "Experimental and numerical investigation of the effect of turbulent inflow on a Horizontal Axis Wind Turbine (Part I: Power performance)," Energy, Elsevier, vol. 113(C), pages 713-722.
    2. Fei, Zhao & Tengyuan, Wang & Xiaoxia, Gao & Haiying, Sun & Hongxing, Yang & Zhonghe, Han & Yu, Wang & Xiaoxun, Zhu, 2020. "Experimental study on wake interactions and performance of the turbines with different rotor-diameters in adjacent area of large-scale wind farm," Energy, Elsevier, vol. 199(C).
    3. 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).
    4. Edmunds, Matt & Williams, Alison J. & Masters, Ian & Banerjee, Arindam & VanZwieten, James H., 2020. "A spatially nonlinear generalised actuator disk model for the simulation of horizontal axis wind and tidal turbines," Energy, Elsevier, vol. 194(C).
    5. van Dijk, Mike T. & van Wingerden, Jan-Willem & Ashuri, Turaj & Li, Yaoyu, 2017. "Wind farm multi-objective wake redirection for optimizing power production and loads," Energy, Elsevier, vol. 121(C), pages 561-569.
    6. Kim, Dae-Young & Kim, Yeon-Hee & Kim, Bum-Suk, 2021. "Changes in wind turbine power characteristics and annual energy production due to atmospheric stability, turbulence intensity, and wind shear," Energy, Elsevier, vol. 214(C).
    7. Gao, Xiaoxia & Li, Bingbing & Wang, Tengyuan & Sun, Haiying & Yang, Hongxing & Li, Yonghua & Wang, Yu & Zhao, Fei, 2020. "Investigation and validation of 3D wake model for horizontal-axis wind turbines based on filed measurements," Applied Energy, Elsevier, vol. 260(C).
    8. Liu, Weiqi & Liu, Weixing & Zhang, Liang & Sheng, Qihu & Zhou, Binzhen, 2018. "A numerical model for wind turbine wakes based on the vortex filament method," Energy, Elsevier, vol. 157(C), pages 561-570.
    9. Qian, Guo-Wei & Song, Yun-Peng & Ishihara, Takeshi, 2022. "A control-oriented large eddy simulation of wind turbine wake considering effects of Coriolis force and time-varying wind conditions," Energy, Elsevier, vol. 239(PA).
    10. Li, Qing'an & Murata, Junsuke & Endo, Masayuki & Maeda, Takao & Kamada, Yasunari, 2016. "Experimental and numerical investigation of the effect of turbulent inflow on a Horizontal Axis Wind Turbine (part II: Wake characteristics)," Energy, Elsevier, vol. 113(C), pages 1304-1315.
    11. Gao, Xiaoxia & Yang, Hongxing & Lu, Lin, 2016. "Optimization of wind turbine layout position in a wind farm using a newly-developed two-dimensional wake model," Applied Energy, Elsevier, vol. 174(C), pages 192-200.
    12. Li, Qing'an & Cai, Chang & Kamada, Yasunari & Maeda, Takao & Hiromori, Yuto & Zhou, Shuni & Xu, Jianzhong, 2021. "Prediction of power generation of two 30 kW Horizontal Axis Wind Turbines with Gaussian model," Energy, Elsevier, vol. 231(C).
    13. Tao, Siyu & Xu, Qingshan & Feijóo, Andrés & Zheng, Gang & Zhou, Jiemin, 2020. "Nonuniform wind farm layout optimization: A state-of-the-art review," Energy, Elsevier, vol. 209(C).
    14. Hegazy, Amr & Blondel, Frédéric & Cathelain, Marie & Aubrun, Sandrine, 2022. "LiDAR and SCADA data processing for interacting wind turbine wakes with comparison to analytical wake models," Renewable Energy, Elsevier, vol. 181(C), pages 457-471.
    15. Sun, Haiying & Gao, Xiaoxia & Yang, Hongxing, 2019. "Validations of three-dimensional wake models with the wind field measurements in complex terrain," Energy, Elsevier, vol. 189(C).
    16. Liu, Weiqi & Shi, Jian & Chen, Hailong & Liu, Hengxu & Lin, Zi & Wang, Lingling, 2021. "Lagrangian actuator model for wind turbine wake aerodynamics," Energy, Elsevier, vol. 232(C).
    17. González-Longatt, F. & Wall, P. & Terzija, V., 2012. "Wake effect in wind farm performance: Steady-state and dynamic behavior," Renewable Energy, Elsevier, vol. 39(1), pages 329-338.
    18. Yan, Shu & Shi, Shaoping & Chen, Xinming & Wang, Xiaodong & Mao, Linzhi & Liu, Xiaojie, 2018. "Numerical simulations of flow interactions between steep hill terrain and large scale wind turbine," Energy, Elsevier, vol. 151(C), pages 740-747.
    19. Chanprasert, W. & Sharma, R.N. & Cater, J.E. & Norris, S.E., 2022. "Large Eddy Simulation of wind turbine fatigue loading and yaw dynamics induced by wake turbulence," Renewable Energy, Elsevier, vol. 190(C), pages 208-222.
    20. Micallef, Daniel & Rezaeiha, Abdolrahim, 2021. "Floating offshore wind turbine aerodynamics: Trends and future challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).
    21. Tian, Linlin & Song, Yilei & Zhao, Ning & Shen, Wenzhong & Zhu, Chunling & Wang, Tongguang, 2020. "Effects of turbulence modelling in AD/RANS simulations of single wind & tidal turbine wakes and double wake interactions," Energy, Elsevier, vol. 208(C).
    22. Shen, Xin & Zhu, Xiaocheng & Du, Zhaohui, 2011. "Wind turbine aerodynamics and loads control in wind shear flow," Energy, Elsevier, vol. 36(3), pages 1424-1434.
    23. Siahpour, Shahin & Khakiani, Fardad N. & Fazlollahi, Vahid & Golozar, Ali & Shirazi, Farzad A., 2021. "Morphing Omni-directional Panel Mechanism: A novel active roof design for improving the performance of the wind delivery system," Energy, Elsevier, vol. 217(C).
    24. Yamaki, Ayumi & Kanematsu, Yuichiro & Kikuchi, Yasunori, 2020. "Lifecycle greenhouse gas emissions of thermal energy storage implemented in a paper mill for wind energy utilization," Energy, Elsevier, vol. 205(C).
    25. Qian, Guo-Wei & Ishihara, Takeshi, 2021. "Wind farm power maximization through wake steering with a new multiple wake model for prediction of turbulence intensity," Energy, Elsevier, vol. 220(C).
    26. Sarah O’Meara & Yvaine Ye, 2022. "Four research teams powering China’s net-zero energy goal," Nature, Nature, vol. 603(7902), pages 41-43, March.
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