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Predictive capability of an improved AD/RANS method for multiple wind turbines and wind farm wakes

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  • Tian, Linlin
  • Song, Yilei
  • Wang, Zhenming
  • Zhao, Ning
  • Zhu, Chunling
  • Lu, Xiyun

Abstract

The actuator disc (AD) model combined with the Reynolds-averaged Navier-Stokes (RANS) model has been demonstrated as the most effective approach for simulating wind farm wakes due to its computational efficiency and acceptable accuracy. However, challenges remain in defining the reference inflow wind speed for the AD model and identifying a robust turbulence model for the RANS method. To solve these issues, two strategies for establishing the AD model (AD-up1D model and AD-local model), along with four turbulence models (standard k-ε, SST k-ω, linear pressure-strain RSM and modified RSM), are comprehensively validated here to predict multiple wakes within two representative wind farms. The temporal variability of multiple turbines’ power output and the significant impact of wind direction on the power is particularly emphasized. Overall, this study indicates that the proposed AD-local/Mod RSM method facilitates a realistic wake recovery in the cumulated wake flow and agrees well with the field measurements. It consistently performs well, never ranking last in any test cases, with percentage deviations ranging from 1.8% to 9.0%. Most importantly, this study evaluates the strengths and weaknesses of each AD/RANS method and provides recommendations for wind farm design purposes.

Suggested Citation

  • Tian, Linlin & Song, Yilei & Wang, Zhenming & Zhao, Ning & Zhu, Chunling & Lu, Xiyun, 2024. "Predictive capability of an improved AD/RANS method for multiple wind turbines and wind farm wakes," Energy, Elsevier, vol. 297(C).
    • Handle: RePEc:eee:energy:v:297:y:2024:i:c:s0360544224009800
    DOI: 10.1016/j.energy.2024.131207
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    References listed on IDEAS

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    1. Shives, Michael & Crawford, Curran, 2016. "Adapted two-equation turbulence closures for actuator disk RANS simulations of wind & tidal turbine wakes," Renewable Energy, Elsevier, vol. 92(C), pages 273-292.
    2. 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.
    3. Edmunds, M. & Williams, A.J. & Masters, I. & Croft, T.N., 2017. "An enhanced disk averaged CFD model for the simulation of horizontal axis tidal turbines," Renewable Energy, Elsevier, vol. 101(C), pages 67-81.
    4. Navarro Diaz, Gonzalo P. & Saulo, A. Celeste & Otero, Alejandro D., 2021. "Full wind rose wind farm simulation including wake and terrain effects for energy yield assessment," Energy, Elsevier, vol. 237(C).
    5. Nguyen, Van Thinh & Guillou, Sylvain S. & Thiébot, Jérôme & Santa Cruz, Alina, 2016. "Modelling turbulence with an Actuator Disk representing a tidal turbine," Renewable Energy, Elsevier, vol. 97(C), pages 625-635.
    6. Archer, Cristina L. & Vasel-Be-Hagh, Ahmadreza & Yan, Chi & Wu, Sicheng & Pan, Yang & Brodie, Joseph F. & Maguire, A. Eoghan, 2018. "Review and evaluation of wake loss models for wind energy applications," Applied Energy, Elsevier, vol. 226(C), pages 1187-1207.
    7. Göçmen, Tuhfe & Giebel, Gregor, 2016. "Estimation of turbulence intensity using rotor effective wind speed in Lillgrund and Horns Rev-I offshore wind farms," Renewable Energy, Elsevier, vol. 99(C), pages 524-532.
    8. Niebuhr, C.M. & Schmidt, S. & van Dijk, M. & Smith, L. & Neary, V.S., 2022. "A review of commercial numerical modelling approaches for axial hydrokinetic turbine wake analysis in channel flow," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    9. Jianxiao Hu & Qingshan Yang & Jian Zhang, 2016. "Study on the Wake of a Miniature Wind Turbine Using the Reynolds Stress Model," Energies, MDPI, vol. 9(10), pages 1-18, September.
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