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AD/RANS Simulations of Wind Turbine Wake Flow Employing the RSM Turbulence Model: Impact of Isotropic and Anisotropic Inflow Conditions

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  • Linlin Tian

    (Jiangsu Key Laboratory of Hi-Tech Research for Wind Turbine Design, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

  • Yilei Song

    (Jiangsu Key Laboratory of Hi-Tech Research for Wind Turbine Design, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

  • Ning Zhao

    (Jiangsu Key Laboratory of Hi-Tech Research for Wind Turbine Design, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

  • Wenzhong Shen

    (Department of Wind Energy, Fluid Mechanics Section, Technical University of Denmark, DK-2800 Lyngby, Denmark)

  • Tongguang Wang

    (Jiangsu Key Laboratory of Hi-Tech Research for Wind Turbine Design, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

Abstract

The Reynolds-averaged Navier–Stokes (RANS)-based generalized actuator disc method along with the Reynolds stress model (AD/RANS_RSM) is assessed for wind turbine wake simulation. The evaluation is based on validations with four sets of experiments for four horizontal-axis wind turbines with different geometrical characteristics operating in a wide range of wind conditions. Additionally, sensitivity studies on inflow profiles (representing isotropic and anisotropic turbulence) for predicting wake effects are carried out. The focus is on the prediction of the evolution of wake flow in terms of wind velocity and turbulence intensity. Comparisons between the computational results and the measurements demonstrate that in the near and transition wake region with strong anisotropic turbulence, the AD/RANS_RSM methodology exhibits a reasonably good match with all the experimental data sets; however, in the far wake region that is characterized by isotropic turbulence, the AD/RANS_RSM predicts the wake velocity quite accurately but appears to over-estimate the wake turbulence level. While the introduction of the overall turbulence intensity is found to give an improved agreement with the experiments. The performed sensitivity study proves that the anisotropic inflow condition is recommended as the profile of choice to represent the incoming wind flow.

Suggested Citation

  • Linlin Tian & Yilei Song & Ning Zhao & Wenzhong Shen & Tongguang Wang, 2019. "AD/RANS Simulations of Wind Turbine Wake Flow Employing the RSM Turbulence Model: Impact of Isotropic and Anisotropic Inflow Conditions," Energies, MDPI, vol. 12(21), pages 1-14, October.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:21:p:4026-:d:279308
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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. Stevens, Richard J.A.M. & Martínez-Tossas, Luis A. & Meneveau, Charles, 2018. "Comparison of wind farm large eddy simulations using actuator disk and actuator line models with wind tunnel experiments," Renewable Energy, Elsevier, vol. 116(PA), pages 470-478.
    3. Rocha, P.A. Costa & Rocha, H.H. Barbosa & Carneiro, F.O. Moura & Vieira da Silva, M.E. & Bueno, A. Valente, 2014. "k–ω SST (shear stress transport) turbulence model calibration: A case study on a small scale horizontal axis wind turbine," Energy, Elsevier, vol. 65(C), pages 412-418.
    4. 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.
    5. Antonini, Enrico G.A. & Romero, David A. & Amon, Cristina H., 2019. "Improving CFD wind farm simulations incorporating wind direction uncertainty," Renewable Energy, Elsevier, vol. 133(C), pages 1011-1023.
    6. Huilai Ren & Xiaodong Zhang & Shun Kang & Sichao Liang, 2018. "Actuator Disc Approach of Wind Turbine Wake Simulation Considering Balance of Turbulence Kinetic Energy," Energies, MDPI, vol. 12(1), pages 1-19, December.
    7. Bai, Guanghui & Li, Jun & Fan, Pengfei & Li, Guojun, 2013. "Numerical investigations of the effects of different arrays on power extractions of horizontal axis tidal current turbines," Renewable Energy, Elsevier, vol. 53(C), pages 180-186.
    8. Göçmen, Tuhfe & Laan, Paul van der & Réthoré, Pierre-Elouan & Diaz, Alfredo Peña & Larsen, Gunner Chr. & Ott, Søren, 2016. "Wind turbine wake models developed at the technical university of Denmark: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 752-769.
    9. Chen, Yaling & Lin, Binliang & Lin, Jie & Wang, Shujie, 2017. "Experimental study of wake structure behind a horizontal axis tidal stream turbine," Applied Energy, Elsevier, vol. 196(C), pages 82-96.
    10. 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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    1. 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).
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