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Study on the Wake of a Miniature Wind Turbine Using the Reynolds Stress Model

Author

Listed:
  • Jianxiao Hu

    (Beijing’s Key Laboratory of Structural Wind Engineering and Urban Wind Environment, School of Civil Engineering, Beijing Jiaotong University, Beijing 100044, China)

  • Qingshan Yang

    (Beijing’s Key Laboratory of Structural Wind Engineering and Urban Wind Environment, School of Civil Engineering, Beijing Jiaotong University, Beijing 100044, China)

  • Jian Zhang

    (Beijing’s Key Laboratory of Structural Wind Engineering and Urban Wind Environment, School of Civil Engineering, Beijing Jiaotong University, Beijing 100044, China)

Abstract

The Reynolds Stress Model (RSM) is adopted to simulate the wind turbine wake and the simulation results are compared with the wind tunnel test data, simulation results from the standard k-ε model and a modified k-ε model. RSM shows good performance in predicting the turbine wakes velocity, turbulence intensity and the kinetic shear stress, while the k-ε based models fail to predict either wakes velocity or turbulence intensity. Simulation results show that the wake velocity will be recovered up to 90% at around 10 D downstream of the turbine (D denotes turbine rotor diameter) and it stops at 91% at around 16 D downstream. The wake turbulence intensity reaches a maximum at around 5 D downstream of turbine. Further investigation shows that the horizontal profile of the wakes velocity can be approximated by a Gaussian distribution, and the turbulence intensity can be approximated by a bimodal distribution. The influence of the wakes effect is limited to within ±D in the across-wind direction. The turbine wakes show clear anisotropy, which could explain the incorrect estimation on the turbulence intensity with the extended k-ε model.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:10:p:784-:d:79285
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    Citations

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    Cited by:

    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).
    2. 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.
    3. 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).
    4. Liu, Haixiao & Fu, Jianing & Liang, Zetao & Liang, Zhichang & Zhang, Yuming & Xiao, Zhong, 2022. "A simple method of fast evaluating full-field wake velocities for arbitrary wind turbine arrays on complex terrains," Renewable Energy, Elsevier, vol. 201(P1), pages 961-976.
    5. 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.

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