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A novel double-Gaussian full wake model for wind turbines considering dependence on thrust coefficient and ambient turbulence intensity

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  • Qian, Guo-Wei
  • Ishihara, Takeshi

Abstract

A novel full wake model using a double-Gaussian function is derived in this study. Firstly, the full wake characteristics under different inflow and turbine operation conditions are investigated using large eddy simulation. The ambient turbulence intensity and thrust coefficient are found to be the key parameters that determine the wake recovery rate and the distance where double-peak velocity deficits transition to one-peak distribution. A novel double-Gaussian wake model is then proposed to estimate the mean velocity deficit in both the near and far wake region. A linear wake expansion rate and non-linear Gaussian minima are demonstrated and utilized to describe the shape transition of velocity deficit from near-wake to far-wake region. All the parameters in the model are expressed as a function of thrust coefficient and ambient turbulence intensity. Finally, the proposed model is validated using a set of LES results and experimental data. The predicted velocity profiles in the near wake region by the proposed model show good agreement with LES and measurements. Furthermore, the proposed full wake model is applied to Horns Rev. offshore wind farm and provides good accuracy for power prediction in the multiple wakes as well. The applications of this new full wake model include, but are not limited to turbine layout optimization, farm control, and repower of existing wind farms.

Suggested Citation

  • Qian, Guo-Wei & Ishihara, Takeshi, 2025. "A novel double-Gaussian full wake model for wind turbines considering dependence on thrust coefficient and ambient turbulence intensity," Applied Energy, Elsevier, vol. 391(C).
    • Handle: RePEc:eee:appene:v:391:y:2025:i:c:s0306261925005896
    DOI: 10.1016/j.apenergy.2025.125859
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    References listed on IDEAS

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    2. Song, Yun-Peng & Ishihara, Takeshi, 2025. "A novel dynamic wake model for prediction of wind speed and power production considering wake propagation velocity and deflection," Applied Energy, Elsevier, vol. 400(C).

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