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An improved dynamic model for wind-turbine wake flow

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  • Feng, Dachuan
  • Gupta, Vikrant
  • Li, Larry K.B.
  • Wan, Minping

Abstract

We present an improved dynamic model to predict the time-varying characteristics of the far-wake flow behind a wind turbine. Our model, based on the FAST.Farm engineering model, is novel in that it estimates the turbulence generated by convective instabilities, which selectively amplifies the inflow velocity fluctuations. Our model also incorporates scale dependence when calculating the wake meandering induced by the passive wake meandering mechanism. For validation, our model is compared with FAST.Farm and large-eddy simulation (LES). For the mean flow, our model agrees well with LES in terms of the wake deficit and wake width, but the FAST.Farm model underestimates the former and overestimates the latter. For the instantaneous flow, our model predicts well the wake-center deflection and turbulent kinetic energy, reducing the discrepancies in the spectral characteristics by more than a factor of two relative to LES, depending on the Strouhal number. By incorporating two key mechanisms governing the far-wake dynamics, our model can predict more accurately the dynamic wake evolution, making it suitable for real-time calculations of wind farm performance.

Suggested Citation

  • Feng, Dachuan & Gupta, Vikrant & Li, Larry K.B. & Wan, Minping, 2024. "An improved dynamic model for wind-turbine wake flow," Energy, Elsevier, vol. 290(C).
    • Handle: RePEc:eee:energy:v:290:y:2024:i:c:s0360544223035612
    DOI: 10.1016/j.energy.2023.130167
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    References listed on IDEAS

    as
    1. 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.
    2. Feng, Dachuan & Li, Larry K.B. & Gupta, Vikrant & Wan, Minping, 2022. "Componentwise influence of upstream turbulence on the far-wake dynamics of wind turbines," Renewable Energy, Elsevier, vol. 200(C), pages 1081-1091.
    3. David Bastine & Lukas Vollmer & Matthias Wächter & Joachim Peinke, 2018. "Stochastic Wake Modelling Based on POD Analysis," Energies, MDPI, vol. 11(3), pages 1-29, March.
    4. Jae Sang Moon & Lance Manuel & Matthew J. Churchfield & Sang Lee & Paul S. Veers, 2017. "Toward Development of a Stochastic Wake Model: Validation Using LES and Turbine Loads," Energies, MDPI, vol. 11(1), pages 1-34, December.
    5. Carl R. Shapiro & Genevieve M. Starke & Charles Meneveau & Dennice F. Gayme, 2019. "A Wake Modeling Paradigm for Wind Farm Design and Control," Energies, MDPI, vol. 12(15), pages 1-19, August.
    6. David Bastine & Björn Witha & Matthias Wächter & Joachim Peinke, 2015. "Towards a Simplified DynamicWake Model Using POD Analysis," Energies, MDPI, vol. 8(2), pages 1-26, January.
    7. Bastankhah, Majid & Porté-Agel, Fernando, 2014. "A new analytical model for wind-turbine wakes," Renewable Energy, Elsevier, vol. 70(C), pages 116-123.
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