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Natural snowfall reveals large-scale flow structures in the wake of a 2.5-MW wind turbine

Author

Listed:
  • Jiarong Hong

    (Saint Anthony Falls Laboratory
    University of Minnesota)

  • Mostafa Toloui

    (Saint Anthony Falls Laboratory
    University of Minnesota)

  • Leonardo P. Chamorro

    (Saint Anthony Falls Laboratory
    University of Illinois at Urbana-Champaign)

  • Michele Guala

    (Saint Anthony Falls Laboratory
    University of Minnesota)

  • Kevin Howard

    (Saint Anthony Falls Laboratory
    University of Minnesota)

  • Sean Riley

    (University of Minnesota)

  • James Tucker

    (Saint Anthony Falls Laboratory)

  • Fotis Sotiropoulos

    (Saint Anthony Falls Laboratory
    University of Minnesota)

Abstract

To improve power production and structural reliability of wind turbines, there is a pressing need to understand how turbines interact with the atmospheric boundary layer. However, experimental techniques capable of quantifying or even qualitatively visualizing the large-scale turbulent flow structures around full-scale turbines do not exist today. Here we use snowflakes from a winter snowstorm as flow tracers to obtain velocity fields downwind of a 2.5-MW wind turbine in a sampling area of ~\n36 × 36 m2. The spatial and temporal resolutions of the measurements are sufficiently high to quantify the evolution of blade-generated coherent motions, such as the tip and trailing sheet vortices, identify their instability mechanisms and correlate them with turbine operation, control and performance. Our experiment provides an unprecedented in situ characterization of flow structures around utility-scale turbines, and yields significant insights into the Reynolds number similarity issues presented in wind energy applications.

Suggested Citation

  • Jiarong Hong & Mostafa Toloui & Leonardo P. Chamorro & Michele Guala & Kevin Howard & Sean Riley & James Tucker & Fotis Sotiropoulos, 2014. "Natural snowfall reveals large-scale flow structures in the wake of a 2.5-MW wind turbine," Nature Communications, Nature, vol. 5(1), pages 1-9, September.
    • Handle: RePEc:nat:natcom:v:5:y:2014:i:1:d:10.1038_ncomms5216
    DOI: 10.1038/ncomms5216
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    Cited by:

    1. Dong, Guodan & Li, Zhaobin & Qin, Jianhua & Yang, Xiaolei, 2022. "Predictive capability of actuator disk models for wakes of different wind turbine designs," Renewable Energy, Elsevier, vol. 188(C), pages 269-281.
    2. El Fajri, Oumnia & Bowman, Joshua & Bhushan, Shanti & Thompson, David & O'Doherty, Tim, 2022. "Numerical study of the effect of tip-speed ratio on hydrokinetic turbine wake recovery," Renewable Energy, Elsevier, vol. 182(C), pages 725-750.

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