Experiments in Fluids

, 54:1459 | Cite as

Three-dimensional flow visualization in the wake of a miniature axial-flow hydrokinetic turbine

  • Leonardo P. Chamorro
  • Daniel R. Troolin
  • Seung-Jae Lee
  • R. E. A. Arndt
  • Fotis Sotiropoulos
Research Article


Three-dimensional 3-component velocity measurements were made in the near wake region of a miniature 3-blade axial-flow turbine within a turbulent boundary layer. The model turbine was placed in an open channel flow and operated under subcritical conditions (Fr = 0.13). The spatial distribution of the basic flow statistics was obtained at various locations to render insights into the spatial features of the wake. Instantaneous and phase-averaged vortical structures were analyzed to get insights about their dynamics. The results showed a wake expansion proportional to the one-third power of the streamwise distance, within the first rotor diameter. Wake rotation was clearly identified up to a distance of roughly three rotor diameters. In particular, relatively high tangential velocity was observed near the wake core, but it was found to be nearly negligible at the turbine tip radius. In contrast, the radial velocity showed the opposite distribution, with higher radial velocity near the turbine tip and, due to symmetry, negligible at the rotor axis. Larger turbulence intensity was found above the hub height and near the turbine tip. Strong coherent tip vortices, visualized in terms of the instantaneous vorticity and the λ 2 criterion, were observed within the first rotor diameter downstream of the turbine. These structures, influenced by the velocity gradient in the boundary layer, appeared to loose their stability at distances greater than two rotor diameters. Hub vortices were also identified. Measurements did not exhibit significant tip–hub vortex interaction within the first rotor diameter.


Wind Turbine Vortical Structure Vortex Interaction Rotor Diameter Streamwise Turbulence Intensity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



Funding was provided by Advanced Water Power Project (Grant No. DE-FG36-08GO18168/M001) and supported by Verdant Power, Department of Energy DOE (DE-EE0002980), and Xcel Energy through the Renewable Development Fund (grant RD3-42). The authors gratefully acknowledge the assistance of Prof. Fernando Porté-Agel during the first stage of the experiments.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Leonardo P. Chamorro
    • 1
  • Daniel R. Troolin
    • 2
  • Seung-Jae Lee
    • 1
  • R. E. A. Arndt
    • 1
  • Fotis Sotiropoulos
    • 1
  1. 1.Saint Anthony Falls Laboratory, Department of Civil EngineeringUniversity of MinnesotaMinneapolisUSA
  2. 2.Fluid Mechanic DivisionTSI IncorporatedSt. PaulUSA

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