Super-large-scale particle image velocimetry and flow visualization with natural snowfall is used to collect and analyse multiple datasets in the near wake of a 2.5 MW wind turbine. Each dataset captures the full vertical span of the wake from a different perspective. Together, these datasets compose a three-dimensional picture of the near-wake flow, including the effect of the tower and nacelle and the variation of instantaneous wake expansion in response to changes in turbine operation. A region of high-speed flow is observed directly behind the nacelle, and a region of low-speed flow appears behind the tower. Additionally, the nacelle produces a region of enhanced turbulence in its wake while the tower reduces turbulence near the ground as it breaks up turbulent structures in the boundary layer. Analysis of the instantaneous wake behavior reveals variations in wake expansion - and even periods of wake contraction - occurring in response to changes in angle of attack and blade pitch gradient. This behaviour is found to depend on the region of operation of the turbine. These findings can be incorporated into wake models and advanced control algorithms for wind farm optimization and can be used to validate wind turbine wake simulations.
|Original language||English (US)|
|Journal||Journal of Physics: Conference Series|
|State||Published - Mar 3 2020|
|Event||North American Wind Energy Academy, NAWEA 2019 and the International Conference on Future Technologies in Wind Energy 2019, WindTech 2019 - Amherst, United States|
Duration: Oct 14 2019 → Oct 16 2019
Bibliographical noteFunding Information:
This work was supported by the National Science Foundation CAREER award (NSF-CBET-1454259), Xcel Energy through the Renewable Development Fund (grant RD4-13) as well as IonE of University of Minnesota. We also thank the students and the engineers from St. Anthony Falls Laboratory, including S. Riley, B. Li, Y. Wu, Y. Liu, J. Tucker, C. Ellis, J. Marr, C. Milliren and D. Christopher for their assistance in the experiments
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