Teja Dasari defends PhD - "Near-wake studies of a utility-scale wind turbine using natural snowfall-based visualization and Particle Image Velocimetry"
Ph.D candidate Teja Dasari successfully defended his Ph.D in Mechanical Engineering on September 5th, 2018. He is advised by Associate Professor Jiarong Hong of the St. Anthony Falls Laboratory and Department of Mechanical Engineering at the University of Minnesota. Congratulations Dr. Dasari!
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Near-wake studies of a utility-scale wind turbine using natural snowfall-based visualization and Particle Image Velocimetry
Teja Dasari, PhD Candidate in Mechanical Engineering
Advisor: Jiarong Hong, Associate Professor of Mechanical Engineering and the St. Anthony Falls Laboratory
Wind turbine technology has been extensively researched in the past century. A major part of this research effort was to improve the efficiencies of energy extraction and the lifespan of the turbines to make the technology economically viable. One important barrier in this regard is the insufficient knowledge on the wakes of the turbines which directly affects the turbine performance and also cause premature failure of the turbines. Only when the complex behavior of wakes is understood can they be successfully controlled/modified/mitigated to improve overall wind turbine/farm efficiencies. In order to achieve this objective, a clear need exists for a complete understanding of wind turbine loading, subsequent vortex system, role of ambient turbulence and coherent turbulent structures within the wake (Sørensen 2011). Furthermore, the overall wake development and behavior is strongly dependent on the near-wake (1 – 4D) characteristics including the coherent structure behavior.
My Ph.D. research work employs natural snowfall based visualization and Particle Image Velocimetry (PIV) techniques to probe the near-wake behavior of the 2.5 MW EOLOS wind turbine located in Rosemount, MN. Over the past 4 years, visualization data were collected with varying fields of view (FOV) ranging from ~35 m x 25 m to ~125 m x 70 m. Especially, extensive smaller FOV data were collected for the behavior of tip vortices while the larger FOV, about the size of a football field, data provided the whole near-wake measurements. The study (i) provided benchmark grade realistic near-wake visualization data needed for the wind energy research community; (ii) Investigated the characteristics of the extreme near-wake in a holistic fashion with unprecedented spatiotemporal resolutions that can provide critical insights on utility-scale wake behavior. (iii) better visualized the wind turbine blade generated coherent structures; (iv) Examined the characteristic behaviors of these coherent structures and explored the correlations with the turbine operation and response characteristics.