Near-wake studies of a utility-scale wind turbine using natural snowfall-based visualization and Particle Image Velocimetry
2018-12
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Near-wake studies of a utility-scale wind turbine using natural snowfall-based visualization and Particle Image Velocimetry
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2018-12
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Abstract
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 affect the turbine performance and also cause premature failure of the turbines (Frandsen et al. 2006; Barthelmie et al. 2007). 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. The current research work employs natural snowfall based visualization techniques demonstrated by Toloui et al. 2014 and Hong et al. 2014 to further probe the near-wake behavior of the 2.5 MW EOLOS wind turbine located in Rosemount, MN. The broad objectives of the study are to examine the near-wake (within 0.2 to 1 D) dynamics under different regions of turbine operation, ambient conditions and turbine loading. The specific objectives are to (i) better visualize the wind turbine blade generated coherent structures; (ii) study the characteristic behaviors of these coherent structures and explore correlations with the turbine operation and response characteristics; (iii) provide unique and realistic near-wake visualization data to the wind energy research community; (iv) explore 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. Accordingly, visualization data are collected with varying regions of interest ranging from ~ 20 m to ~120 m. The typical whole-wake measurements include visualization and super-large-scale Particle Image Velocimetry (SLPIV) measurements in the near wake of the turbine in a field of view (FOV) of the size of a football field (~ 120 m vertical × 70 m streamwise). The SLPIV measurements provide velocity deficit and turbulent kinetic energy assessments over the entire rotor span. The instantaneous velocity fields from SLPIV indicate the presence of intermittent wake contraction states which are in clear contrast with the expansion states typically associated with wind turbine wakes. These contraction states feature a pronounced upsurge of velocity in the central portion of the wake. The wake velocity ratio R_w, defined as the ratio of the spatially-averaged velocity of the inner wake to that of the outer wake, is introduced to categorize instantaneous near wake into expansion (R_w<1) and contraction states (R_w>1). Based on R_w criterion, the wake contraction occurs 25% of the time during the 30-minute time duration of SLPIV measurements. The contraction states are found to be correlated with the rate of change of blade pitch by examining the distribution and samples of time sequences of wake states with different turbine operation parameters. Moreover, blade pitch change is shown to be strongly correlated to the tower and blade strains measured on the turbine, and the result suggests that the flexing of the turbine tower and the blades could indeed lead to the interaction of rotor with the turbine wake, causing wake contraction. Similar visualization data collected along the wake symmetry plane, along the tower axis, revealed an accelerating flow field behind the nacelle of the turbine. This region is also characterized by relatively higher turbulence characteristics due to the shear production of TKE. This region of TKE with relatively high values (or peak in TKE) is found to waver about the hub elevation which might be an effect of yaw error on the turbine. The smaller field of view studies representing visualization of tip vortex behavior, near the elevation corresponding to the bottom blade tip, over a broad range of turbine operational conditions, demonstrate the presence of the state of consistent vortex formation as well as various types of disturbed vortex states. The histograms corresponding to the consistent and disturbed states are examined over a number of turbine operation/response parameters, including turbine power and tower strain as well as the fluctuation of these quantities, with different conditional sampling restrictions. This analysis establishes a clear statistical correspondence between these turbine parameters and tip vortex behaviours under different turbine operation conditions, which is further substantiated by examining samples of time series of these turbine parameters and tip vortex patterns. This study not only offers benchmark datasets for comparison with the-state-of-the-art numerical simulation, laboratory and field measurements but also sheds light on understanding wake characteristics and its downstream development, turbine performance and regulation, as well as developing novel turbine or wind farm control strategies.
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University of Minnesota Ph.D. dissertation.December 2018. Major: Mechanical Engineering. Advisor: Jiarong Hong. 1 computer file (PDF); xiv, 91 pages + 2 supplementary mp4 files.
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Dasari, Teja. (2018). Near-wake studies of a utility-scale wind turbine using natural snowfall-based visualization and Particle Image Velocimetry. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/202208.
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