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Browsing by Author "Hong, Jiarong"

Now showing 1 - 5 of 5
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    Database of snow holograms collected from 2019 to 2022 for machine learning training or other purposes
    (2022-10-06) Li, Jiaqi; Guala, Michele; Hong, Jiarong; li001334@umn.edu; Li, Jiaqi; University of Minnesota Flow Field Imaging Lab
    This dataset includes the original combined snow holograms and holograms with image augmentation (rotation, exposure, blur, noise) for YOLOv5 model training to detect and classify snow particles. The individual snow particles are cropped and combined to enrich the particle numbers in each image for the ease of manual labeling. The snow particles are classified into six categories, including aggregate/irregular (I), dendrite (P2), graupel/rime (R), plate (P1), needle/column (N/C), and small particles/germ (G).
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    Snow properties, trajectories, and turbulence data collected during field deployments from 2022 to 2023 at Eolos research station
    (2024-07-29) Li, Jiaqi; Guala, Michele; Hong, Jiarong; li001334@umn.edu; Li, Jiaqi
    The data contains the three-dimensional trajectories, snow particle properties, and raw meteorological data of four datasets (with dominant snow types, including aggregates, dendrites, graupels, and needles). The datasets are collected during three deployments, on April 7th, 2022, December 19th, 2022, and January 2nd, 2023, at the Eolos Wind Research Field Station in Rosemount, MN.
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    The spatial structure of the logarithmic region in very-high-Reynolds-number rough wall turbulent boundary layers
    (Cambridge University Press, 2018-10-26) Heisel, Michael; Dasari, Teja; Liu, Yun; Hong, Jiarong; Coletti, Filippo; Guala, Michele
    Using super-large-scale particle image velocimetry (SLPIV), we investigate the spatial structure of the near-wall region in the fully rough atmospheric surface layer with Reynolds number Reτ∼O(10^6). The field site consists of relatively flat, snow-covered farmland, allowing for the development of a fully rough turbulent boundary layer under near-neutral thermal stability conditions. The imaging field of view extends from 3 m to 19 m above the ground and captures the top of the roughness sublayer and the bottom of an extensive logarithmic region. The SLPIV technique uses natural snowfall as seeding particles for the flow imaging. We demonstrate that SLPIV provides reliable measurements of first- and second-order velocity statistics in the streamwise and wall-normal directions. Our results in the logarithmic region show that the structural features identified in laboratory studies are similarly present in the atmosphere. Using instantaneous vector fields and two-point correlation analysis, we identify vortex structures sharing the signature of hairpin vortex packets. We also evaluate the zonal structure of the boundary layer by tracking uniform momentum zones (UMZs) and the shear interfaces between UMZs in space and time. Statistics of the UMZs and shear interfaces reveal the role of the zonal structure in determining the mean and variance profiles. The velocity difference across the shear interfaces scales with the friction velocity, in agreement with previous studies, and the size of the UMZs scales with wall-normal distance, in agreement with the attached eddy framework.
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    The spectral signature of wind turbine wake meandering: a wind tunnel and field-scale study
    (Wiley, 2018-04-06) Heisel, Michael; Hong, Jiarong; Guala, Michele
    Field-scale and wind tunnel experiments were conducted in the 2D to 6D turbine wake region to investigate the effect of geometric and Reynolds number scaling on wake meandering. Five field deployments took place: 4 in the wake of a single 2.5-MW wind turbine and 1 at a wind farm with numerous 2-MW turbines. The experiments occurred under near-neutral thermal conditions. Ground-based lidar was used to measure wake velocities, and a vertical array of met-mounted sonic anemometers were used to characterize inflow conditions. Laboratory tests were conducted in an atmospheric boundary layer wind tunnel for comparison with the field results. Treatment of the low-resolution lidar measurements is discussed, including an empirical correction to velocity spectra using colocated lidar and sonic anemometer. Spectral analysis on the laboratory- and utility-scale measurements confirms a meandering frequency that scales with the Strouhal number St = fD/U based on the turbine rotor diameter D. The scaling indicates the importance of the rotor-scaled annular shear layer to the dynamics of meandering at the field scale, which is consistent with findings of previous wind tunnel and computational studies. The field and tunnel spectra also reveal a deficit in large-scale turbulent energy, signaling a sheltering effect of the turbine, which blocks or deflects the largest flow scales of the incoming flow. Two different mechanisms for wake meandering—large scales of the incoming flow and shear instabilities at relatively smaller scales—are discussed and inferred to be related to the turbulent kinetic energy excess and deficit observed in the wake velocity spectra.
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    Wind turbine wake flow visualization data from experiments conducted at UMore park on March 5-6, 2018 and April 8-9, 2018
    (2020-05-20) Abraham, Aliza; Hong, Jiarong; jhong@umn.edu; Hong, Jiarong; University of Minnesota Flow Field Imaging Lab
    This data includes flow visualization videos using natural snowfall in the wake of a utility-scale (2.5 MW) wind turbine. In addition, the meteorological and SCADA data from the same time periods are included. These datasets are used to analyze the interaction between the wind turbine wake and the ground surface, which has important implications for understanding the impact of wind farms on their surroundings, particularly in the area of agriculture.