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14 results for “wind tunnel data”
Wind tunnel test data for the evaluation of the aerodynamic coefficients of an antenna mast with ancillaries.
<p>This dataset comprises measured data and results from static wind tunnel tests conducted in April 2024 at the Giovanni Solari Wind Tunnel Facility (GS-WinDyn). The tests aim to assess the drag, lift, and moment coefficients <span>of an antenna mast designed as a triangular lattice tower, equipped with both linear and discrete ancillary components.</span> The wind tunnel experiments are carried out under both smooth and turbulent flow conditions using a scaled 3D model of the antenna mast. Five ancillary configurations, based on predominant patterns observed, are tested. Drag forces, lift forces and moments are measured using two six-component force balances attached to the ends of the model, while downstream three-component velocity data is captured by a Cobra probe. For each configuration, aerodynamic coefficients are determined for angles of attack ranging from 0° to 360°, with increments of up to 10°. The dataset provides the measured data and the obtained aerodynamic coefficients and it has significant reuse potential in several applications: comparison with experimental wind tunnel data, validation of analytical and numerical CFD models with similar configurations, estimation of wind loads due to ancillary structures, and characterization of wake effects.</p>
Icing Wind Tunnel Measurements of Supercooled Large Droplets Using the 12 mm Total Water Content Cone of the Nevzorov Probe: Measurement Data
<p>This repository contains the measurement data that was used for the publication "Icing Wind Tunnel Measurements of Supercooled Large Droplets Using the 12 mm Total Water Content Cone of the Nevzorov Probe".</p>
Twin Test 2: Wake interactions of a cluster of turbines and wake steering techniques. Wind tunnel data.
<p>The aerodynamic performance of two identical wind turbine models was characterized under various static and dynamic conditions in a synchronous configuration within the wind tunnel test section. Two experimental campaigns were performed at Technische Universität München (TUM) and at the National Technical University of Athens (NTUA) to investigate wake flow control techniques. This document contains the necessary information to understand the performed experiments and to access and use the available data. While both experimental set ups are detailed, only data from the TUM campaign are available at the time of writing, as the NTUA campaign results will form Phase II of an ongoing blind test campaign and cannot be published.</p>
Data example and code used in the publication "Is transport of microplastics different from that of mineral dust? Results from idealized wind tunnel studies"
<p>Background</p> <p>The code labels microspheres and counts them. Further, the code determines which microspheres are independent of microsphere-microsphere collisions by their relative position to the other microspheres in an image. Images were taken with a full-frame visual camera (Sony Alpha 7RII) with a long-distance-microscopy lens (K2 DistaMax).</p> <p>Description of the dataset</p> <ul> <li>image_data_all.zip contains 228 tif-format images taken in a single experiment <ul> <li>the images show borosilicate microspheres with diameters from 63 to 75 µm</li> <li>during the experiment, the microspheres are detached from the substrate and are transported out of the image</li> </ul> </li> <li>functions_particle_labeling.jl contains all necessary functions for particle labeling</li> <li>analysis_protocol.jl is an example, that first determines a color threshold, and then labels all microspheres in all images stored in "image_data_all/substrate_a/image_data_single_experiment"</li> <li>post_processing_visualisation.R is an r-script, that reads the output of analysis_protocol.jl and demonstrates how logistic functions were fitted to the data</li> </ul> <p> </p> <p>We used julia 1.8.5 and R 4.3.0.</p> <p> </p>
FarmConners Wind Farm Flow Control Benchmark: Blind Test with CL-WINDCON Wind Tunnel Data
<p>This is the dataset used for running the fourth Blind Test of the FarmConners Wind Farm Flow Control Benchmark. The Blind Test was performed with an extensive dataset gathered while testing a cluster of three scaled wind turbines within a large boundary layer wind tunnel. The experimental dataset has been compared against the predictions provided by 5 different control-oriented wind farm flow models. The resulting comparison is described in the paper "FarmConners Wind Farm Flow Control Benchmark: Blind Test Results, Part 2", by Campagnolo et al, (2023). The dataset consists of:</p> <ol> <li>measurements of the flow within the wake shed by one or two machines, as well as measurements of the power, loads (on the rotating shaft and at tower base), and pitch/yaw/torque actuators states of the three scaled machines. The measurements have been performed under a wide range of inflow and machines operating conditions. The time series of the measured data are provided in the format of Matlab structures saved in .mat files.</li> <li>Predictions provided by the models used by the Blind Test participants</li> <li>Matlab scripts used for comparing the experimental dataset and the numerical predictions provided by the Blind Test participants</li> <li>Additional data provided to the Blind Test participants. This includes a FAST model of the scaled wind turbine and the mapping of the inflow of the empty wind tunnel. </li> </ol>
Periodic dynamic induction control of wind farms: proving the potential in simulations and wind tunnel experiments - data sets
<p>Data sets of the wind tunnel experiments described in "Periodic dynamic induction control of wind farms: proving the potential in simulations and wind tunnel experiments". DOI: https://doi.org/10.5194/wes-2019-50</p>
Politecnico di Milano - Wind tunnel test data on high-rise building
<p>High-resolution pressure data recorded in wind tunnel tests performed at the Politecnico di Milano wind tunnel on a generic prismatic high-rise building.<br>If you use these data, please cite:<br>Lamberti, G., Amerio, L., Pomaranzi, G., Zasso, A., & Gorlé, C. (2020). Comparison of high resolution pressure measurements on a high-rise building in a closed and open-section wind tunnel. Journal of wind engineering and industrial aerodynamics, 204, 104247. DOI: 10.1016/j.jweia.2020.104247</p>
Wind Tunnel Mosquito Trajectory Data
<p>Mosquitoes track odors, locate hosts, and find mates visually. The color of a food resource, such as a flower or warm-blooded host, can be dominated by long wavelengths of the visible light spectrum (green to red for humans) and is likely important for object recognition and localization. However, little is known about the hues that attract mosquitoes or how odor affects mosquito visual search behaviors. We use a real-time 3D tracking system and wind tunnel that allows careful control of the olfactory and visual environment to quantify the behavior of more than 1.3 million mosquito trajectories. We find that CO<sub>2</sub> induces a strong attraction to specific spectral bands, including those that humans perceive as cyan, orange, and red. Sensitivity to orange and red correlates with mosquitoes' strong attraction to the color spectrum of human skin, which is dominated by these wavelengths. The attraction is eliminated by filtering the orange and red bands from the skin color spectrum and by introducing mutations targeting specific long-wavelength opsins or CO<sub>2 </sub>detection. Collectively, our results show that odor is critical for mosquitoes' wavelength preferences and that the mosquito visual system is a promising target for inhibiting their attraction to human hosts.</p>
Data from: A new low-turbulence wind tunnel for animal and small vehicle flight experiments
Our understanding of animal flight benefits greatly from specialized wind tunnels designed for flying animals. Existing facilities can simulate laminar flow during straight, ascending and descending flight, as well as at different altitudes. However, the atmosphere in which animals fly is even more complex. Flow can be laminar and quiet at high altitudes but highly turbulent near the ground, and gusts can rapidly change wind speed. To study flight in both laminar and turbulent environments, a multi-purpose wind tunnel for studying animal and small vehicle flight was built at Stanford University. The tunnel is closed-circuit and can produce airspeeds up to 50 m s−1 in a rectangular test section that is 1.0 m wide, 0.82 m tall and 1.73 m long. Seamless honeycomb and screens in the airline together with a carefully designed contraction reduce centreline turbulence intensities to less than or equal to 0.030% at all operating speeds. A large diameter fan and specialized acoustic treatment allow the tunnel to operate at low noise levels of 76.4 dB at 20 m s−1. To simulate high turbulence, an active turbulence grid can increase turbulence intensities up to 45%. Finally, an open jet configuration enables stereo high-speed fluoroscopy for studying musculoskeletal control in turbulent flow.
Data from: A new low-turbulence wind tunnel for animal and small vehicle flight experiments
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Wind Tunnel Mosquito Trajectory Data
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The flow past a flatback airfoil with flow control devices: Benchmarking numerical simulations against wind tunnel data - Animations
<p>As wind turbines grow larger, the use of flatback airfoils has become standard practice for the root region of the blades. Flatback profiles provide higher lift and reduced sensitivity to soiling at significantly higher drag values. A number of flow control devices has been proposed to improve the performance of flatback profiles. In the present study, the flow past a flatback airfoil at a chord Reynolds number of 1.5×10<sup>6 </sup>with and without trailing edge flow control devices is considered. Two different numerical approaches are applied, Unsteady Reynolds Averaged Navier Stokes (RANS) simulations and Detached Eddy Simulations (DES). The computational predictions are compared to wind tunnel measurements to assess the suitability of each method. The effect of each flow control device on the flow is examined based on the DES results on the finer mesh. Results agree well with the experimental findings and show that a newly proposed flap device outperforms traditional solutions for flatback airfoils. In terms of numerical modelling, the more expensive DES approach is more suitable if the wake frequencies are of interest, but the simplest 2D RANS simulations can provide acceptable load predictions.</p> <p>These animations are the DES results on a Fine (25M cells, AR = 1) mesh. Animations include a 3D view of Q = 1.5 isosurfaces and a side view of Q = 100 isosurfaces for each case.</p> <p> </p>
The flow past a flatback airfoil with flow control devices: Benchmarking numerical simulations against wind tunnel data - Animations II
<p>Animations from accepted (06/2020) publication:</p> <p>The flow past a flatback airfoil with flow control devices: Benchmarking numerical simulations against wind tunnel data, Wind Energ. Sci., https://doi.org/10.5194/wes-2020-36</p> <p>These animations are the DES results on a Fine (25M cells, AR = 1) mesh. Animations include a 3D view of Delta = 10^5 isosurfaces coloured by X vorticity and Z vorticity. Two animations are available for each case (Plain airfoil, Flap, Flap + Cavity, Cavity, Splitter). Contour levels are included in separate files.</p> <p> </p>
Data in "From dome dune to barchan dune: airflow structure changes measured with particle image velocimetry in a wind tunnel"
<p>The dataset of side view and top view were stored as Tecplot file format. These data were used in Figures 5, 10, 12, 13 and 14. </p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.