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42 results for “wind tunnel”
Wind tunnel experiments on wind turbine wakes in yaw
<p>This data set contains Laser Doppler Anemometer measurements in the wake behind two different model wind turbines, recorded in a wind tunnel campain at the NTNU in Trondheim. Full plane wake data were recorded, with a focus on the effect of yaw misalignment and inflow turbulence. Please refer to the documentation document for more information.</p>
Supplemental Material to Journal Article "Tunneling Crack Initiation in Trailing-Edge Bond Lines of Wind-Turbine Blades"
<p>This set supplements the figure data to the article "Tunneling Crack Initiation in Trailing-Edge Bond Lines of Wind-Turbine Blades", DOI: <a href="http://doi.org/10.2514/1.J058179">10.2514/1.J058179</a>.</p>
Setup of a 3D printed wind tunnel: application for calibrating bi-directional velocity probes used in Fire Engineering Applications
<p>The research presented here focuses on the development of a 3D printed wind tunnel and the relevant equipment to be used for calibrating bi-directional velocity probes (BDVP). BDVP are equipment to be used for measuring velocity flow by determining the pressure difference of hot gases generated during fires. The manufactured probes require calibration to determine the calibration factor to achieve precise measurement. The calibration is usually performed in wind tunnels which can be difficult to access due to costs, complexity and the various pieces of equipment required. The aim of the current study is to develop and assemble an inexpensive and easy-to-build bench-scale wind tunnel, with a data-logging system and fan control functionalities for fast and effective calibration of BDVP. A 3D printer with a PET-G filament is used, able to produce parts for the wind tunnel system which are durable and easy to handle and assemble. The system additionally includes an Arduino-based measuring unit with a hot-wire anemometer and temperature correction: Rev. P. This takes precise measurements; continuously logging data on a computer through a USB interface and capable of saving data on an SD card. This design provides users with parameters of velocity flow up to 4 m/s with standard deviation of 1.2 % and turbulence intensity of 1 %. The main advantages of this wind tunnel are its simplicity to build and portability.</p> <p>The dataset contains design files for 3D printing of the wind tunnel, BOM and wiring of electronic components.</p> <p>The project is prototype under development and authors are not responsible for any damages or injuries caused by inappropriate construction or operation. This source is distributed WITHOUT ANY EXPRESS OR IMPLIED WARRANTY, INCLUDING OF MERCHANTABILITY, SATISFACTORY QUALITY AND FITNESS FOR A PARTICULAR PURPOSE.</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 Measurements of Aerodynamic Entrainment Rate of Particles
<p>This dataset is related to a wind tunnel experiment of aerodynamic entrainment rate of particles. Wind profiles are measured by pitot tube, for calibrating surface shear stress measured by Irwin sensors. Aerodynamic entrainment rate is measured through the mass difference weighting before and after the erosion event. Each case is repeated three times to find the average value and the error. The experimental setup is showed in figure 1.</p>
Synethic dataset for differential OGI images and wind tunnel dataset
<h2>Fluid Flow Dataset</h2> <p> </p> <h2>1. Introduction</h2> <p>A total of three datasets are included here, namely, (1) the synthetic dataset used for model training as mentioned in the paper, (2) the wind tunnel dataset used to validate the segmentation performance of the model, including the corresponding manually labeled labels, and (3) the original wind tunnel experiment dataset.</p> <h2>2. Synthetic differential image dataset for segmentation</h2> <p>To address the lack of semantic segmentation datasets for infrared fluid flow imagery, we created a synthetic dataset from the ScalarFlow dataset, focusing on pixel-level labels suitable for neural network training. Our process, illustrated as shown in the paper, includes generating realistic noise by capturing images under controlled conditions with an Optical Gas Imaging (OGI) camera, followed by image subtraction, normalization, and merging with ScalarFlow data. This method ensures the inclusion of real-world disturbances such as camera jitter effects, enhancing the dataset's robustness and applicability. The dataset, enriched with various data augmentation techniques, comprises over 30,000 images split into training, validation, and testing sets, catering to the rigorous demands of practical applications in fluid dynamics analysis.</p> <h2>3. Wind tunnel dataset</h2> <p>To enable the determination of velocities of fluid flow by using optical flow algorithms, a wind tunnel data set that includes fluid images captured by different cameras was recorded. </p> <p>The fluid flow is created in the wind tunnel and generated by different substances, i.e., dry ice, smoke matches, or paraffin oil. In addition, velocity data collected from the 3D ultrasonic anemometer were used as a reference to evaluate the performance and accuracy of the optical flow algorithms. The fluid flow rate was set at three different velocities in Euclidean space, i.e., 0.7 m/s, 1.4 m/s, and 2.0 m/s.</p> <div> <div>This dataset is captured by using a wind tunnel, the OGI camera FLIR GF320 and a 3D anemometer for obtaining reference flow velocities. Below is the information of the used camera.</div> </div> <h3>FLIR GF320 Camera Info</h3> <table> <tbody> <tr> <td> <div> <div><strong>Parameter</strong></div> </div> </td> <td> <div> <div><strong>Value</strong></div> </div> </td> </tr> <tr> <td> <div> <div>Spectral Range</div> </div> </td> <td> <div> <div>3.2 – 3.4 μm</div> </div> </td> </tr> <tr> <td> <div> <div>Standard Temperature Range</div> </div> </td> <td> <div> <div>–20°C to +350°C</div> </div> </td> </tr> <tr> <td> <div> <div>Accuracy</div> </div> </td> <td> <div> <div> ±1 °C for 0 °C to 100 °C; ±2% > 100 °C</div> </div> </td> </tr> <tr> <td> <div> <div>Lenses</div> </div> </td> <td> <div> <div>24° × 18°</div> </div> </td> </tr> <tr> <td> <div> <div>Resolution</div> </div> </td> <td> <div> <div>320 × 240 Pixel</div> </div> </td> </tr> </tbody> </table> <p> </p> <p>The dataset consists of three parts, i.e., OGI images of fluids/smoke generated from three different substances.</p> <ul> <li>1. Smoke matches dataset<br> <br> <table> <tbody> <tr> <td> <div> <div><strong>Id</strong></div> </div> </td> <td><strong>Substances</strong></td> <td> <div> <div><strong>Velocity in m/s</strong></div> </div> </td> <td> <div> <div><strong>Group</strong></div> </div> </td> <td> <div> <div><strong>Number of frames</strong></div> </div> </td> </tr> <tr> <td>1</td> <td>Smoke matches</td> <td>0.7</td> <td>1</td> <td>143</td> </tr> <tr> <td>2</td> <td>Smoke matches</td> <td>0.7</td> <td>2</td> <td>597</td> </tr> <tr> <td>3</td> <td>Smoke matches</td> <td>1.4</td> <td>1</td> <td>145</td> </tr> <tr> <td>4</td> <td>Smoke matches</td> <td>1.4</td> <td>2</td> <td>598</td> </tr> <tr> <td>5</td> <td>Smoke matches</td> <td>2.0</td> <td>1</td> <td>145</td> </tr> <tr> <td>6</td> <td>Smoke matches</td> <td>2.0</td> <td>2</td> <td>596</td> </tr> </tbody> </table> </li> <li>2. Paraffin oil dataset<br><br> <table> <tbody> <tr> <td> <div> <div><strong>Id</strong></div> </div> </td> <td><strong>Substances</strong></td> <td> <div> <div><strong>Velocity in m/s</strong></div> </div> </td> <td> <div> <div><strong>Group</strong></div> </div> </td> <td> <div> <div><strong>Number of frames</strong></div> </div> </td> </tr> <tr> <td>7</td> <td> <div> <div>Paraffin oil</div> </div> </td> <td>0.7</td> <td>1</td> <td>597</td> </tr> <tr> <td>8</td> <td>Paraffin oil</td> <td>0.7</td> <td>2</td> <td>597</td> </tr> <tr> <td>9</td> <td>Paraffin oil</td> <td>1.4</td> <td>1</td> <td>597</td> </tr> <tr> <td>10</td> <td>Paraffin oil</td> <td>1.4</td> <td>2</td> <td>597</td> </tr> <tr> <td>11</td> <td>Paraffin oil</td> <td>2.0</td> <td>1</td> <td>597</td> </tr> <tr> <td>12</td> <td>Paraffin oil</td> <td>2.0</td> <td>2</td> <td>597</td> </tr> </tbody> </table> </li> <li>3. Dry ice dataset<br><br> <table> <tbody> <tr> <td> <div> <div><strong>Id</strong></div> </div> </td> <td><strong>Substances</strong></td> <td> <div> <div><strong>Velocity in m/s</strong></div> </div> </td> <td> <div> <div><strong>Group</strong></div> </div> </td> <td> <div> <div><strong>Number of frames</strong></div> </div> </td> </tr> <tr> <td>13</td> <td>Dry ice</td> <td>0.7</td> <td>1</td> <td>598</td> </tr> <tr> <td>14</td> <td>Dry ice</td> <td>0.7</td> <td>2</td> <td>597</td> </tr> <tr> <td>15</td> <td>Dry ice</td> <td>1.4</td> <td>1</td> <td>595</td> </tr> <tr> <td>16</td> <td>Dry ice</td> <td>1.4</td> <td>2</td> <td>596</td> </tr> <tr> <td>17</td> <td>Dry ice</td> <td>2.0</td> <td>1</td> <td>185</td> </tr> <tr> <td>18</td> <td>Dry ice</td> <td>2.0</td> <td>2</td> <td>628</td> </tr> </tbody> </table> </li> </ul> <p> </p> <p>We also provide the corresponding 3D anemometer data, which allows the user to convert the pixel displacement from the image to the actual flow rate, as shown in below.</p> <div> <h3>Velocities in m/s and pixel</h3> <table> <tbody> <tr> <td> <div> <div><strong>Velocity in m/s</strong></div> </div> </td> <td> <div> <div><strong>Settings of WindChannel</strong></div> </div> </td> <td> <div> <div><strong> Velocity in pixel</strong></div> </div> </td> </tr> <tr> <td> <div> <div>0.7</div> </div> </td> <td>1.88</td> <td>4.57</td> </tr> <tr> <td>1.4</td> <td>2.50</td> <td>9.14</td> </tr> <tr> <td>2.0</td> <td>3.06</td> <td>13.06</td> </tr> </tbody> </table> <p> </p> <h2>4. Wind tunnel segmentation dataset</h2> <p>This part of the dataset is from the dry ice dataset portion of the wind tunnel test dataset described above. And labels are generated by manual labeling for evaluating the performance of the image segmentation model in real-world scenarios, a total of 100 differential images and 100 labels.</p> <p> </p> </div>
Dataset - Radially resolved dynamic inflow pitch step experiment in wind tunnel
<p>The relevant turbine data of MoWiTO 1.8 and the preprocessed data presented in the following accepted paper are uploaded:</p> <p><br> Berger, F., Onnen, D., Schepers, J. G., and Kühn, M.: Experimental analysis of radially resolved dynamic inflow effects due to pitch steps, Wind Energ. Sci. Discuss. [preprint], https://doi.org/10.5194/wes-2021-70, accepted, 2021.</p> <p> </p> <p>The dataset describes a pitch step experiment with a model wind turbine in a wind tunnel between a high rotor load and a low rotor load. Measurements of turbine loads, near wake flow and radius resolved induction measurements are available.</p>
First wind tunnel campaign - PERSEUS airfoil control
<p>Loads experienced by the airfoil designed in the european project PERSEUS</p>
Efficacy of Dry Needling With the Fascial Winding Technique in the Carpal Tunnel Syndrome
ClinicalTrials.gov study NCT03907956. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Wind Tunnel Measurements of Aerodynamic Entrainment Rate of Particles
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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>
Evaluation of a wind tunnel designed to investigate the response of evaporation to changes in the incoming longwave radiation at a water surface. Part I and II
<p>Data in spreadsheet format for wind tunnel evaporation experiments 2019-2022.</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>
The Effect of Slope Gradient and Shape on the Formation Process of Climbing Dunes in a Wind Tunnel Experiment
<p>the data from the wind-tunnel experiments </p>
Wind Tunnel Threshold Speed Document Bundle
Collection of scanned figures relating to the wind tunnel threshold speed data from boundary layer wind tunnels
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>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.