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261 results for “Turbine”
Wind turbine blade structural health monitoring dataset
<p>The dataset is related to a unique experiment conducted at ETH Zurich in collaboration with the Institute of Fluid Flow Machinery, Polish Academy of Sciences. The synchronisation between fatigue loading and guided wave excitation and sensing is unique. The dataset can be used to construct and test damage indexes for structural health monitoring.</p> <p>The tests were carried out on a Sonkyo Windspot 3.5 kW wind turbine blade equipped with strain gauges, a thermocouple, and five piezoelectric transducers. One piezoelectric transducer was used for Hann windowed sine excitation whereas the remaining piezoelectric transducers were used as sensors. The fatigue loading was induced by using a 1 kN capable Tira shaker. The fatigue program is explained in the readme.txt file and involves overloading the blade with a crane up to the blade's failure. The shaker was excited by a sine signal of frequency around the first resonant frequency of the wind turbine blade. The synchronisation with guided wave excitation was realised during three characteristic moments: (1) at maximum amplitude of sine, (2) at zero crossing, and (2) at the minimum amplitude of sine. This stage of the experiment is called 'dynamic' for short, and the data is stored in respective 'raw' folders. After each set of 1000 cycles, the shaker was stopped until the blade stopped vibrating. Then another set of guided wave measurements was taken at the blade's rest position. This stage of the experiment is called 'static' for short, and the data is stored in respective 'average' folders. It contains signals averaged over 10 measurements. During the whole process strain as well as temperature were measured.</p> <p>Three files are included for data visualization: (1) 'plot_strain_temperature.m', (2) 'read_plot_static.m', and (3) 'read_plot_dynamic.m'. These are MATLAB scripts showing how to load data and visualize the dependence of strains and temperatures on fatigue cycle number or time, plot exemplary signals of guided waves, and construct a damage index for structural health monitoring of the wind turbine blade.</p> <p>The details of experimental setup can be found in the paper.</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>
Site-specific results DeltaWind + Innwind 10MW Reference Wind Turbine
<p><strong>Digitalización Offshore EU Project - CENER- Digital Twin site specific results.</strong></p> <p>Related dataset:</p> <ul> <li><a href="https://zenodo.org/records/14070345">Virtual results DeltaWind + Innwind 10MW Reference Wind Turbine</a></li> </ul> <p>Related presentation:</p> <ul> <li><a href="https://zenodo.org/records/14067010">Digitalización de parques eólicos</a></li> </ul> <p>Simulation of floating offshore wind turbine</p> <ul> <li>DeltaWind platform + Innwind 10 MW Reference Wind Turbine)</li> <li>Meteocean conditions of Canary Islands</li> <li>Depth: 350 m</li> </ul> <p>Simulations specifications:</p> <ul> <li>Simulation carried out with OpenFAST v3.4.1 version <a href="https://github.com/OpenFAST/openfast/releases/tag/v3.4.1">Release v3.4.1 · OpenFAST/openfast</a></li> <li>CENER in-house controller</li> <li>400 s transient removed</li> </ul> <p> </p> <p><strong>Dataset: </strong></p> <p>zip that contains 243 csv files.</p> <p>Each file containing one-hour time series of load simulation (time step 0.5s). </p> <p><strong>Filenames</strong> specify details about the simulation:</p> <ul> <li>dlc - Design Load Case [12 : normal power production, 64 : idling]</li> <li>Vh [wind speed]</li> <li>Y [yaw angle]</li> <li>W [wave height _ period]</li> <li>D [wind direction]</li> <li>M [wave misalignment= wave direction with respect to wind direction]</li> </ul> <p>The <strong>columns </strong>of each csv file includes followind signals according to OpenFAST nomenclature and reference frames:</p> <ul> <li>Time (s)</li> <li>PtfmSurge (m)</li> <li>PtfmSway (m)</li> <li>PtfmHeave (m)</li> <li>PtfmRoll (deg)</li> <li>PtfmPitch (deg)</li> <li>PtfmYaw (deg)</li> <li>GenPwr (kW)</li> <li>RotThrust (kN)</li> <li>GenTq (kN-m)</li> <li>RotSpeed (rpm)</li> <li>BlPitch1 (deg)</li> <li>TipDxc1 (m)</li> <li>RootMxc1 (kN-m)</li> <li>RootMyc1 (kN-m)</li> <li>TwrBsMxt (kN-m)</li> <li>TwrBsMyt (kN-m)</li> <li>FAIRTEN1 (N)</li> <li>FAIRTEN2 (N)</li> <li>FAIRTEN3 (N)</li> </ul>
Virtual results DeltaWind + Innwind 10MW Reference Wind Turbine
<p><strong>Digitalización Offshore EU Project - CENER- Digital Twin site specific results.</strong></p> <p>Related dataset:</p> <ul> <li><a href="https://zenodo.org/records/14068807">Site-specific results DeltaWind + Innwind 10MW Reference Wind Turbine</a></li> </ul> <p>Related presentation:</p> <ul> <li><a href="https://zenodo.org/records/14067010">Digitalización de parques eólicos</a></li> </ul> <p> </p> <p>Simulation of floating offshore wind turbine</p> <ul> <li>DeltaWind platform + Innwind 10 MW Reference Wind Turbine)</li> <li>Virtual Meteocean conditions </li> <li>Depth: 350 m</li> </ul> <p>Simulations specifications:</p> <ul> <li>Simulation carried out with OpenFAST v3.4.1 version <a href="https://github.com/OpenFAST/openfast/releases/tag/v3.4.1">Release v3.4.1 · OpenFAST/openfast</a></li> <li>CENER in-house controller</li> <li>400 s transient removed</li> </ul> <p> </p> <p><strong>Dataset:</strong></p> <p>csv that contains statistics from 4320 simulations</p> <p>Statistics obtained from one-hour time series of load simulations</p> <p>- The <strong>definition</strong> of the simulations are included in the csv through the <strong>columns</strong>:</p> <ul> <li>DLC - Design Load Case [12 : production, 64 : idling]</li> <li>Wind Speed </li> <li>WaveHeight</li> <li>Wave Period</li> <li>Wind Direction</li> <li>Wave Direction</li> </ul> <p>- The <strong>statistics calculated</strong> are included in columns:</p> <ul> <li> <div>BlPitch1_avg (deg)</div> </li> <li> <div>FAIRTEN1_avg (N)</div> </li> <li> <div>FAIRTEN2_avg (N)</div> </li> <li> <div>FAIRTEN3_avg (N)</div> </li> <li> <div>GenPwr_avg (kW)</div> </li> <li> <div>GenTq_avg (kN-m)</div> </li> <li> <div>PtfmHeave_avg (m)</div> </li> <li> <div>PtfmPitch_avg (deg)</div> </li> <li> <div>PtfmRoll_avg (deg)</div> </li> <li> <div>PtfmSurge_avg (m)</div> </li> <li> <div>PtfmSway_avg (m)</div> </li> <li> <div>PtfmYaw_avg (deg)</div> </li> <li> <div>RootMxc1_avg (kN-m)</div> </li> <li> <div>RootMyc1_avg (kN-m)</div> </li> <li> <div>RotSpeed_avg (rpm)</div> </li> <li> <div>RotThrust_avg (kN)</div> </li> <li> <div>TipDxc1_avg (m)</div> </li> <li> <div>TwrBsMxt_avg (kN-m)</div> </li> <li> <div>TwrBsMyt_avg (kN-m)</div> </li> <li> <div>BlPitch1_std (deg)</div> <p> </p> </li> </ul>
zEPHYR - Large On Shore Wind Turbine Benchmark
<p>Large On Shore Wind Turbine Benchmark - This benchmark collects data for the validation of wind turbine noise prediction methods to be applied in realistic weather conditions. It includes metmast data for the weather prediction model validation, acoustic measurements and an approached model of the SWT2.3-93 wind turbine used during the test campaign, as well as the corresponding<br> CAD.</p>
Björkö Wind Turbine Version 1 (45kW) SCADA
<p>Chalmers wind turbine has variable speed operation with a direct driven generator and a frequency converter, it also has a digital control system developed by Chalmers. The wind turbine has a rated power of 45 kW and rated speed of 75 rpm. The wooden tower is 30 m high, the blades of carbon fibres are 7.5 m long, and the turbine diameter is 15.9 m. The individually blade pitch system is electrical. The turbine is situated on the island Björkö at Skarviksvägen, 20 km west of Göteborg city. The coordinates are: 57.71818820625921, 11.683382148764485.<br> 68 SCADA Channels contain timeseries observations (at 1Hz) of Rotor, Gearbox, Yaw Motor, Inverter, and other components. In addition, some structural monitoring data such as nacelle accelerations, tower and blades bending moments are included. The data set covers time period from 06 July 2022 to 15 July 2023.<br> Structured metadata about wind turbine characteristics and SCADA channels is included as JSON files and CSV. Additional information is available upon request.</p>
Björkö Wind Turbine Version 1 (45kW) high frequency Structural Health Monitoring (SHM) data
<p>The Chalmers wind turbine has variable speed operation with a direct driven generator and a frequency converter, it also has a digital control system developed by Chalmers. The wind turbine has a rated power of 45 kW and rated speed of 75 rpm. The wooden tower is 30 m high, the blades of carbon fibres are 7.5 m long, and the turbine diameter is 15.9 m. The individually blade pitch system is electrical. The turbine is situated on the island Björkö at Skarviksvägen, 20 km west of Göteborg city. The coordinates are: 57.71818820625921, 11.683382148764485.</p> <p><br> 69 SCADA and structural vibration and loads Channels timeseries (sampled at 20 and 100 Hz) such as nacelle accelerations, tower and blades bending moments are included.</p> <p><br> Structured metadata about wind turbine characteristics, SCADA, vibration and loads channels are included as JSON files and CSV.</p> <p>This particular dataset consisting of high frequency sampled data, is intended for condition and structural health analysis.</p> <p><strong>The data covers:</strong></p> <ul> <li>the measurements sampled at 100 Hz correspond to the period from 05 July 2022 to 9 June 2023</li> <li>the measurements sampled at 20 Hz correspond to the period from 05 July 2022 to 2 August 2023</li> </ul> <p><strong>This repository includes:</strong></p> <p><strong>Time-series data in csv format:</strong></p> <ul> <li>B1_CL4_20.csv (this is the data sampled at 20 Hz)</li> <li>B1_CL4_100.csv (this is the data sampled at 100 Hz)</li> </ul> <p><strong>Metadata:</strong></p> <ul> <li>Bjorko_Sensors_Specs_Metadata.csv (Sensors signals specification in csv format)</li> <li>Bjorko_modes_mapping.csv (numerical integer value representing the wind turbine controller system mode in csv format)</li> <li>Bjorko_modes_mapping.json (numerical integer value representing the wind turbine controller system mode in csv JSON format)</li> <li>Bjorko_digital_io_states_mappings.csv (Description of digital input and output states in the wind turbine controller system in csv format)</li> </ul> <p><strong>Media:</strong></p> <ul> <li>Chalmers-Wind turbine.pdf (description of the wind turbine including pictures)</li> <li>Chalmers wind turbine description 220121-short.pdf (description of the wind turbine including pictures)</li> </ul> <p><strong>Semantic artifacts:</strong></p> <ul> <li>N/A</li> </ul> <p><strong>Other:</strong></p> <ul> <li>N/A</li> </ul> <p>Additional information is available upon request.</p>
Aventa AV-7 ETH Zurich Research Wind Turbine SCADA and high frequency Structural Health Monitoring (SHM) data
<p><strong>General description of wind turbine: </strong>The ETH owned wind turbine is Aventa AV-7, manufactured by Aventa AG in Switzerland and was commissioned in December 2002. The turbine is operated via a belt-driven generator and a frequency converter with a variable speed drive. The rated power of the Aventa AV-7 is 7 kW, beginning production at a wind speed of 2 m/s and having a cut-off speed of 14 m/s. The rotor diameter is 12.8 m with 3 rotor blades, and a hub height is 18m. The maximum rotational speed of the turbine is 63 rpm. The tower is a tubular steel-reinforced concrete structure, supported on concrete foundation, while the blades are made of glassfiber with a tubular steel main-spar. The turbine is regulated via a variable-speed and variable pitch control system.</p> <p><strong>Location of site: </strong>The wind turbine is located in Taggenberg, about 5 km from the city centre of Winterthur, Switzerland. This site is easily accessible by public transport and on foot with direct road access right next to the turbine. This prime location reduces the cost of site visits and allows for frequent personal monitoring of the site when test equipment is installed. The coordinates of the site are: 47°31'12.2"N 8°40'55.7"E.</p> <p><strong>Control and measurement systems and signals: </strong>The turbine is regulated via a variable-speed and collective variable pitch control system.</p> <p><strong>SHM Motivation: </strong>Designed and commissioned in 2002, the Aventa wind turbine in Winterthur is soon reaching its end of design lifetime. In order to assess the various techniques of predicting the remaining useful lifetime, a Structural Health Monitoring (SHM) campaign was implemented by ETH Zurich. The monitoring campaign started in 2020, and is still ongoing. In addition, the setup is used as a research platform on topics such as system identification, operational modal analysis, faults/damage detection and classification. We analyze the influence of operational and environmental conditions on the modal parameters and to further infer Performance Indicators (PIs) for assessing structural behavior in terms of deterioration processes.</p> <p><strong>Data Description: </strong>The tower and nacelle have been instrumented with 11 accelerometers distributed along the length of the tower, nacelle main frame, main bearing and generator. Two full bridge strain gauges are installed on the concrete tower based measuring fore-aft and side-side strain (and can be converted to bending moments) – all acceleration and strain signals sampled at 200Hz. Temperature and humidity are measured at the tower base – 1Hz data. In additional we are collecting operational performance data (SCADA), namely: wind speed, nacelle yaw orientation, rotor RPM, power output and turbine status – SCADA signals are sampled at 10Hz. See appendix for further details of the sensors layout.</p> <p>The measurements/instrumentation setup, type and layout is provided in the pdf files.</p> <p><strong>The data:</strong> the data is provided in zip files corresponding to four use-cases as follows:</p> <ul> <li>Normal operation data for system identification</li> <li>Aerodynamic imbalance on one blade</li> <li>Rotor icing event</li> <li>Failure of the flexible coupling of the linear drive of the collective pitch system</li> </ul> <p>The data for each of the four uses-cases is organized in zip files. The content of each zip file is as follows:</p> <ul> <li>Time-series data in HDF5 format</li> <li>Metadata: <ul> <li>Turbine specification (Aventa-AV-7.json and Aventa-AV-7.yaml)</li> <li>Sensor specification (Aventa_sensors.json )</li> <li>Unstructured description of the Aventa Turbine and the installed sensors (Aventa_Sensors_Specs.xlsx)</li> </ul> </li> <li>Semantic artifacts: <ul> <li>WindIO Wind Turbine YAML schema describing turbine specifications (IEAontology_schema.yaml)</li> <li>Sensor specification JSON schema (sensors_schema.json)</li> </ul> </li> <li>Media: Pictures of leading edge roughness and a clip of wind turbine operation</li> <li>Code: Jupyter notebook containing example code to load metadata from JSON and data from HDF5 files (example.ipynb)</li> </ul> <p>Additional data is available upon request, please contact:</p> <ul> <li>Prof. Dr. Eleni Chatzi (chatzi@ibk.baug.ethz.ch)</li> <li>Dr. Imad Abdallah (ai@rtdt.ai , abdallah@ibk.baug.ethz.ch)</li> </ul> <p>For further details or questions, please contact:</p> <p>Prof. Dr. Eleni Chatzi<br> Chair of Structural Mechanics & Monitoring</p> <p>ETH Zürich<br> <a href="http://www.chatzi.ibk.ethz.ch/">http://www.chatzi.ibk.ethz.ch/</a></p>
Aerodynamic model comparison for an X-shaped vertical-axis wind turbine
<p>This repository can be used to reproduce the power, thrust, blade forces, and vertical induction from the journal paper 'Aerodynamic model comparison for an X-shaped vertical-axis wind turbine (https://doi.org/10.5194/wes-2023-115)'. The processing and plotting files are in MATLAB format (*.m). As an alternative to MATLAB, Octave can be used to run these files as well.</p>
Ice Throw from Wind Turbines: Experimental Data, 6DOF Model, CFD results, 3D Scans
<p>Compiled data and code from the Eisball Project (funded by the Austrian Research Promotion Agency FFG, project number 865060)</p> <p> </p> <p> </p> <p>6DOF_model_octave.zip - reference implementation of the six-degree-of-freedom model in MathML (Octave or MATLAB)</p> <p>experimental_data.csv - Experimental Data from dropping artificial ice fragments from wind turbines, recording drop distance and direction, details in experimental_data_column_description.txt</p> <p>???_forces_and_moments.csv - forces and moments tables for the use in the 6DOF model, specific per specimen type</p> <p> </p> <p>Data was first published in Nov 2021 at https://boku.ac.at/wau/risk/abgeschlossene-projekte/eisball-1 (may not persist)</p>
'Wind theft' from onshore wind turbine arrays: Sensitivity to wind farm parameterization and resolution
<p>Data and namelists from Pryor S.C., Shepherd T.J., Volker P., Hahmann A.N. and Barthelmie R.J.: ‘Wind theft’ from onshore wind turbine arrays: Sensitivity to wind farm parameterization and resolution. <em>Journal of Applied Meteorology and Climatology (doi:10.1175/JAMC-D-19-0235.1)</em></p>
LiDAR Cluster Statistic of Wind Turbine Wakes
<p>Mean and standard deviation of the wake velocity field generated by utility-scale wind turbines for different turbulence intensity of the incoming wind and rotor thrust coefficient. Statistics are retrieved from wind LiDAR measurements. More details in this paper https://onlinelibrary.wiley.com/doi/full/10.1002/we.2430 </p>
Capacity factors for wind turbines
<p>Simulated capacity factors in Finland for six wind turbine models, Vestas V90-3.0 MW, V90-2.0 MW, V112-3.3 MW, V126-3.3 MW, V117-3.45 MW and V136-3.45 MW at four turbine hub heights 75, 100, 125, 150 m. Wind speed data are from Finnish Wind Atlas [1, 2], from which the Weibull distribution shape and scale parameters (labelled ‘Weibull all data k’ and ‘Weibull all data A’, respectively) and the frequencies of the wind sectors (‘Frequency all data’) were used.</p> <p>File <em>FWA_coordinates_2500m.csv</em> holds the geographical coordinates (WGS 84) of the Wind Atlas in 2.5×2.5 km<sup>2</sup> resolution.</p> <p>To simulate a wind farm where each turbine experiences a slightly different wind speed, we used a normal distribution with variance <span class="math-tex">\(\sigma^2(v) = 0.2v + 0.6\,\mathrm{m/s}\)</span>, (where <em>v</em> is wind speed) to smooth (convolute) the original power curves [3, 4].</p> <p>The calculation of capacity factor cf at wind atlas grid point k is described by the formula<br> <span class="math-tex">\(\mathit{CF}_k = \mathop{\mathbb{E}}_{i, s} g(v_i) \approx \sum_{s=1}^{12} f_{k,s} \sum_{i=1}^N p_{k,s}(v_i) g(v_i) \Delta v\)</span>,<br> where g(v) is the power curve function for current wind turbine model, vi the mean wind speed of bin i, fk,s the frequency of occurrence of wind direction s at point k, N the number of wind speed bins, pk,s(v) the Weibull probability density function for sector s at point k at the hub height and Δv the width of the wind speed bin.</p> <p><strong>References</strong></p> <ol> <li>Finnish Meteorological Institute, “Finnish Wind Atlas,” 2008. [Online]. Available: <a href="http://www.windatlas.fi">http://www.windatlas.fi</a>. [Accessed: 28-Jun-2016]</li> <li>B. Tammelin, T. Vihma, E. Atlaskin, J. Badger, C. Fortelius, H. Gregow, M. Horttanainen, R. Hyvönen, J. Kilpinen, J. Latikka, K. Ljungberg, N. G. Mortensen, S. Niemelä, K. Ruosteenoja, K. Salonen, I. Suomi, and A. Venäläinen, “Production of the Finnish Wind Atlas,” Wind Energy, vol. 16, no. 1, pp. 19–35, Jan. 2013.</li> <li>Staffell, Iain, and Richard Green. 2014. “How Does Wind Farm Performance Decline with Age?” Renewable Energy 66. Elsevier Ltd: 775–86. doi:10.1016/j.renene.2013.10.041.</li> <li>Staffell, Iain, and Stefan Pfenninger. 2016. “Using Bias-Corrected Reanalysis to Simulate Current and Future Wind Power Output.” Energy 114 (November): 1224–39. doi:10.1016/j.energy.2016.08.068.</li> </ol> <p> </p>
Dataset for "Long-term extreme response of an offshore turbine: How accurate are contour-based estimates?"
<p>Datasets belonging to the paper "Long-term extreme response of an offshore turbine: How accurate are<br> contour-based estimates?" by Haselsteiner, Frieling, Mackay, Sander and Thoben.</p> <p>Available are:<br> * A 1000-year time series of hourly environmental conditions<br> * 516 1-hour time series of the mudline overturning moment, simulated using openFAST</p> <p> </p>
Supplementary Material: A Large-Eddy Simulation Study of Vertical Axis Wind Turbine Wakes in the Atmospheric Boundary Layer
<p>Supplementary material for <em>Energies</em> <strong>2016</strong>, <em>9</em>, 366; doi:10.3390/en9050366:</p> <p><strong>Video S1:</strong> Normalized instantaneous streamwise velocity field both on a vertical plane (<em>x</em>-<em>z</em>) going through the center of the turbine and on a horizontal plane at the equator height of the turbine (Note: the physical time corresponding to this video is 1 minute and 17 seconds, and the size of the blades is magnified for illustration purposes).</p> <p><strong>Video S2:</strong> Normalized instantaneous streamwise velocity field on a horizontal plane at the equator height of the turbine for two cases: when the turbine starts to operate (top) and when the flow has reached statistically steady condition (bottom) (Note: the physical time corresponding to both videos is 1 minute and 17 seconds, and the size of the blades is magnified for illustration purposes).</p>
X-ray CT data: fatigue damage in glass fibre/polyester composite used for wind turbine blades
<p>These data are obtained using a Zeiss Xradia Versa 520 scanner to scan a uni-directional glass fibre reinforced polyester composite made from a non-crimp fabric used for wind turbine blades. The scans were performed to study the fatigue damage progression in this material. The data is published together with the below journal paper, in which more information can be found. The present videos of the data relate directly to the figures in this paper.</p> <p>Jespersen, K. M., Zangenberg Hansen, J., Lowe, T., Withers, P. J., & Mikkelsen, L. P. (2016). <em>Fatigue damage assessment of uni-directional non-crimp fabric reinforced polyester composite using X-ray computed tomography</em>. <em>Composites Science and Technology</em>, <em>136</em>, 94–103. DOI:10.1016/j.compscitech.2016.10.006</p> <p>For use of these data, please remember to cite the above mentioned paper.</p> <p>Corresponding author, K. M. Jespersen, e-mail kmun@dtu.dk</p>
zEPHYR - Audio files of recorded and auralized wind turbine noise
<p>Audio files associated with the publication "Wind farm noise prediction and auralization", Andrea P. C. Bresciani, Julien Maillard, Arthur Finez, submitted to Acta Acustica in Dec. 2023.</p> <p>Audio 1: Auralized noise for OC1 and SB1<br>Audio 2: Auralized noise for OC1 and SB2<br>Audio 3: Auralized noise for OC1 and SB3<br>Audio 4: Auralized noise for OC2 and SB1<br>Audio 5: Auralized noise for OC2 and SB2<br>Audio 6: Auralized noise for OC2 and SB3<br>Audio 7: Recorded noise for OC1 and SB1<br>Audio 8: Recorded noise for OC1 and SB2<br>Audio 9: Recorded noise for OC1 and SB3<br>Audio 10: Recorded noise for OC2 and SB1<br>Audio 11: Recorded noise for OC2 and SB2<br>Audio 12: Recorded noise for OC2 and SB3<br>Audio 13: Auralized noise for OC2 and SB2 without amplitude fluctuations</p>
Infrared thermography of turbulence patterns of operational wind turbine rotor blades supported with high-resolution photography: KI-VISIR Dataset
<h2>Abstract</h2> <p><span>With increasing wind energy capacity and installation of wind turbines, new inspection techniques are being explored to examine wind turbine rotor blades, especially during operation. A common result of surface damage phenomena (such as leading-edge erosion) is the premature transition of laminar to turbulent flow on the surface of rotor blades. In the KI-VISIR (Künstliche Intelligenz Visuell und Infrarot Thermografie – Artificial Intelligence-Visual and Infrared Thermography) project, infrared thermography is used as an inspection tool to capture so-called thermal turbulence patterns (TTP) that result from such surface contamination or damage. To compliment the thermographic inspections, high-resolution photography is performed to visualise, in detail, the sites where these turbulence patterns initiate. A convolutional neural network (CNN) was developed and used to detect and localise the turbulence patterns. A unique dataset combining the thermograms and visual images of operational wind turbine rotor blades has been provided, along with the simplified annotations for the turbulence patterns. Additional tools are available to allow users to use the data requiring only basic Python programming skills.</span></p>
Aeroelastic simulations of wind turbines affected by leading edge erosion: datasets for multivariate time-series classification
<p>This repository contains data generated and used for classification in the publication:<br> Duthé, G.; Abdallah, I.; Barber, S.; Chatzi, E. Modeling and Monitoring Erosion of the Leading Edge of Wind Turbine Blades. <em>Energies</em> <strong>2021</strong>, <em>14</em>, 7262. https://doi.org/10.3390/en14217262</p> <p>The data is generated via OpenFAST aeroelastic simulations coupled with a Non-Homogeneous Compound Poisson Process for degradation modelling and was used to train a Transformer deep learning model.</p> <p>One degradation run generates 1200 samples (1 sample every 6 days corresponding to a 20 year degradation period). In total 20 degradation runs are made available (20x1200 = 24'000 multivariate time-series samples). This repo can serve to benchmark long multivariate time-series classification algorithms. There are 10 possible classes of erosion severity.</p> <p>Each sample is a multivariate time-series of length 60'000, with the following 4 channels extracted from the simulations for a section at the tip of the blade:</p> <ul> <li>Inflow velocity</li> <li>Angle of attack</li> <li>Lift coefficient</li> <li>Drag coefficient</li> </ul> <p>Please see the publication above for more information as well as the included readme for information about the data and an example of how to load it into to PyTorch.</p> <p> </p>
Wall Resolved Fluid-Structure Interaction Numerical Simulation of a Modern Wind Turbine Blade
<p>Wall-resolved fluid-structure interaction (FSI) numerical simulations of the NREL 5 MW wind turbine blade<br> are compared using two FSI approaches. The first method is based on high-fidelity Nektar++/SHARPy FSI framework,<br> where the fluid governing equations are solved using high-order spectral/hp element method and the turbulent flow is<br> resolved using Large Eddy Simulation (LES) on thick strips, while large-deformation dynamics of the structure are mod-<br> elled using a geometrically exact nonlinear composite beam finite-element model. Thick strip method for the fluid reduces<br> the computational cost by considering a series of smaller domains, each of which has a finite thickness in the spanwise<br> direction. Hence, the overall flow over the blade is treated with a sectional approach, where in each of these sections,<br> strips, the 3D flow is reconstructed locally. Tip-loss correction is used to compensate for the sectional approach over the<br> blade. The second FSI approach is based on OpenFoam/Calculix coupling, where the second-order unstructured finite<br> volume method approach is used for solving the three-dimensional flow equations and the flow turbulence is captured us-<br> ing the k-ω SST model. The structural dynamics are modeled via second-order finite element method using standard solid<br> elements. Effects of the solution fidelity on the prediction of aerodynamic forces as well as on the full three-dimensional<br> flow modelling over the blade versus sectional representation of flow over the blade while incorporating the local three-<br> dimensionality in each section and tip-correction are discussed. Further, significance of two approaches on modelling<br> the slender blade, one using the beam mode and the other utilizing the full 3D solution of structure is addressed. Finally,<br> assessment of computational cost and scalability of the two approaches are presented and discussed.</p>
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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.