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WILLOW - Norther: data set for the full-scale validation of model-based virtual sensing methods for an operational offshore wind turbine
<h1><em><strong>1. General description </strong></em></h1> <p>This data set contains as-build design information, as well as full-scale vibration response measurements from an operational offshore wind-turbine. The turbine is part of the Norther wind farm which is located in the Belgian North Sea<em> </em>and includes a total of 44 Vestas V164 (8.4MW) wind turbines on monopile foundations, see <a href="../api/records/11093262/draft/files/Fig1_Norther_locaction.png/content" target="_blank" rel="noopener noreferrer">Fig1_Norther_locaction.png</a>. This data set is intended to verify and validate model-based virtual sensing algorithms, using data as well as modeling information from a real turbine. </p> <h2><em><strong>1.1 Summary of the shared structural information</strong></em></h2> <p>The included information entails a detailed description of the geometric properties of the monopile and transition piece, distributed and lumped structural masses . All information shared in this record is conform the as-designed documentation. An example of the lumped masses considered in the model input files is presented in "<a href="../api/records/11093262/draft/files/Fig2_Sensor_Network.png/content" target="_blank" rel="noopener">Fig2_Sensor_Network.png"</a></p> <h2><em><strong>1.2 Summary of the shared geotechnical information</strong></em></h2> <p>Monopiles are distinguished by the significant role of soil-structure interaction. Ground reaction is most typically included in the structural model as non-linear p-y curves. Different p-y curves are available for a certain number of soils in the standards applicable to offshore structures (API RP 2GEO, 2011, and ISO 19901-4:2016(E), 2016).</p> <p>The required soil properties to define p-y curves according to the API framework are given in the soil profile provided in a separate Excel. Rather than symbols, the name of the soil properties is generally used as column header (e.g., <em>Undrained shear strength</em>). Therefore, it is straightforward to identify each soil parameter. The only soil parameter that might lead to confusion is:</p> <ul> <li><em>"epsilon50 [-]" </em>represents the vertical strain at half the maximum principal stress difference in a static undrained triaxial compression test on an undisturbed soil sample.</li> </ul> <p>It's worthy to note that estimates for the small shear strain stiffness, referred to as Gmax, are also included. Despite not being required as an input to define the API p-y curves, this parameter remains a key input for other soil reaction frameworks than the API (e.g., PISA). </p> <h2><em><strong>1.3 Summary of the shared measurement data</strong></em></h2> <p>Two sets of measurement data have been curated for validation purposes; the first interval has been collected during parked conditions, whereas the second interval has been collected during rated operational conditions. Both records have a length of 2 hours, and are subdivided into 10-minute data sets. Furthermore 1Hz SCADA data has been made available for the selected intervals. All different data sources are time synchronized and have been subjected to several internal quality checks. </p> <p>The sensor network on NRT-WTG is illustrated in in <strong>Fig. 2, </strong>whereas a description of the sensor types is presented in <strong>Tab.1.</strong> The acceleration sensors are installed in the horizontal plane, and measure tangential (Y) and orthogonal (X) to the wall, where the positive Y direction is pointing clockwise and the positive X direction is pointing inwards. All strain sensors are installed vertically and are located on the inside of the wall.</p> <table> <tbody> <tr> <td><strong>Data type </strong></td> <td><strong>Sensor type</strong></td> <td><strong>Fs (Hz)</strong></td> <td> <p><strong>Level mLAT (m)</strong></p> </td> <td><strong>Description </strong></td> </tr> <tr> <td>Acceleration (g) </td> <td>Piezo-electric acc. sensor (<strong>ACC</strong>)</td> <td>30</td> <td>15, 69, 97 </td> <td>3 Bi-directional accelerometers at different levels. LAT 15 installed at 240 degree heading; LAT 69 and 97 at 60 degree.</td> </tr> <tr> <td>Strain (micro strain)</td> <td>Resistive strain gauge (<strong>SG</strong>)</td> <td>30</td> <td>14</td> <td>6 SGs: equally spaced around the inner circumference of the can. Headings: 50, 110, 170, 230, 290, 350 degree.</td> </tr> <tr> <td>Strain (micro strain)</td> <td>Fiber-Bragg Grating strain gauge (<strong>FBG</strong>)</td> <td>100</td> <td>-17, -19</td> <td>2 FBGs per level at 165 and 255 degree respectively.</td> </tr> </tbody> </table> <p><strong>Table 1. Description of sensor types.</strong></p> <p>The FBG strain time series have been synchronized with the SG time series using using a cross-correlation based approach. Therefore the SG data has been used to genereate refrence strain time series at the headings of the FBG sensors; the FBG data is subsequently synchronized with regard to this reference time series. No synchronization of the acceleration data was needed, since these are collected using the same data aquisition system as the SG data. </p> <p>The SG strain time series have been calibrated and temperature compensated, whereas this is not the case for the FBG strain time series. The latter have a yet to be determined calibration offset. </p> <p>In conjunction to the sensor channels presented in <strong>Tab. 1</strong>, 1 Hz SCADA data is provided. A summary of the provided SCADA parameters, all sampled at 1Hz, is presented in <strong>Tab 2.</strong></p> <table> <tbody> <tr> <td><strong>Parameter</strong></td> <td><strong>Unit</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>Wind speed</td> <td>m/s</td> <td>Wind speed as recorded in the turbine SCADA</td> </tr> <tr> <td>Wind direction</td> <td>°</td> <td>Wind direction relative to North (0°) as recorded in the turbine SCADA</td> </tr> <tr> <td>Yaw angle</td> <td>°</td> <td>Yaw orientation of the nacelle relative to North (0°) as recorded in the turbine SCADA</td> </tr> <tr> <td>Pitch angle</td> <td>°</td> <td>Rotor blade pitch as recorded in the turbine SCADA</td> </tr> <tr> <td>Rotor speed</td> <td>rpm</td> <td>Rotor speed in rotations per minute as recorded in the turbine SCADA</td> </tr> <tr> <td>Power</td> <td>kW</td> <td>Active power of the turbine as recorded in the turbine SCADA</td> </tr> </tbody> </table> <p><strong>Table 2. </strong>List of provided SCADA parameters</p> <p> </p> <p>A summary of the selected intervals and relevant corresponding scada parameters is given in <strong>Tab 3</strong>.</p> <table> <tbody> <tr> <td><strong>Scenario </strong></td> <td><strong>T1 (UTC)</strong></td> <td><strong>T2 (UTC) </strong></td> <td><strong>Windspeed</strong></td> <td><strong>RPM </strong></td> <td><strong>Pitch </strong></td> </tr> <tr> <td>Parked</td> <td> <p>03/07 01:30</p> </td> <td> <p>03/07 03:30</p> </td> <td>< 4.5 m/s</td> <td>~1</td> <td>~18 °</td> </tr> <tr> <td>Rated</td> <td> <p>05/07 22:30</p> </td> <td> <p>06/07 00:30 </p> </td> <td>~15 m/s</td> <td>10.5</td> <td>8.1°</td> </tr> </tbody> </table> <p><strong>Table 3. </strong>Selected data intervals and relevant scada parameters</p> <p> </p> <h1><em><strong>2. Included in this version </strong></em></h1> <h2><em><strong>2.1 Version - 0.1.0</strong></em></h2> <ul> <li>Relevant Design information can be found in: <ul> <li>Geometry data for NRT-WTG: "WILLOW-Geometry_v4.xlsx"</li> <li>Best estimate soil profile: "WILLOW-BE_soil_profile.xlsx"</li> </ul> </li> <li>Acceleration, strain and scada data can be found in the following parquet files: <ul> <li>Measurement data for the parked case: "NRT-WTG_Parked.parquet.gz"</li> <li>Measurement data for the rated case: "NRT-WTG_Rated.parquet.gz"</li> </ul> </li> </ul> <p> </p> <h1><em><strong>3. Importing parquet files </strong></em></h1> <p>To import the measurement data into Python it is recommended to use pandas:</p> <pre>import pandas as pd<br># Read Parquet file with Pandas: relative_file_path = '<a href="../api/records/11093262/draft/files/NRT-WTG_Parked.parquet.gz/content" target="_blank" rel="noopener noreferrer">NRT-WTG_Parked.parquet.gz</a>' data = pd.read_parquet(relative_file_path ) <br><br>Once the dataframe has been imported, the users can process/re-arrange the raw data according the their needs; it should be noted that the imported dataframe contains NAN values - these are caused by the different sampling rates of the provided signals. </pre>
Vibration-based Monitoring of a Small-scale Wind Turbine Blade Under Varying Climate Conditions. Part I: An Experimental Benchmark
<p>This repository contains all publicly available data related to the experimental part of <a href="https://onlinelibrary.wiley.com/doi/epdf/10.1002/stc.2660">Sonkyo-Benchmark</a>. The data of each experimental case (R, A, B, C, D, E, F, G, H, I, J, K, L) and temperature point (-15, -10, -5, 0, 5, 10, 15, 20, 25, 30, 35, 40) are stored in a zip file named "Case_<em>X</em>_(<em>T</em>)", where <em>X</em> denotes the case label and <em>T</em> refers to the temperature value. Each file "Case_<em>X</em>_(<em>T</em>).zip" contains two folders "Case_<em>X</em>_(<em>T</em>)_1" and "Case_<em>X</em>_(<em>T</em>)_2", wherein the test results from the two sensor layouts are stored. </p>
A blind test on wind turbine wake modelling based on wind tunnel experiments: Phase I – The benchmark case
<p>This data set ("Data files.zip") contains the wind tunnel measurement data from Phase I of the Blind test on wind turbine wake modelling based on wind tunnel experiments organised during the TWEET-IE project (www.tweet-ie.eu).</p> <p>This updated version <strong>replaces</strong> the older versions 1.0.0 (https://doi.org/10.5281/zenodo.10566401), 1.1.0 (https://doi.org/10.5281/zenodo.11370112), 2.0 (https://doi.org/10.5281/zenodo.12188194) and 2.1 (https://doi.org/ 10.5281/zenodo.13918935). In comparison to the previous version 2.1 the data documentation has been updated to follow the template of the TWEET-IE project documents, indicating the Grant Agreement Number with the European Union and the Call Topic of the project.</p> <p>All tests were conducted in the closed-loop, low-speed boundary layer wind tunnel of the Chair of Aerodynamics and Fluid Mechanics at Technische Universität München (TUM). The experiments concerned two wind turbines, aligned with the flow, one downstream of the other, at a distance of 5 diameters. For Phase I, no control was applied to the wind turbine models, which were operating at constant RPM. The turbine models, designed and manufactured by TUM, were instrumented with multiple sensors and actuators and had a diameter of 1.1M. Measurements include velocity, power and loads on the turbines. A detailed description of the experimental set up can be found in the accompanying document ("Data documentation.pdf"). </p> <p>File "Submission procedure.zip" includes the format description and the templates of the output data that should be submitted by the participants in the blind test comparison.</p>
DeepOWT v2.25.1: An updated and improved global offshore wind turbine dataset until 2025Q1
<p>DeepOWT (deep learning derived global offshore wind turbines) is an independent and openly accessible data set of offshore wind energy infrastructure locations and their temporal deployment dynamics on a global scale. It is derived by applying deep learning based object detection on ESA's spaceborne Sentinel-1 synthetic aperture radar (SAR) archive. DeepOWT provides OWT locations along with their quarterly deployment stages from 2016Q1 until 2025Q1. It differentiates between platforms under construction, OWTs which are readily deployed and offshore wind farm substations, such as transformer stations.<br><br>The dataset continues the work of <a href="https://essd.copernicus.org/articles/14/4251/2022/">10.5194/essd-14-4251-2022</a>.</p> <p>File metadata</p> <table> <tbody> <tr> <th>File</th> <th>Time</th> <th>Geometry</th> <th>Spatial extent</th> </tr> <tr> <td>DeepOWT.geojson (Dataset)</td> <td>2016Q1-2025Q1</td> <td>points</td> <td>Global</td> </tr> <tr> <td>gt_2021Q2_nsb.geojson (Ground Truth Location)</td> <td>2021Q2</td> <td>polygons</td> <td>North Sea Basin</td> </tr> <tr> <td>gt_2021Q2_ecs.geojson (Ground Truth Location)</td> <td>2021Q2</td> <td>polygons</td> <td>East China Sea</td> </tr> <tr> <td>gt_2021Q2_vtn.geojson (Ground Truth Location)</td> <td>2021Q2</td> <td>polygons</td> <td>Southeast Vietnamese Coast</td> </tr> <tr> <td>gt_nsb_gridded.geojson (Ground Truth Region)</td> <td>-</td> <td>polygon</td> <td>North Sea Basin</td> </tr> <tr> <td>gt_ecs_gridded.geojson (Ground Truth Region)</td> <td>-</td> <td>polygon</td> <td>East China Sea</td> </tr> <tr> <td>gt_ecs_gridded.geojson (Ground Truth Region)</td> <td>-</td> <td>polygon</td> <td>Southeast Vietnamese Coast</td> </tr> </tbody> </table> <p> </p> <table> <thead> <tr> <th>Used semantic label</th> </tr> </thead> <tbody> <tr> <td>open sea</td> </tr> <tr> <td>under construction</td> </tr> <tr> <td>offshore wind turbine</td> </tr> <tr> <td>offshore wind farm substation</td> </tr> </tbody> </table> <p> </p>
Hydroelastic response of the scaled model of a floating offshore wind turbine platform in waves: HELOFOW Project Database
<p>This dataset contains the data measured during the <strong>HELOFOW </strong>model test campaign, performed at the Ocean and Hydrodynamic Engineering wave tank of Ecole Centrale Nantes (ECN): decay tests, regular wave tests and irregular waves tests. The preprocessed measured data is contained in MAT files.</p> <p>The model, the measurements and the tests are described in the appended Excel files. A Matlab(R) function is given as a short example to show how the MAT files are structured and how data may be handled for a plot. </p> <p>As stated in the reference paper (Leroy et al., <em>Ocean Engineering</em>, 2022):</p> <p>"As the size of floating wind turbines continues to increase, floating platforms reach dimensions that make their elastic and hydro-elastic behaviour significant. Several works in connection with the numerical modelling of the elastic behaviour of these wind turbines have been carried out but few validation data are available. This study focuses on the hydro-elastic response of a large floating wind turbine, in regular waves and severe sea-states. A new experimental wind turbine model has been designed to represent a 1:40 Froude-scaled spar platform carrying the DTU 10 MW turbine. The main challenge is here to reproduce a 1st bending mode frequency and hydrodynamic loads representative of a realistic large floating wind turbine. The platform model is made of a flexible backbone, reproducing the correct flexibility, and light floaters fixed on it provide the correctly scaled geometry. This experimental model is tested in various conditions including regular waves of several periods and steepness, and irregular waves of various intensity, including extreme 50-year return period conditions."</p> <p> </p> <p>This work was carried out within the framework of the WEAMEC, West Atlantic Marine Energy Community, and with funding from the Pays de la Loire Region and Europe (European Regional Development Fund). <br><br>HELOFOW project on <a href="https://www.weamec.fr/en/projects/helofow/">the WEAMEC website</a>. </p>
Characterization of Wind Turbine Wakes with Nacelle-Mounted Doppler LiDARs and Model Validation in the Presence of Wind Veer
<p>Dataset of the paper "Characterization of Wind Turbine Wakes with Nacelle-Mounted Doppler LiDARs and Model Validation in the Presence of Wind Veer" published in Remote Sensing [1].</p> <p>[1] Brugger P, Fuertes FC, Vahidzadeh M, Markfort CD, Porté-Agel F. Characterization of Wind Turbine Wakes with Nacelle-Mounted Doppler LiDARs and Model Validation in the Presence of Wind Veer. <em>Remote Sensing</em>. 2019; 11(19):2247. https://doi.org/10.3390/rs11192247.</p>
Field measurements of wake meandering at a utility-scale wind turbine with nacelle-mounted Doppler lidars
<p>Dataset of the paper " Dataset of the paper "Characterization of Wind Turbine Wakes with Nacelle-Mounted Doppler LiDARs and Model Validation in the Presence of Wind Veer" published in Remote Sensing [1]. " published in Wind Energy Science [1].</p> <p>[1] Brugger, P., Markfort, C., and Porté-Agel, F.: Field measurements of wake meandering at a utility-scale wind turbine with nacelle-mounted Doppler lidars, Wind Energ. Sci., 7, 185–199, https://doi.org/10.5194/wes-7-185-2022, 2022.</p>
FLOATECH WP3 experimental dataset : wave-tank hybrid testing of a 10 MW turbine based on a spar platform (ECN)
<p>This dataset presents the experimental measurements made in the Hydrodynamic and Ocean Engineering wave tank of Ecole Centrale de Nantes, in France, with the model of a 10 MW turbine supported by a spar platform at a scale 1:40. </p> <p>The tests were performed using a real-time hybrid testing method (or software-in-the-loop) called SoftWind presented and published in Ocean Engineering (the paper is available at this <a title="Paper SoftWind" href="https://doi.org/10.1016/j.oceaneng.2024.118390">link</a>). </p> <p> </p> <p><strong>Presentation of the experimental model:</strong></p> <p>The model is presented in details in the provided Excel file "FLOATECH_C3_Project data and model description.xlsx". </p> <p> </p> <p><strong>In the dataset:</strong></p> <p>The measurement files of the tests are gathered in folders by "series", and each test file has a test number. The series and the test conditions of each run are detailed in the provided Excel file "FLOATECH_C3_Database_Matrix.xlsx". </p> <p>Decay tests, pull-out tests and hammer tests were performed and are given in the dataset. </p> <p> </p> <p><strong>Real-time simulation models</strong></p> <p>The numerical models used in the real-time OpenFAST simulations are also provided in the compressed file "RT Simulations files.zip". </p> <p> </p> <p><strong>Data used in the published paper:</strong></p> <p>Some of the tests were used in the paper (see <a title="Paper SoftWind" href="https://doi.org/10.1016/j.oceaneng.2024.118390">link</a>). The corresponding test numbers are given in the table below. </p> <table> <tbody> <tr> <td><strong>Load cases</strong></td> <td><strong>Hs (m)</strong></td> <td><strong>Tp (s)</strong></td> <td><strong>Uhub (m/s)</strong></td> <td><strong>TI (%)</strong></td> <td><strong>Wave dir. (°)</strong></td> <td><strong>Wind dir(°)</strong></td> <td><strong>TestNum 1C</strong></td> <td><strong>TestNum 3C</strong></td> <td><strong>TestNum 5C</strong></td> </tr> <tr> <td>1.2</td> <td>7</td> <td>12</td> <td>14</td> <td>13.8</td> <td>0</td> <td>0</td> <td>269</td> <td>268</td> <td>270</td> </tr> <tr> <td>2.1</td> <td>7</td> <td>12</td> <td>14</td> <td>13.8</td> <td>0</td> <td>25</td> <td>275</td> <td>307</td> <td>281</td> </tr> </tbody> </table> <p> </p> <p> </p>
Water quality data from Turbinator
<p>Sensors to monitor water quality are operating in harsh environments since they are in frequent contact with water. The Turbinator turbidity and water level sensor developed by IVL works around this issue by using a laser beam and a camera to measure turbidity and water level without being in contact with water. It can be used for early warning of pollutants or for predictive maintenance of a city's pipeline network for waste- and stormwater. The Turbinator is easy to install or de-install for example when the battery needs to be changed or the sensor is moved to a new location. No drilling is required. The installation and un-installation can be done without entering the drain, thus avoiding otherwise necessary safety measures. The sensors measure distance from the sensor to the water and turbidity. The dataset contains the following columns:</p> <ul> <li>id: identifier of the Turbinator;</li> <li>dateobserved: timestamp of the measurement in UTC;</li> <li>location: Well Known Text representation of the GPS location of the Turbinator;</li> <li>turbidity: turbidity in NTU</li> <li>distance: distance between the sensor (top of the well) and the water in meters.</li> </ul>
Fatigue properties of wind turbine rotor blade hybrid epoxy adhesives
<p>This dataset includes the tensile data at two different strain rates and tensile-tensile fatigue data of epoxy adhesives used in wind turbine rotor blades. SPABOND™ 820HTA (non-toughened) and SPABOND™ 840HTA (toughened) epoxy adhesives are combined at different weight proportions to develop the hybrid adhesives. The hybrid and ASTM D638-22 tensile specimen geometry (Type I and Type II) effects on fatigue performance are determined through instrumented experiments. </p>
Wind turbine blade simulations under changing environment for benchmarking SHM algorithms
<p>This data set contains the flapwise vibration response simulation of a wind turbine blade under <em>Environmental and Operational Variability</em> (EOV) as well as increasing damage. The blade’s dynamics are represented by means of a 4 element FEM of a cantilever beam, while dynamic loading corresponds to a discretized turbulent wind field calculated with the help of the software <em>TurbSim</em> for prescribed 10-minute average wind speed and turbulence. Rotation effects are ignored. The wind loading is coupled with the structural dynamics considering aeroelastic interactions, based on lift and drag forces calculated from a NACA 64-618 airfoil. Ambient temperature (10-minute average) is used to set the elasticity (Young’s) modulus of the blade material.</p> <p>While on the healthy state, the vibration response of the blade is simulated over a year of temperature and wind speed variations according to the average values measured in an area of north-central Switzerland. In addition, a week of extreme weather (abnormally high temperature in summer) and a month where the blade is subject to increasing damage are also simulated. Damage is represented as a decrement of the stiffness on a single FEM element located on the blade’s root. Damage increments linearly from 0 to 25% decrease of the total stiffness during a period of two weeks, while on the remaining two weeks a 25% stiffness decrement is sustained.</p> <p>The main aim of this data set is to be used as a benchmark of vibration based SHM methods, particularly on damage detection and localization under EOV. To this end, both the blade’s vibration response and the environmental and operational parameters (temperature and wind) used to simulate each response are provided. Further details can be found in the publication attached.</p>
Oscillations of Offshore Wind Turbines undergoing Installation I: Raw Measurements
<p><strong>Overview</strong></p> <p>This repository contains data from an offshore measurement campaign conducted during the installation of the offshore wind farm trianel wind farm borkum II (https://www.trianel-borkumzwei.de/). The wind farm consists of 32 Senvion 6XM152 turbines. The installation took place between August 2019 and May 2020.</p> <p>An offshore wind turbine undergoing installation is interesting from a research point of view for several reasons:</p> <ul> <li>Simple geometry: turbine foundation and tower are both rotationally symmetric steel tubes. Rotational symmetry also leads to (approximate) isotropical structural characteristics in the plane normal to tower and foundation.</li> <li>High Reynolds number flow: Assuming a tower diameter of 6 m, and average wind speeds ranging from 5 m/s to 12 m/s under installation conditions, Reynolds numbers range from 4.5 million to 10.5 million.</li> <li>Wave loading under full-scale conditions.</li> <li>Practical relevance to improving the competetivity of offshore wind.</li> </ul> <p>For fluid mechanics, closely monitoring offshore wind turbines under wind and wave loading thus compares to a full-scale experiment. Monitoring 32 turbines undergoing installation thus enables the measurement a broad spectrum of different states.</p> <p>The investigation into the data is ongoing, questions and contributions are welcome. The current data release still does not include all data. The dataset will thus be updated again in the future with more data to come. First analytic results can be found here:</p> <p>Sander, A, Haselsteiner, AF, Barat, K, Janssen, M, Oelker, S, Ohlendorf, J, & Thoben, K. "Relative Motion During Single Blade Installation: Measurements From the North Sea." Proceedings of the ASME 2020 39th International Conference on Ocean, Offshore and Arctic Engineering. Volume 9: Ocean Renewable Energy. Virtual, Online. August 3–7, 2020. V009T09A069. ASME. <https://doi.org/10.1115/OMAE2020-18935></p> <p>Sander, A, Meinhardt, C & Thoben, KD. "Monitoring of Offshore Wind Turbines under Wind and Wave Loading during Installation" Proceedings of the EuroDyn 2020 XI International Conference on Structural Dynamics. Volume 1. Virtual, Online. November 23-26, 2020. <https://generalconferencefiles.s3-eu-west-1.amazonaws.com/eurodyn_2020_ebook_procedings_vol1.pdf></p> <p>Recordings of the conference presentations are available on youtube:</p> <ul> <li>OMAE20: https://www.youtube.com/watch?v=QcAwdv6Z4e4</li> <li>EURODYN20: https://www.youtube.com/watch?v=iL-jAe0luTw</li> </ul> <p><strong>Physical Background</strong></p> <ol> <li>An offshore wind turbine under installation conditions can be simplified as a cantilevered beam (circular cross-section, rotationally symmetric wall thickness) with an eccentric mass (nacelle with generator) vibrating transversally (fore-aft and side-side in the reference system of the nacelle) under wind and wave loads.</li> <li>Both wind and wave loads are stochastic and are described using statistical models.</li> <li>Wave loads are a function of the sea state. For a sea state, the most important parameters are significant wave heigh H_m0 and Wave peak period T_P. To a lesser extend, wave direction, zero upcrossing period and maximum wave height are also important. Different statistical models can be used to describe the sea state and the relationship between significant wave heigh H_m0 and wave peak period. Most prominent in the North Sea is the JONSWAP spectrum.</li> <li>Wind loads are depending on wind speed, wind direction, shear factor and turbulence intensity. Different statistical models are available to describe the wind spectrum.</li> <li>Wind and wave loads trigger a structural response of the turbine. The structural response depends on the loading spectrum as well as the transfer function. Furthermore, the structural response is strongly depending on the damping and elasticity of the structure. In turbines, damping is typically very low (~ 0.5 - 1.5 %).</li> <li>The response is dominated by the first Eigenfrequency of the turbine. As the turbine is assumed to be rotationally symmetric, the fore-aft and side-side mode are extremely close together if not indistinguishable [1].</li> <li>The response has the characteristics of a narrow-band random vibration. A narrow-band random vibration is characterized by being dominated by a single, narrow frequency peak (here: first Eigenfrequency). The amplitude envelope follows a Rayleigh distribution and the phase angle is equally distributed between 0 and 2 pi.</li> <li>If viewed from above, the structural response describes a closed curve (orbit) which can be characterized by it shape (eccentricity), mean amplitude and direction. Mathematically speaking, this is a lissajous-figure, where the time series from one response direction is plotted as a function of the time series of the second response direction.</li> </ol> <p>[1]: under installation condition</p> <p><strong>Experimental Setup</strong></p> <p>Several locations were used to record data during the installation of the wind farm. They are listed in the following table:</p> <ul> <li>helihoist-{1,2}: data recorded from the helicopter hoisting platform atop the turbine nacelle. For most installations, two sensor boxes were deployed to ensure data availability.</li> <li>tp: Measurements from the transition piece</li> <li>sbitroot: Measurements from the blade lifting yoke's blade root side. The Z-axis is aligned to the blade main axis, X-Axis is perpendicular.</li> <li>sbittip: Measurements from the tip side of the blade lifting yoke. Z-axis aligned with the blade main axis, X-axis perpendicular</li> <li>damper: Measurements from the tuned mass damper used during single blade installation</li> <li>towertop: measurements from inside the turbine tower at the upper lift plattform</li> <li>towertransfer: measurements from atop the towers during sail out from the base harbour to the installation site </li> </ul> <p><strong>Organization of data</strong></p> <p>For each turbine installation, a separate folder can be found, e.g. turbine-01 for the first and turbine-16 for the 16th turbine. Turbine numbering follows the order of installation.</p> <p>Different data sources are organized in subfolders for each turbine dataset. Unfortunately, not every data source is available for each turbine. Data sources are roughly sorted into categories. The following table lists these categories:</p> <ul> <li>location / tom : data from custom build sensor boxes. Data includes acceleration, angular acceleration, magnetic field, gnss recording and rough estimates of the eulerian angles.</li> <li>waves / wmb-sued : Sea state statistics for the installatin period of the turbine.</li> <li>waves / fino : Sea state statistics from the german research platform FINO1 located approx. 6 km from the installation site.</li> <li>waves / waveradar : Sea state statstics, recorded by a wave rider wave laser. </li> <li>wind / lidar : high fidelity wind data recorded on the installation vessel during the installation of the wind farm.</li> <li>wind / scada : 10 min. mean wind statistisc recorded on wind turbines in the vicinity of the installation site. This data is used in case no LIDAR data is available.</li> <li>wind / anemometer : During some of the installations, anemometers were present on the installation vessel. These recordings are sorted into this sub-subfolder.</li> <li>wind / fino : Additonal wind statistics recorded by the FINO research station. Least recommended for investigations, as these recordings were taken approx. 6 km from the installation site.</li> </ul> <p>The zenodo data set includes 16 zip archives (for 16 turbines) as well as one zip archive including environmental data. The following lists the folder structure of the turbine-04.zip archive (with most of the data files removed for clarity).</p> <pre><code class="language-bash">└── turbines ├── turbine-04 │ ├── helihoist-1 │ │ └── tom │ │ └── clean │ │ ├── turbine-04_helihoist-1_tom_clean_2019-09-01-11-27-17_2019-09-01-11-54-00.csv │ │ ├── turbine-04_helihoist-1_tom_clean_2019-09-01-11-54-00_2019-09-01-12-20-44.csv │ ├── sbitroot │ │ └── tom │ │ └── clean │ │ ├── turbine-04_sbitroot_tom_clean_2019-09-07-06-48-53_2019-09-07-07-17-16.csv │ │ ├── turbine-04_sbitroot_tom_clean_2019-09-07-07-17-16_2019-09-07-07-45-59.csv │ ├── towertop │ │ └── tom │ │ └── clean │ │ ├── turbine-04_towertop_tom_clean_2000-01-06-18-55-52_2000-01-06-20-30-10.csv │ │ ├── turbine-04_towertop_tom_clean_2000-01-06-20-30-11_2000-01-06-22-04-31.csv │ ├── towertransfer │ │ └── tom │ │ └── clean │ │ ├── turbine-04_towertransfer_tom_clean_2019-08-31-03-11-53_2019-08-31-04-00-09.csv │ │ ├── turbine-04_towertransfer_tom_clean_2019-08-31-04-00-10_2019-08-31-04-48-19.csv │ └── tp │ └── tom │ └── clean │ ├── turbine-04_tp_tom_clean_2019-08-31-18-34-45_2019-08-31-19-07-52.csv │ ├── turbine-04_tp_tom_clean_2019-08-31-19-08-00_2019-08-31-19-40-43.csv </code></pre> <p>The following list the contents of the environment.zip archive. Note that again most of the data files have been removed for clarity. </p> <pre><code class="language-bash">└── environment ├── waves │ └── wmb-sued │ ├── wmb-sued_2019-08-15.csv │ ├── wmb-sued_2019-08-16.csv │ ├── wmb-sued_2019-08-17.csv └── wind └── lidar ├── lidar_2019-08-03.csv ├── lidar_2019-08-04.csv ├── lidar_2019-08-05.csv </code></pre> <p><strong>TOM data description</strong></p> <p>The abbreviation <strong>TOM</strong> referes to <em>Tower Oscillation Measurement</em> and the data that was acquired using a specific set of sensor boxes built by university of Bremen for this specific purpose. These Sensor Boxes were initially designed to measure accelerations and GPS tracks of offshore wind turbine towers undergoing installation. During the installation of the wind farm Trianel Windpark Borkum II they were subsequentially used to track the complete installation with a focus on single blade installation</p> <p>Data from the TOM devices comes as CSV files. The firmware of the boxes was designed, such that data was written into 10 MB sized txt files instead of one large txt file in order to circumvent data corruption due to power loss. However, this leads to a few milliseconds of missing data between log files. Additionally, jitter due to IO-Operations is present in the data as well and data should under all circumstance be resampled befor further analysis.</p> <p>Parameters provided by the TOM devices are listed in the following:</p> <ul> <li>epoch : machine readable time stamp based on the unix epoch in UTC [s] </li> <li>runtime : time since last boot of the tom device [ms] </li> <li>latitude : Degrees latitude in decimal writing. For Trianel: [degree due North] </li> <li>longitude : Degrees longitude in decimal writing. For Trianel: [degree due East] </li> <li>elevation : elevation above mean sea level [m] </li> <li>rot_{x,y,z} : Rotational acceleration around the three cartesian axis {x,y,z} of the TOM box. [degree / s^2] </li> <li>acc_{x,y,z} : linear acceleration in each of the three cartesian axis {x,y,z} in the reference system of the TOM box [m/s^2] </li> <li>mag_{x,y,z} : magnetic field strength in each of the local cartesian TOM box axis {x,y,z} [micro-Tesla] </li> <li>roll : eulerian roll angle (angle around the x Axis of the tom box) [degree] </li> <li>pitch : eulerian pitch angle (angle around the y Axis of the tom box) [degree] </li> <li>yaw : eulerian yaw angle (angle around the z Axis of the tom box) [degree] </li> </ul> <p><strong>LIDAR wind data description</strong></p> <p>A Leosphere WindCube LIDAR was mounted on the installation vessel <em>Taillevent</em> to record the atmospheric boundary layer during installation. The data recorded by the lidar was exported as csvs and provided by the vessel operator. The raw data includes the following parameters</p> <ul> <li>epoch : time stamp as a unix epoch in UTC [s]</li> <li>wind_speed_N : The wind speed at the N'th return level [m/s]</li> <li>wind_dir_N : Wind direction at the N'th return level in the vessels reference frame [degree]</li> <li>wind_dir_N_corr : Wind direction at the N'th return level due North [degree due North]</li> <li>heigh_N : The height of the lidar return level [m] </li> </ul> <p>Data is resampled to a 1 s return interval. </p> <p><strong>wave data description</strong></p> <p>Wave data was recorded using a waverider wave buoy (DWR-G). The wave rider buoy was located at 54 00' 238'' degree North and 6 26' 553'' degree East. In decimals: 54.0031096 North, 6.4425532 East. Based on the raw data, sea state statistics were derived with a return period of 30 minutes.</p> <p>The data comes as csv, column oriented text files. The first line entails parameter names and units and is commented out by a hashbang (unix commentary). The data is a combination of two different data files as provided by the buoy: \*.HIS Data Text File (History of Spectrum parameters) and \*.HIW Data Text File (History of Wave Statistics). This results in the two timestamps in the data file because history of wave statistics data is available immediately after each measurement time period, whereas history of spectrum parameters take approx. 5 minutes to calculate by the buoy and thus have a slightly later time stamp. For actual postprocessing purposes, a third timestamp rounded to full half hour is used. Parameters included in the data files are:</p> <ul> <li>epoch: number of seconds since 1st of January 1970 in UTC. Common time stamp format in computing. [s]</li> <li>Tp Tp := 1 / fp, peak period, the frequency at which S(f) is maximal [s]</li> <li> Dirp peak direction, the direction at f = fp [deg due North]</li> <li> Sprp peak spread, the directional spread at f = fp [s]</li> <li> Tz Tz := sqrt(m0 / m2), zero-upcross period [s]</li> <li> Hm0 Hm0 := 4*sqrt(m0), the significant waveheight [m]</li> <li> TI TI := sqrt(m[-2] / m0), integral period [s]</li> <li> T1 T1 := m0 / m1, mean period [s]</li> <li> Tc Tc := sqrt(m2 / m4), crest period [s]</li> <li> Tdw2 Tdw2 := sqrt(m[-1] / m1) [s]</li> <li> Tdw1 Tdw1 := sqrt(m[-1,2] / m0) [s]</li> <li> Tpc Tpc := m[-2] * m1 / m0 ^ 2, calculated peak period [s]</li> <li> nu nu := sqrt((T1 / Tz) ^ 2 - 1), band width parameter [-]</li> <li> eps eps := sqrt(1 - (Tc / Tz) ^ 2), bandwidth parameter [-]</li> <li> QP QP := 2 * m[1,2] / m0 ^ 2, Goda's peakedness parameter [-]</li> <li> Ss Ss :=2 * pi / g * Hs / Tz ^ 2, significant steepness [-]</li> <li> TRef TRef, reference temperature (25deg) [C]</li> <li> TSea TSea, sea surface temperature [C]</li> <li> Bat Bat, battery status (0..7) </li> <li> m[n] m[n] := Integral from f=0 to f=Inf over S(f) * f ^ n </li> <li> m[n,2] m[n,2] := Integral from f=0 to f=Inf over S(f) ^ 2 * f ^ n </li> <li> Percentage Percentage of data with no reception errors [%] </li> <li> Hmax Height of the highest wave [cm] </li> <li> Tmax Period of the highest wave) [s] </li> <li> H(1/10) Average height of 10% highest waves [cm] </li> <li> T(1/10) Average period of 10% highest waves [s] </li> <li> H(1/3) Average height of 33% highest waves [cm] </li> <li> T(1/3) Average period of 33% highest waves [s] </li> <li> Hav Average height of all waves [cm] </li> <li> Tav Average period of all waves) [s] </li> <li> Eps bandwidth parameter </li> <li> #Waves Number of waves</li> </ul> <p>Note: the m's are moments of the power spectral density S(f).</p> <p> </p> <p> </p>
SPLEEN - High Speed Turbine Cascade – Test Case Database - PIV Measurements
<p>This is an open-access database of Particle Image Velocimetry (PIV) measurements of the flow in the <strong>high-speed low-pressure turbine cascade SPLEEN C1</strong>. </p><p>This database complements aerodynamics measurements reported in the database "SPLEEN - High Speed Turbine Cascade – Test Case Database" which can be found at <a href="https://zenodo.org/doi/10.5281/zenodo.7264761">10.5281/zenodo.7264761</a>. </p><p>The data have been collected at the <strong>von Karman Institute for Fluid Dynamics</strong> during 2022 within the H2020 Clean Sky 2 project <strong>SPLEEN </strong>– Secondary and Leakage Flow Effects in High-Speed Low-Pressure Turbines.</p><p>This database contains documents that describe the experimental setup, instrumentation, measurement uncertainties, geometries, and the dataset structure related to the SPLEEN C1 PIV measurements.</p><p>The database contains experimental data of the test campaign conducted on the linear cascade codenamed SPLEEN C1 in the VKI high-speed wind tunnel S1/C.</p><p>PIV measurements were performed on the upstream blade-to-blade plane at cascade midspan, passage blade-to-blade plane at cascade midspan, and on a cascade outlet axial plane located 50% of the airfoil axial chord downstream the cascade trailing edge, near the cascade endwall (0-18% of the cascade span).</p><p>The turbine cascade geometry is representative of designs of high-speed low-pressure turbines for next-generation geared turbofan engines.</p><p>The measurement datasets describe the flow through the turbine cascade tested at on- and off-design conditions (cascade exit Reynolds and Mach numbers), with and without a turbulence grid located upstream of the cascade. The database includes measurements of flow velocity, flow angles, Mach numbers, and turbulence quantities.</p><p>The PIV measurements were performed on the <strong>SPLEEN C1 turbine cascade</strong> equipped <strong>WITH a REFERENCE CAVITY ENDWALL</strong> (Cavity Aref), <strong>WITHOUT WAKE GENERATOR</strong> (steady inlet flow), <strong>WITH and WITHOUT TURBULENCE GRID</strong> (different inlet turbulence level).</p><p>The project has received Funding from the Clean Sky 2 Joint Undertaking under the European Union's Horizon 2020 research and innovation program under the grant agreement 820883.</p>
Melting glass fibres recovered from wind turbine blades into new glass fibres for wind turbine blades: Dataset
<p><strong><span>Melting glass fibres recovered from wind turbine blades into new glass fibres for wind turbine blades: Dataset.</span></strong></p> <p><span>In the study titled “Melting glass fibres recovered from wind turbine blades into new glass fibres for wind turbine blades”, four different types of glass fibres were manufactured with varying fractions of recycled fibre powder, 0 wt%, 1.64 wt%, 1.90 wt% or 1.96 wt%. Furthermore, these glass fibre types were used to manufacture composite specimens and characterised by static tension tests in fibre and transverse directions. </span></p> <p><span>This dataset is a collection of 13 Excel files.</span></p> <p><span>The “Glass fibre properties and Weibull analysis” Excel file summarise the single fibre tensile testing of glass fibre types and strength analysis using unimodal 2-parameter Weibull theory. There are four sheets in the Excel files for 0 wt%, 1.64 wt%, 1.90 wt% or 1.96 wt% glass fibres. </span></p> <p><span>The Excel files “Single glass fibres-Stress-strain curves-0 %, 1.64 %, 1.90 %, 1.96 %” contains the raw data of individual fibres obtained from the single fibre tensile testing experiments.</span></p> <p><span>There are eight Excel files containing the raw data of static tensile tests in the fibre and transverse direction of the composites made with glass fibres were manufactured with varying fractions of recycled fibre powder, 0 wt%, 1.64 wt%, 1.90 wt% or 1.96 wt%.</span></p>
Turbine Blade Microstructure
<p>This microstructure has been created using tools and algorithms developed at the Technion, and are part of the IRIT geometric modeling kernel (<a href="https://www.cs.technion.ac.il/~irit/">https://www.cs.technion.ac.il/~irit/</a>).</p> <p>This turbine is a functional composition of trivariate spline tiles inside a macro trivariate shape of a turbine.</p> <p>Model is provided in IGES format, as B-spline surfaces.</p>
Post-processed dataset from 50000 numerical simulations of monopile-supported NREL 5MW wind turbine in OpenFAST
<p>The dataset contains two separate files: NREL_Trainset40000.mat and NREL_Testset10000.mat.</p> <p>The stored input enviormental and operational parameters are:</p> <ul> <li>Significant wave height, m (Hs), peak period, s (Tp), wave direction, deg (Wave_dir);</li> <li>Wind speed, m/s (Vw_mean, Vw_std), wind direction, deg (Wdir_mean, Wdir_std);</li> <li>Turbine rotational speed, rpm (Rpm_mean, Rpm_std), blade pitch, deg (Pitch_mean, Pitch_std), turbine yaw angle, deg (Yaw_mean, Yaw_std).</li> </ul> <p>The output of the simulations includes the time series, sampled at 50 Hz, of the reaction force and bending moments at the mudline:</p> <ul> <li>Fzz, N</li> <li>Mxx, Nm</li> <li>Myy, Nm</li> </ul> <p>contact: nandar.hlaing@uliege.be</p>
Large-Eddy Simulation of Wind Turbine Flows: A New Evaluation of Actuator Disk Models - Dataset
<p>Main data used in the following paper: Revaz, T.; Porté-Agel, F. Large-Eddy Simulation of Wind Turbine Flows: A New Evaluation of Actuator Disk Models. <em>Energies</em> <strong>2021</strong>, <em>14</em>, 3745. https://doi.org/10.3390/en14133745</p>
Data archive for "Flight behaviour of Red Kites within their breeding area in relation to local weather variables: Conclusions with regard to wind turbine collision mitigation"
<p>The archive contains the data files to reproduce the results presented in the article “Flight behaviour of Red Kites within their breeding area in relation to local weather variables: Conclusions with regard to wind turbine collision mitigation” published in the Journal of Applied Ecology.</p>
Experimental investigation of RVGs on a wind turbine airfoil
<p>This project contains the data obtained as a result of the Preludium Grant no 2022/45/N/ST8/01425 of the Polish National Science Centre fundings. Within the "Aeroacoustic investigations of streamwise vortex generators for boundary layer separation control" project, two main research tasks were defined:<br> 1. Post-processing and analysis of acoustic measurements using beamforming techniques to investigate RVGs effect on acoustic sources<br> 2. Post-processing and analysis of Particle Image Velocimetry (PIV) data to investigate a flow structure downstream of RVG</p> <p>The resutls from these tasks are uploaded here. The details of the data are included in the EOP_medata_1.docx document uploaded. <br>Further information regarding the data is published in the paper <a href="https://www.researchgate.net/publication/380825438_Aeroacoustic_effect_of_boundary_layer_separation_control_by_rod_vortex_generators_on_the_DU96-W-180_airfoil">https://www.researchgate.net/publication/380825438_Aeroacoustic_effect_of_boundary_layer_separation_control_by_rod_vortex_generators_on_the_DU96-W-180_airfoil</a></p>
SPLEEN - High Speed Turbine Cascade – Test Case Database
<p>This is an open-access database of experimental data of the flow in a <strong>high-speed low-pressure turbine cascade</strong>. The data have been collected at the <strong>von Karman Institute for Fluid Dynamics</strong> in the period 2018-2022 within the H2020 Clean Sky 2 project <strong>SPLEEN</strong> – Secondary and Leakage Flow Effects in High-Speed Low-Pressure Turbines, a project in collaboration with Safran Aircraft Engines.</p> <p>This version contains documents that describe the experimental setup, instrumentation, measurement uncertainties, geometries, and database structure.</p> <p>The database contains experimental data of the test campaign conducted on the linear cascade codenamed SPLEEN C1 in the VKI high-speed wind tunnel S1/C.</p> <p>The turbine cascade geometry is representative of designs of high-speed low-pressure turbines for next-generation geared turbofan engines.</p> <p>The measurement datasets describe the <strong>flow through the turbine cascade tested at on- and off-design conditions</strong>, with and without a wake generator located upstream of the cascade, and with and without endwall cavity injection. The database includes measurements of pressure, temperature, flow angles, Mach numbers, pressure loss, unsteady blade and endwall pressure and quasi-shear-stress.</p> <p>The database includes measurement datasets performed on the turbine cascade <strong>WITH a FLAT ENDWALL </strong>(no cavity)<strong> WITHOUT WAKE GENERATOR (</strong>Steady inlet flow)<strong>, WITH a FLAT ENDWALL </strong>(no cavity) <strong>AND WAKE GENERATOR</strong> (unsteady inlet flow)<strong>, WITH ENDWALL CAVITY</strong> (purged cavity) <strong>AND WAKE GENERATOR</strong> (unsteady inlet flow)<strong>.</strong></p> <p>The project has received Funding from the Clean Sky 2 Joint Undertaking under the European Union's Horizon 2020 research and innovation program under the grant agreement 820883.</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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