Find research datasets worth reusing

opencc-by-4.0Aug 2022View details →

Environmental Flows Turbine Optimization

This is a dataset of the results from determining the capacity (MW) of potential minimum flow turbines based on available environmental flows. This includes the turbine capacity from two methods of data processing, as well as the iterative screening envelopes to determine locations of likely opportunities for implementing these environmental flow turbines.  Cite this dataset as well as the manuscript by the authors that is available through Renewable and Sustainable Energy Reviews.

Supplementary audio files: Propagation effects in the synthesis of wind turbine noise

The audio files are supplementary files required for the audio article titled: "Propagation effects in the synthesis of wind turbine noise". Each audio file refers to a signal of a test obtained from the wind turbine noise model which is described in the article.  The audio files can be read as:      NNx-x-TETIN-eeeeeee.mp3 NNx-x refers to the Test case and case number for the syntheisized trailing edge and turbulent inflow noise, eeeeeee is the description of the specific case. (tauTT- angle of the receiver, ff - for free field, GPE for ground and propagation effects included, GPE_Turb for ground and propagation effects included with turbulence scattering) eg: C2-2-TETIN_tau80_GPE_Turb.mp3 is the synthesized sound for Test case C2-2 in the article which includes the ground and propagation effects and also scattering due to turbulence. The receiver is at an angle of 80° with respect to the wind direction.

"A physics-based model for wind turbine wake expansion in the atmospheric boundary layer"

Vahidi, Dara, and Fernando Porté-Agel. "A physics-based model for wind turbine wake expansion in the atmospheric boundary layer." Journal of Fluid Mechanics 943 (2022).

"Wind turbine wakes on escarpments: A wind-tunnel study"

Dar, Arslan Salim, and Fernando Porté-Agel. "Wind turbine wakes on escarpments: A wind-tunnel study." Renewable Energy 181 (2022): 1258-1275.

"An experimental investigation of a roof-mounted horizontal-axis wind turbine in an idealized urban environment"

Dar, Arslan Salim, Guillem Armengol Barcos, and Fernando Porté-Agel. "An experimental investigation of a roof-mounted horizontal-axis wind turbine in an idealized urban environment." Renewable Energy (2022).

D6.6 Dataset of turbine properties of Hog-Jaeren SE

This dataset was used in Deliverable 6.6 of Upwards. It contains turbine properties of turbines in a simulation of the Hog-Jaeren wind park. The wind direction is chosen to be south-east. The simulation was conducted for different yaw misalignments of the turbines in the freestream. Thus this dataset contains 28 folders which each correspond to one yaw misalignment. The folder name indicates the yaw misalignment where 270 corresponds to a yaw misalignment of 0° while 260 corresponds to -10°. The files in each folder contain the following properties: <ul> <li>time</li> <li> torqueGen</li> <li>powerRotor</li> <li>rotSpeed</li> <li>thrust</li> <li> torqueRotor</li> <li>azimuth</li> <li>azimuth1</li> <li>azimuth2</li> <li>azimuth3</li> </ul> Azimuth1, azimuth2 and azimuth3 are the azimuth angles of the three blades in radians. Azimuth is the same as azimuth1, just in degrees. These properties are available for all turbines in the park. Further the accumulated properties are available for each row of turbines and the whole park.

D6.6 Dataset of turbine properties of Hog-Jaeren NW

This dataset was used in Deliverable 6.6 of Upwards. It contains turbine properties of turbines in a simulation of the Hog-Jaeren wind park. The wind direction is chosen to be north-west. The simulation was conducted for different yaw misalignents of the turbines in the freestream. Thus this dataset contains 27 folders which each correspnd to one yaw misalignment. The folder name indicates the yaw misalignment where 270 corresponds to a yaw misalignment of 0° while 260 corresponds to -10°. The files in each folder contain the following properties: <ul> <li> time </li> <li> torqueGen </li> <li> powerRotor </li> <li> rotSpeed </li> <li> thrust </li> <li> torqueRotor </li> <li> azimuth </li> <li> azimuth1 </li> <li> azimuth2 </li> <li> azimuth3 </li> <li> Blade1 </li> <li> Blade2 </li> <li> Blade3 </li> </ul> Azimuth1, azimuth2 and azimuth3 are the azimuth angles of the three blades in radians. Azimuth is the same as azimuth1, just in degrees. Blade1, Blade2 and Blade3 are the accumulated forces applied to the three blades. These properties are available for all turbines in the park. Further the accumulated properties are available for each row of turbines and the whole park.

D6.6 Dataset of turbine properties of Lillgrund wind park

This dataset was used in Deliverable 6.6 of Upwards. It contains turbine properties of turbines in a simulation of the Lillgrund wind park. The simulation was conducted for different yaw misalignments of the turbines in the freestream. Thus this dataset contains 4 folders which each correspond to one yaw misalignment. The folder name indicates the yaw misalignment where 270 corresponds to a yaw misalignment of 0° while 260 corresponds to -10°. The files in each folder contain the following properties: <ul> <li>time</li> <li>torqueGen</li> <li>powerRotor</li> <li>rotSpeed</li> <li>thrust</li> <li>torqueRotor</li> </ul> These properties are available for all turbines in the park. Further the accumulated properties are available for each row of turbines and the whole park.

Supplementary audio files: Physics-based synthesis of wind turbine noise

The supplementary files required in the thesis "Physics-based synthesis of wind turbine noise" for the "Chapter 3: Synthesis tool".  A repository can also be found on: https://sites.google.com/view/david-mascarenhas For the test cases: Section 3.9 -------------------------------------------------- The audio files are supplementary files required for the audio article titled: "Propagation effects in the synthesis of wind turbine noise". Each audio file refers to a signal of a test obtained from the wind turbine noise model which is described in the article.  The audio files can be read as:      NNx-x-TETIN-eeeeeee.mp3 NNx-x refers to the Test case and case number for the syntheisized trailing edge and turbulent inflow noise, eeeeeee is the description of the specific case. (tauTT- angle of the receiver, ff - for free field, GPE for ground and propagation effects included, GPE_Turb for ground and propagation effects included with turbulence scattering) eg: C2-2-TETIN_tau80_GPE_Turb.mp3 is the synthesized sound for Test case C2-2 in the article which includes the ground and propagation effects and also scattering due to turbulence. The receiver is at an angle of 80° with respect to the wind direction.

opencc-by-4.0Sep 2023View details →

Radar-based sensing of wind turbines blades based on 35 GHz FMCW sensors installed at operational wind turbine towers

The dataset contains radar-based measurements of rotor blades from three operational wind turbines as part of a structural health monitoring system. For this purpose, a sensor box with a 35 GHz radar sensor (about 1 000 measurements per second) and a camera system (about 100 images per second), is mounted on each wind turbine tower at approximately 100 m height. In order to distinguish individual rotor blades, a machine-readable marker printed on a self-adhesive film was applied on the blade’s surface. When a rotor blade passes the sensor, the camera captures an image of the marker while the radar records a measurement. The marker is then identified and the recorded data is assigned to a particular rotor blade. The measurements demonstrate that the damage detection methodology can be transferred to an image processing problem. The challenge is to manage the strong influence from variable environmental and operational conditions, e.g. wind speed, azimuth orientation, that modify the rotor blade appearance in the radargram significantly. The dataset contains measurements from the intact turbine blade conditions, because it was not possible to introduce structural damage.

opencc-zeroJun 2024View details →

Dataset: Digital Turbine, Inc. (APPS) Stock Performance

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

opencc-zeroJun 2024View details →

Dataset: Digital Turbine, Inc. (APPS) Stock Performance

opencc-by-4.0Jun 2024View details →

Wind turbine condition monitoring dataset of Fraunhofer LBF

<h3>Fraunhofer wind turbine dataset  contains monitoring data from a 750 W wind turbine (WT), including accelerometers and tachometer, to capture structural response, bearing vibrations and rotational velocity. Additionally, temperatures of the structure, wind speed and wind direction have been measured, while weather conditions have been acquired from selected sources. Various damage scenarios, including mass imbalance, and aerodynamic imbalance as well as damages on bearings’ outer race, inner race and roller element have been implemented. The availability of time series data makes the dataset well suited for both machine learning and signal processing-based condition monitoring (CM) applications. The availability of heterogeneous sensors has created a dataset particularly suited for information fusion, data fusion, multi-sensor approaches, and holistic monitoring. Experiments were conducted in real-world conditions outside of a controlled laboratory environment, thereby introducing challenges such as variable rotor speed, noise, overloads, and other environmental factors. Consequently, the dataset is qualified for tasks involving uncertainty quantification and signal pre-processing. This document will detail the test equipment, experimental procedures, simulated damage cases, measurement parameters, data specifics, and preliminary analysis aimed at validating data quality.</h3> <h3>See the full data descriptor at: https://doi.org/10.1038/s41597-024-03934-5</h3>

opencc-by-nc-4.0Dec 2017View details →

Ultrasound scans of wind turbine bearings with white etching crack damage

A set of ultrasound images of wind turbine bearings with white etching crack subsurface damage. Attenuation levels above -10 dB indicate subsurface damage. Five tests were run under constant loading in a laboratory test rig, with two bearings in parallel. Tests were stopped at the indicated times (in hours) when vibration levels went above a certain threshold. This typically means that one of the two bearings has failed, although this is somewhat inconclusive for the fifth test where both bearings seem to be close to failure. For each bearing two sides were scanned (indicated by A and H). The experiment was performed in summer-autumn 2017 by DTU Wind Energy, as part of the FP7 Integrated Research Programme in the field of Wind Energy (IRPWIND), 2nd Round of Joint Experiments. For more information about this dataset please contact Dr Hilmar Danielsen at DTU Wind Energy.

opencc-by-4.0Jun 2018View details →

Observations of microscale tensile fatigue damage mechanisms of composite materials for wind turbine blades

A scout and zoom dataset including video-versions of the figures behind the following paper to where the references should be given: Mikkelsen, L.P. Observations of microscale tensile fatigue damage mechanisms of composite materials for wind turbine blades, IOP Conf. Series: Materials Science and Engineering 380 (2018) 012006 , http://iopscience.iop.org/article/10.1088/1757-899X/388/1/012006. The SFoV data-set is saved as both a 3D and a 2D (zipped) tiff stack.

opencc-by-4.0May 2018View details →

Data set: Control design, implementation and evaluation for an in-field 500 kW wind turbine with a fixed-displacement hydraulic drivetrain

Data set of Wind Energy Science (WES) paper: Control design, implementation and evaluation for an in-field 500 kW wind turbine with a fixed-displacement hydraulic drivetrain

opencc-by-4.0Jul 2019View details →

Wake data documentation for a wind turbine rotor with winglets

This is the documentation of data, measured in a experimental campaign, in which the effects of winglets on a model wind turbine rotor were investigated

opencc-by-4.0Aug 2024View details →

SiWiRoRa - Simulated Wind-turbine Rotor-blade Radargrams

<h1>SiWiRoRa</h1> SiWiRoRa stand for 'Simulated Wind-turbine Rotor-blade Radargrams'. <h2>Overview</h2> A novel kind of dataset comprised of 9504 grayscale images (quadratic, 224 px) representing simulated radargrams. SiWiRoRa enables analytical machine-learning experiments in the emerging area of radar-based computer-vision research on wind-turbine rotor blades. Here, radargrams are images showing distance on the horizontal x-axis (increasing from left to right) and time on the vertical y-axis (increasing from top to bottom) as well as reflected intensity given by the colorscale (increasing from black to white). <h2>Details</h2> Geometries have been modeled using Cyberbotics Webots (1188 different configurations) and stochastic elements have been added in post-processing (additional 8 independent repetitions per 1 configuration). Besides this aforementioned stochastic noise, the dataset has a full-factorial design consisting in...  <ul> <li>9 distinct levels of rotor speeds (clockwise rotation when viewed from the exterior onto the rotor and the radar behind it)</li> <li>11 levels of the yaw angles (between radar direction and nacelle orientation)</li> <li>6 variations of wind pressure (forcing the rotor closer to the radar)</li> <li>2 different time offsets (corresponding to alternative triggers of the radar)</li> </ul> Geometries have been chosen so as to yield non-axisymmetrical radargrams (even apart from noise). <h2>Acknowledgements</h2> The present dataset complements and has been inspired by the field measurements published under https://doi.org/10.5281/zenodo.8366654 (Hyperlink is provided in the References section).

opencc-by-4.0Sep 2024View details →

Offshore wind turbine damage probability maps and hub height TC wind speeds for U.S. Atlantic and Gulf Coasts exposed to historical and future tropical cyclones

Damage probability maps for offshore wind turbines exposed to tropical cyclones (TCs) under both historical and future climate scenarios along the U.S. Atlantic and Gulf Coasts are presented in this dataset. TCs are generated using <a href="../records/10392725" target="_blank" rel="noopener">The Risk Analysis Framework for Tropical Cyclones (RAFT)</a>, forced by <a href="https://pcmdi.llnl.gov/CMIP6/" target="_blank" rel="noopener">CMIP6</a> historical and future global climate simulations. Maximum wind speeds for 20- and 50-year TCs are processed through a <a href="https://www.sciencedirect.com/science/article/pii/S0960148120311423">fragility function</a> specific to offshore wind (OSW) turbines in order to estimate the probability of damage – specifically yielding and buckling – based on wind speed intensity.  Included data: <ul> <li>TC wind speeds: Peak 10-min mean hub height (90m) TC wind speed maps</li> <li>Damage states: Yielding and Buckling probability maps for OSW turbines</li> <li>Geographic coverage: U.S. Atlantic and Gulf Coasts (up to 200km from the shoreline)</li> <li>Time periods: Historic (1980-2014) and Future (2066-2100)</li> </ul> Methodology: <ul> <li>Tropical cyclone simulation: The RAFT TC model is used to simulate storms for historical and future climates using CMIP6 environmental conditions.</li> <li>TC impact metric: Wind speeds associated with 20- and 50-year return period TCs are used to estimate the aerodynamic and sea wave loading on OSW turbines.</li> <li>Fragility functions: Wind speeds are input into a fragility function developed for OSW turbines, estimating the probability of yielding and buckling damage.</li> <li>Damage probability maps: The results consist of eight (8) gridded damage probability maps representing the likelihoods of yielding and buckling to OSW turbines from 20- and 50-year TCs under historical and future climatic conditions.</li> </ul> Potential Uses: <ul> <li>Assessing the spatial vulnerability of OSW infrastructure to TCs</li> <li>Supporting decision-making for the design and siting of turbines</li> <li>Evaluating the impact of climate change on the risk of damage to OSW infrastructure</li> </ul> For further insights into this dataset, users are encouraged to refer to the associated paper: <a href="https://www.nature.com/articles/s43247-024-01887-6">https://www.nature.com/articles/s43247-024-01887-6</a> This dataset offers valuable insights into the potential impact of TCs on offshore wind infrastructure, aiding in risk assessment and resilience planning for the renewable energy sector.