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15 results for “wake modelling”
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>
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>
OWA Wake Modelling Challenge Dataset
<p>This repository collects input and simulation datasets from the Offshore Wind Accelerator (OWA) Wake Modelling Challenge, whose objective is to improve confidence in wake models in the prediction of array efficiency. The data is meant to be used together with the open-source model evaluation scripts available in the following github repository: <a href="https://github.com/CENER-EPR/OWAbench">https://github.com/CENER-EPR/OWAbench</a></p> <p>The results of the challenge are summarized in the following paper:</p> <p>Sanz Rodrigo J, Borbón Guillén F, Fernandes Correia P M, García Hevia B, Schlez W, Schmidt S, Basu S, Li B, Nielsen P, Cathelain M, Dall’Ozzo C, Grignon L, Pullinger D (2020) Validation of Meso-Wake Models for Array Efficiency Prediction Using Operational Data from Five Offshore Wind Farms. J. Phys.: Conf. Ser., under review</p>
A physically interpretable data-driven surrogate model for wake steering
<p>PALM input files for the simulations performed in the study "A physically interpretable data-driven surrogate model for wake steering" by Sengers et al. (2022). </p> <p>The PALM code is available at <a href="https://palm.muk.uni-hannover.de/">https://palm.muk.uni-hannover.de</a><br> Additional information to the input files is given in the README file</p> <p>Cite this as:<br> B.A.M. Sengers (2022). Dataset: A physically interpretable data-driven surrogate model for wake steering. https://doi.org/10.5281/zenodo.6821164</p>
"A physics-based model for wind turbine wake expansion in the atmospheric boundary layer"
<p>Vahidi, Dara, and Fernando Porté-Agel. "A physics-based model for wind turbine wake expansion in the atmospheric boundary layer." <em>Journal of Fluid Mechanics</em> 943 (2022).</p>
Parameter uncertainty quantification of wake models to analyze effects of wake superposition: data and code
<p>Codebase for wake deficit, wake superposition, and wake-added turbulence modeling within Markov-chain Monte Carlo framework. Data for results and figures in associated paper is also included.</p>
Validation of an interpretable data-driven wake model using lidar measurements from a field wake steering experiment
<p>Selection of the data in the following paper:<br> Sengers, B. A. M., Steinfeld, G., Hulsman, P., & Kuehn, M. (2023). Validation of an interpretable data-driven wake model using lidar measurements from a free-field wake steering experiment. Wind Energy Science Discussions, 1-32.</p> <p>This data subset provides input parameters commonly used in wake models, as well as ten-minuted averaged cross sections of the flow field at 4 rotor diameters downstream, as measured by a nacelle-mounted lidar. </p> <p>Cite this as:<br> B.A.M. Sengers (2023). Dataset: Validation of an interpretable data-driven wake model using lidar measurements from a field wake steering experiment. https://doi.org/10.5281/zenodo.7741395</p>
Figures: Vortex model of the aerodynamic wake of airborne wind energy systems
<p>Figures in .pdf, .png and .fig format.</p><p>Figures in .fig format can be opened with MATLAB or other open source programming languages (e.g., Python thought the command scipy.io.loadmat or Octave)</p><p>Figures were updated after: Trevisi, F., Croce, A., and Riboldi, C. E. D.: Corrigendum to "Vortex model of the aerodynamic wake of airborne wind energy systems", published in Wind Energ. Sci., 8, 999–1016, 2023, https://doi.org/10.5194/wes-8-999-2023-corrigendum"</p>
Dataset supporting - On the Problem of Modeling the Boat Wake Climate; the Florida Intracoastal Waterway - by Forlini et al., submitted to JGR-Ocean
<p>This dataset comprises of 9 .txt file containing water levels data (m) necessary to reconstruct the wakes observations for all the instruments (Acoustic Doppler Velocimeter) deployed during the field experiment.</p> <p> </p>
Aerodynamics code used in Wind Energy Science paper "Comparison of a coupled near- and far-wake model with a free-wake vortex code"
<p>This research code has been developed from the start of my PhD as a first step before the HAWC2 implementation of the near wake model.</p> <p>It can be used to make aerodynamic computations of a stiff wind turbine rotor, and it includes</p> <ul> <li>A BEM and far wake model implementation based on the one in HAWC2</li> <li>An attached flow unsteady airfoil aerodynamics model including the modifications described in the WES article</li> <li>Most importantly a near wake model implementation including all major modifications except the recent stand still extension presented at TORQUE 2016</li> </ul> <p>All the data files need to be in a subfolder 'NREL_5MW' located in the same folder as the compiled source code.</p> <p>With the present (hardcoded) settings, the program will simulate the NREL 5 MW reference turbine for 650 seconds, with blade vibrations according to different prescribed mode shapes after steady state is reached. The aerodynamics model is a coupled near and far wake model. The integrated aerodynamic work during 1 period of the different prescribed vibrations will be output in the file 'aerowork.out' .</p> <p>The NREL 5 MW turbine is described in:</p> <p>Jonkman, J., Butterfield, S., Musial,W., and Scott, G.: Definition of a 5-MW Reference Wind Turbine for Offshore System Development, National Renewable Energy Laboratory, 2009.</p>
Large-eddy simulation of yawed wind-turbine wakes: comparisons with wind tunnel measurements and analytical wake models
<p>Dataset of the paper "Large-eddy simulation of yawed wind-turbine wakes: comparisons with wind tunnel measurements and analytical wake models" published on Energies [1].</p> <p>[1] Lin, M., & Porté-Agel, F. (2019). Large-eddy simulation of yawed wind-turbine wakes: comparisons with wind tunnel measurements and analytical wake models. <em>Energies</em>, <em>12</em>(23), 4574.</p>
Improvements to the dynamic wake meandering model by incorporating the turbulent Schmidt number
<p>Data to replicate the figures in Brugger, P., Markfort, C., and Porté-Agel, F.: Improvements to the Dynamic Wake Meandering Model by incorporating the turbulent Schmidt number, Wind Energ. Sci. Discuss. [preprint], https://doi.org/10.5194/wes-2023-150, 2023.</p>
Data set used in article: Model Predictive Control for Wake Redirection in Wind Farms: a Koopman Dynamic Mode Decomposition Approach
<p>Step-wise yaw deflection in 2 wind turbines in SOWFA. More information in the article.</p>
Model integration of circadian and sleep-wake driven contributions to rhythmic gene expression reveals distinct regulatory principles
Open the record for dataset details and reuse information.
Model integration of circadian and sleep-wake driven contributions to rhythmic gene expression reveals novel regulatory principles
GEO Series GSE262410. Mus musculus. 124 samples. Type: Expression profiling by high throughput sequencing.
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