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108 results for “wind modelling”
Tropical Pacific SST and wind anomalies generated by a Nonlinear Inverse Model
<p>Tropical Pacific (40S-40N; 120E-50W) sea surface temperature (SST), zonal wind (U) and meridional wind (V) anomalies generated by the Nonlinear Inverse Model described in Martinez-Villalobos et al., 2024 (https://doi.org/10.1038/s41612-024-00675-5). The data consists in 99 realizations (<a href="../api/records/10411023/draft/files/NLIM_output_085.nc/content" target="_blank" rel="noopener noreferrer">NLIM_output_XXX.nc</a>) of 1,000yrs each emulating SST, U, and V monthly anomalies conditions during 1980-2020 (<a href="../api/records/10411023/draft/files/Monthly_obs_1980_2020.nc/content" target="_blank" rel="noopener noreferrer">Monthly_obs_1980_2020.nc</a>) given in a 2.5deg-2.5deg grid. For observations, we used the NOAA Extended Reconstruction SST v5 reanalysis (SST; Huang et al., 2017) and NCEP-NCAR reanalysis (winds; Kalnay et al., 1996) The observed anomalies are calculated as described in Martinez-Villalobos et al., 2024 (https://doi.org/10.1038/s41612-024-00675-5).</p> <p>Given that the stochastic forcing considered is white in time and space (https://doi.org/10.1038/s41612-024-00675-5; Methods, section "Offline simulation of SSH_{12}, PC2, and spatial patterns fron nonlinear inverse model output"), the spatial patterns and lead-lag relationships are better identified using composites. A modification of the methodology that allows for spatially coherent stochastic forcing will be implemented in a future article.</p> <p>When using the data please cite https://doi.org/10.5281/zenodo.10411023 (the data) and Martinez-Villalobos et al., 2024 (https://doi.org/10.1038/s41612-024-00675-5; for the methodology). </p> <p>Any question, please contact Cristian Martinez-Villalobos at his email cristian.martinez.v@uai.cl</p> <p>References</p> <p>Martinez-Villalobos, C., Dewitte, B., Garreaud, R.D. <em>et al.</em> Extreme coastal El Niño events are tightly linked to the development of the Pacific Meridional Modes. <em>npj Clim Atmos Sci</em> <strong>7</strong>, 123 (2024). https://doi.org/10.1038/s41612-024-00675-5</p> <p>Huang, B. et al. Extended Reconstructed Sea Surface Temperature, Version 5 (ERSSTv5): Upgrades, Validations, and Intercomparisons. Journal of Climate 30, 8179–8205 (2017).</p> <p>Kalnay, E. et al. The NCEP/NCAR 40-Year Reanalysis Project. Bulletin of the American Meteorological Society 77, 437–471 (1996).</p> <p> </p>
EXPOS Model for Estimating Topographic Exposure to Wind
EXPOS is a simple model of topographic exposure to wind that utilizes digital elevation data to predict which points on a landscape are exposed to or protected from a given wind direction. The model was developed to approximate the effects of topography on hurricane wind flow at a landscape scale (~ 10 km). The model requires an accurate digital elevation map and a specified wind direction. Each point on the elevation map is then classified as protected or exposed, depending on whether or not it falls within the wind shadow cast by points upwind. The wind shadow is estimated by assuming that the wind bends downward no more than a fixed inflection angle from the horizontal as it passes over a height of land. The effects of topographic features beyond the margins of the input elevation map are of course not predicted by the model. For a given landscape and wind direction, predicted protected areas decrease in size as the inflection angle increases. Application of the model in studies of two recent hurricanes suggests that the probability of wind damage in protected areas also decreases as the inflection angle increases. An inflection angle of about 5 to 10 degrees was found to give good results. For more information on the EXPOS model, please see the published paper (Boose, E. R., D. R. Foster, and M. Fluet. 1994. Hurricane impacts to tropical and temperate forest landscapes. Ecological Monographs 64: 369-400). Additional information is contained in the documentation that accompanies the program. For an updated version of the EXPOS model in R and Python, please see HF447.
HURRECON Model for Estimating Hurricane Wind Speed, Direction and Damage
HURRECON is a simple meteorological model that estimates hurricane surface wind speed and direction based on the track, size, and intensity of a hurricane and the surface type (land or water). The model also estimates Fujita-scale wind damage as a function of peak 1/4 mile wind speed and wind gust factor. Estimates can be generated for a single site or a rectangular region. The model is based on published empirical studies of many hurricanes. HURRECON can be used to study the impacts of individual hurricanes or to reconstruct the hurricane disturbance regime for a particular region. For more information on the most recent version of the model please see the published paper (Boose, E. R., K. E. Chamberlin and D. R. Foster. 2001. Landscape and regional impacts of hurricanes in New England. Ecological Monographs 71: 27-48). Additional information is contained in the documentation that accompanies the program. For an updated version of the HURRECON model in R and Python, please see HF446.
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>
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>
Wind measurement data from the publication: "Development of a load model validation framework applied to synthetic turbulent wind field evaluation"
<h3>Dataset description:</h3> <p>This datasat represents supplementary material used in the contribution "Development of a load model validation framework applied to<br>synthetic turbulent wind field evaluation" by Meyer, Huhn and Gottschall.</p> <p>Wind measurements from the Testfeld BHV are made available. For installation details, see the mentioned reference.</p> <p> </p> <h3>File description:</h3> <ul> <li>Lidar_HWS.nc - Horizontal wind speed measurements (10 min averages) from a WindCube V2 vertical profiler for one day with a low-level jet occurrence ( <div> <div>2021-04-20)</div> </div> </li> <li>Cups_HWS.nc - Horizontal wind speed measurements (10 min averages) from cup anemometer installed on a met mast for the same day</li> <li>Ensemble_averaged_Spectra.nc - Ensemble averaged spectra for neutral and near neutral situations from a Gill Windmaster at 110m above ground level, used to fit the Mann and KSEC model parameters</li> </ul> <h3> </h3> <h3>Referencing:</h3> <p>When used, please cite like the following:</p> <p>Meyer, Paul J., Matthias L. Huhn, and Julia Gottschall. 2024. "Development of a Load Model Validation Framework Applied to Synthetic Turbulent Wind Field Evaluation" <em>Energies</em> 17, no. 4: 797. https://doi.org/10.3390/en17040797</p> <p> </p> <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>
Additional evidence for a pulsar wind nebula in SN 1987A from multi-epoch X-ray data and MHD modelling
<p>This is a basic reproduction package for the paper "Additional evidence for a pulsar wind nebula in the hearth of sN 1987A from multi-epoch X-ray data and MHD modeling" by Greco et al. 2022. It aims to provide the most important data products to check and reproduce the main results of the paper.</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>
HURRECON Model for Estimating Hurricane Wind Speed, Direction, and Damage (R and Python)
The HURRECON model estimates wind speed, wind direction, enhanced Fujita scale wind damage, and duration of EF0 to EF5 winds as a function of hurricane location and maximum sustained wind speed. Results may be generated for a single site or an entire region. Hurricane track and intensity data may be imported directly from the US National Hurricane Center's HURDAT2 database. HURRECON is available in R and Python. The R version is available on CRAN as HurreconR. The model is an updated version of the original HURRECON model written in Borland Pascal for use with Idrisi (see HF025). New features include support for: (1) estimating wind damage on the enhanced Fujita scale, (2) importing hurricane track and intensity data directly from HURDAT2, (3) creating a land-water file with user-selected geographic coordinates and spatial resolution, and (4) creating plots of site and regional results. The model equations for estimating wind speed and direction, including parameter values for inflow angle, friction factor, and wind gust factor (over land and water), are unchanged from the original HURRECON model. For more details and sample datasets, see the project website on GitHub (https://github.com/hurrecon-model).
EXPOS Model for Estimating Topographic Exposure to Wind (R and Python)
The EXPOS model uses a digital elevation model (DEM) to estimate exposed and protected areas for a given hurricane wind direction and inflection angle. The resulting topograhic exposure maps can be combined with output from the HURRECON model to estimate hurricane wind damage across a region. EXPOS is available in R and Python. The R version is available on CRAN as ExposR. The model is an updated version of the original EXPOS model written in Borland Pascal for use with Idrisi (see HF024). For more details and sample datasets, see the project website on GitHub (https://github.com/expos-model).
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>
The winds of young Solar-type stars in the Hyades - Quiet Sun model
<p>This is the quiet Sun model from my MNRAS paper "The winds of young Solar-type stars in the Hyades"(https://doi.org/10.1093/mnras/stab1696). Please see the paper for a full description.</p>
Wind tunnel experiment of a micro wind farm model
<p>Simultaneous strain gage measurements of sixty porous disk models, in a scaled wind farm with one hundred models, and for fifty-six different layouts. </p> <p>For detailed information about the experimental setup and wind farm layouts see: </p> <p>Bossuyt, J., Meneveau, C., & Meyers, J. (2018). Effect of layout on asymptotic boundary layer regime in deep wind farms. <em>Physical Review Fluids. See also:</em> https://arxiv.org/abs/1808.09579 .</p> <p>For more information about the experimental design of the porous disk models, see also:</p> <p>Bossuyt, J., Howland, M. F., Meneveau, C., & Meyers, J. (2017). Measurement of unsteady loading and power output variability in a micro wind farm model in a wind tunnel. <em>Experiments in Fluids</em>, <em>58</em>(1), 1. http://doi.org/10.1007/s00348-016-2278-6</p> <p> Bossuyt, J., Meneveau, C., & Meyers, J. (2017). Wind farm power fluctuations and spatial sampling of turbulent boundary layers. <em>Journal of Fluid Mechanics</em>, <em>823</em>, 329-344. http://doi.org/10.1017/jfm.2017.328</p> <p> </p> <p>The data contains matrices 'WF_U', 'x', and 'y', and variable 'fs' for each layout. <br> The matrix 'WF_U' contains the reconstructed velocity signal in m/s measured by each porous disk, and has size ( 20 , 3 , number of time samples), with 20 the number of porous disk rows, and 3 the number of streamwise aligned porous disk columns in the wind farm. Matrices 'x', and 'y' have size (20,3) and contain the locations of each instrumented porous disk in units of disk diameter D = 0.03m. It is important to note that the wind farm has one extra column of non-instrumented porous disk models on each side, for a total of 20x5=100 porous disk models.The variable 'fs' contains the sampling frequency in Hz, at which all 60 porous disks are simultaneously sampled.</p> <p>--------------------------------------------------------<br> Example code to load data in Matlab :<br> --------------------------------------------------------<br> filename = 'U_C1_1.h5';<br> fileID = H5F.open(filename,'H5F_ACC_RDONLY','H5P_DEFAULT');</p> <p>datasetID = H5D.open(fileID,'WF_U');<br> WF_U = H5D.read(datasetID,'H5ML_DEFAULT','H5S_ALL','H5S_ALL','H5P_DEFAULT');<br> H5D.close(datasetID);</p> <p>datasetID = H5D.open(fileID,'fs');<br> fs = H5D.read(datasetID,'H5ML_DEFAULT','H5S_ALL','H5S_ALL','H5P_DEFAULT');<br> H5D.close(datasetID);</p> <p>datasetID = H5D.open(fileID,'x');<br> x = H5D.read(datasetID,'H5ML_DEFAULT','H5S_ALL','H5S_ALL','H5P_DEFAULT');<br> H5D.close(datasetID);</p> <p>datasetID = H5D.open(fileID,'y');<br> y = H5D.read(datasetID,'H5ML_DEFAULT','H5S_ALL','H5S_ALL','H5P_DEFAULT');<br> H5D.close(datasetID);</p> <p>H5F.close(fileID);</p> <p>--------------------------------------------------------<br> Example code to load data in Python:<br> --------------------------------------------------------<br> import h5py<br> filename = 'U_C1_1.h5'<br> f = h5py.File(filename, 'r')</p> <p>U = f['WF_U'][()]<br> x = f['x'][()]<br> y = f['y'][()]<br> fs = f['fs'][0][0]<br> f.close()</p> <p>--------------------------------------------------------<br> Example code to generate figures 15 and 16 of Bossuyt et al. (2018). Effect of layout on asymptotic boundary layer regime in deep wind farms. Physical Review Fluids, in Matlab<br> --------------------------------------------------------<br> WF_cases_selected = 1:7;</p> <p>folder = '/';% folder with files</p> <p>WF_cases_l = {'U_C1';'U_C2';'NU1_C1';'NU1_C2';'NU2_C1';'NU2_C2';'NU2_C3'};% name of layout variations<br> WF_cases_n = [6, 7, 11, 8, 11, 7, 6]; % 'number of layout variations for each case</p> <p>WF_data.x = cell( length(WF_cases_selected) , 1);% x - coordinates of porous disk locations<br> WF_data.y = cell( length(WF_cases_selected) , 1);% y - coordinates of porous disk locations<br> WF_data.shift = cell( length(WF_cases_selected) , 1);% spanwise shift of layout series<br> WF_data.fs = cell( length(WF_cases_selected) , 1);<br> WF_data.WF_Pm = cell( length(WF_cases_selected) , 1);<br> WF_data.WF_Um = cell( length(WF_cases_selected) , 1);<br> WF_data.WF_U_rms = cell( length(WF_cases_selected) , 1);</p> <p><br> for i = 1 : length(WF_cases_selected)<br> <br> WF_data_case = struct;<br> WF_data_case.x = cell( WF_cases_n(i) , 1);<br> WF_data_case.y = cell( WF_cases_n(i) , 1);<br> WF_data_case.shift = cell( WF_cases_n(i) , 1);<br> WF_data_case.fs = cell( WF_cases_n(i) , 1);<br> WF_data_case.WF_Pm = cell( WF_cases_n(i) , 1);<br> WF_data_case.WF_Um = cell( WF_cases_n(i) , 1);<br> WF_data_case.WF_U_rms = cell( WF_cases_n(i) , 1);<br> <br> for j = 1:WF_cases_n(i)<br> clc<br> i<br> j<br> <br> WF_data_var = struct;<br> <br> %read the file<br> filename = [folder WF_cases_l{i} '_' num2str(j) '.h5'];<br> fileID = H5F.open(filename,'H5F_ACC_RDONLY','H5P_DEFAULT');<br> <br> datasetID = H5D.open(fileID,'WF_U');<br> WF_data_var.WF_U = H5D.read(datasetID,'H5ML_DEFAULT','H5S_ALL','H5S_ALL','H5P_DEFAULT');<br> H5D.close(datasetID);<br> <br> datasetID = H5D.open(fileID,'fs');<br> WF_data_case.fs{j} = H5D.read(datasetID,'H5ML_DEFAULT','H5S_ALL','H5S_ALL','H5P_DEFAULT');<br> H5D.close(datasetID);<br> <br> datasetID = H5D.open(fileID,'x');<br> WF_data_case.x{j} = H5D.read(datasetID,'H5ML_DEFAULT','H5S_ALL','H5S_ALL','H5P_DEFAULT');<br> H5D.close(datasetID);<br> <br> datasetID = H5D.open(fileID,'y');<br> WF_data_case.y{j} = H5D.read(datasetID,'H5ML_DEFAULT','H5S_ALL','H5S_ALL','H5P_DEFAULT');<br> H5D.close(datasetID);<br> <br> H5F.close(fileID);<br> <br> WF_data_var.WF_P = WF_data_var.WF_U.^3;</p> <p> % Time averaged power<br> WF_data_case.WF_Pm{j} = mean(WF_data_var.WF_P,3);<br> <br> % normalize by power in first row: Pi/P1<br> WF_data_case.WF_Pm{j} = WF_data_case.WF_Pm{j}./mean(WF_data_case.WF_Pm{j}(1,:));<br> <br> % Time averaged velocity<br> WF_data_case.WF_Um{j} = mean(WF_data_var.WF_U,3);<br> <br> % u_rms --> TI<br> WF_data_case.WF_U_rms{j} = std(WF_data_var.WF_U,[],3);<br> end<br> <br> WF_data.x{i} = WF_data_case.x;<br> WF_data.y{i} = WF_data_case.y;<br> WF_data.fs{i} = WF_data_case.fs;<br> WF_data.WF_Pm{i} = WF_data_case.WF_Pm;<br> WF_data.WF_Um{i} = WF_data_case.WF_Um;<br> WF_data.WF_U_rms{i} = WF_data_case.WF_U_rms;<br> <br> %determine spanwise shift for plot legends<br> tmp1 = WF_data.y{i}{j-1};<br> tmp2 = WF_data.y{i}{j};<br> dy = diff( [tmp1(:,1) tmp2(:,1)] ,1,2);<br> dy = max(dy(abs(dy)>0));<br> WF_data.shift{i} = 0:dy:(WF_cases_n(i)-1)*dy;<br> <br> end</p> <p>%%<br> line_tick = {'o-','*-','+-','d-','s-','^-','v-','<-','>-','p-','h-'};<br> line_color = [51,160,44; 141,211,199; 31,120,180; 106,61,154; 227,26,28; 177,89,40; 255,127,0; 166,206,227]./255;</p> <p>legend_items = cell(size(WF_cases_selected));<br> for i = 1:length(legend_items)<br> legend_items{i} = strrep(WF_cases_l{i},'_','-');<br> end</p> <p>%% average power entire farm<br> row_start = 1;<br> row_end = 19;<br> f1 = figure;<br> set(gcf,'paperposition',[0,0,8.4,4.9])<br> hold on</p> <p>for i = 1 : length(WF_cases_selected)<br> tmp_P = zeros(size(WF_data.shift{i}));<br> for j = 1:WF_cases_n(i)<br> tmp_P(j) = mean(mean( WF_data.WF_Pm{i}{j}(row_start:row_end,:)));<br> end<br> plot( WF_data.shift{i} , tmp_P, line_tick{i} ,'Color', line_color(i,:) ,'MarkerFaceColor', line_color(i,:) )<br> end</p> <p>% manualy plot errorbars<br> for i = 1:length(WF_cases_selected)<br> tmp_P = zeros(size(WF_data.shift{i}));<br> for j = 1:WF_cases_n(i)<br> tmp_P(j) = mean(mean( WF_data.WF_Pm{i}{j}(row_start:row_end,:)));<br> end<br> px = WF_data.shift{i} ;<br> py = tmp_P;<br> pw = 0.05;<br> pe = zeros(size(px))+0.01;%for uncertainty value see Bossuyt et al. (2018) Physical Review Fluids. <br> for j = 1:WF_cases_n(i)<br> plot( [px(j)-pw/2 px(j)+pw/2] , [py(j)+pe(j) py(j)+pe(j)],'-', 'Color', line_color(i,:),'LineWidth',0.5)<br> plot( [px(j)-pw/2 px(j)+pw/2] , [py(j)-pe(j) py(j)-pe(j)],'-', 'Color', line_color(i,:),'LineWidth',0.5)<br> plot( [px(j) px(j)],[py(j)-pe(j) py(j)+pe(j)],':', 'Color', line_color(i,:),'LineWidth',0.5)<br> end<br> end<br> xlabel('\Delta_y [D]')<br> ylabel('$\langle P_i /P_1\rangle_{1}^{19}$','Interpreter','Latex')<br> box('on')<br> ylim([0.35 0.66])<br> xlim([-0.1 2.6])<br> legend1 = legend(legend_items');<br> set(legend1,'Location','southeast');<br> print(f1, 'WF_Pm_all','-dpng','-r300')</p> <p>%% average power end of farm<br> row_start = 16;<br> row_end = 19;<br> f2 = figure;<br> set(gcf,'paperposition',[0,0,8.4,4.9])<br> hold on</p> <p>for i = 1 : length(WF_cases_selected)<br> tmp_P = zeros(size(WF_data.shift{i}));<br> for j = 1:WF_cases_n(i)<br> tmp_P(j) = mean(mean( WF_data.WF_Pm{i}{j}(row_start:row_end,:)));<br> end<br> plot( WF_data.shift{i} , tmp_P, line_tick{i} ,'Color', line_color(i,:) ,'MarkerFaceColor', line_color(i,:) )<br> end</p> <p>% manualy plot errorbars<br> for i = 1:length(WF_cases_selected)<br> tmp_P = zeros(size(WF_data.shift{i}));<br> for j = 1:WF_cases_n(i)<br> tmp_P(j) = mean(mean( WF_data.WF_Pm{i}{j}(row_start:row_end,:)));<br> end<br> px = WF_data.shift{i} ;<br> py = tmp_P;<br> pw = 0.05;<br> pe = zeros(size(px))+0.02; %for uncertainty value see Bossuyt et al. (2018) Physical Review Fluids. <br> for j = 1:WF_cases_n(i)<br> plot( [px(j)-pw/2 px(j)+pw/2] , [py(j)+pe(j) py(j)+pe(j)],'-', 'Color', line_color(i,:),'LineWidth',0.5)<br> plot( [px(j)-pw/2 px(j)+pw/2] , [py(j)-pe(j) py(j)-pe(j)],'-', 'Color', line_color(i,:),'LineWidth',0.5)<br> plot( [px(j) px(j)],[py(j)-pe(j) py(j)+pe(j)],':', 'Color', line_color(i,:),'LineWidth',0.5)<br> end<br> end<br> xlabel('\Delta_y [D]')<br> ylabel('$\langle P_i /P_1\rangle_{16}^{19}$','Interpreter','Latex')<br> box('on')<br> ylim([0.27 0.52])<br> xlim([-0.1 2.6])<br> legend1 = legend(legend_items');<br> set(legend1,'Location','southeast');<br> print(f2, 'WF_Pm_end', '-dpng','-r300')</p> <p>%% plot average unsteady loading total farm<br> row_start = 1;<br> row_end = 19;<br> f3 = figure;<br> set(gcf,'paperposition',[0,0,8.4,4.9])<br> hold on<br> for i = 1 : length(WF_cases_selected)<br> tmp_TI = zeros(size(WF_data.shift{i}));<br> for j = 1:WF_cases_n(i)<br> tmp_TI(j) = mean(mean(WF_data.WF_U_rms{i}{j}(row_start:row_end,:)./WF_data.WF_Um{i}{j}(row_start:row_end,:)))*100;<br> end<br> plot( WF_data.shift{i} , tmp_TI , line_tick{i} ,'Color', line_color(i,:) ,'MarkerFaceColor', line_color(i,:))<br> end</p> <p>% manualy plot errorbars<br> for i = 1:length(WF_cases_selected)<br> tmp_TI = zeros(size(WF_data.shift{i}));<br> for j = 1:WF_cases_n(i)<br> tmp_TI(j) = mean(mean(WF_data.WF_U_rms{i}{j}(row_start:row_end,:)./WF_data.WF_Um{i}{j}(row_start:row_end,:)))*100;<br> end<br> px = WF_data.shift{i} ;<br> py = tmp_TI;<br> pw = 0.05;<br> pe = zeros(size(px))+ 0.004*tmp_TI;%for uncertainty value see Bossuyt et al. (2018) Physical Review Fluids. <br> for j = 1:WF_cases_n(i)<br> plot( [px(j)-pw/2 px(j)+pw/2] , [py(j)+pe(j) py(j)+pe(j)],'-', 'Color', line_color(i,:),'LineWidth',0.5)<br> plot( [px(j)-pw/2 px(j)+pw/2] , [py(j)-pe(j) py(j)-pe(j)],'-', 'Color', line_color(i,:),'LineWidth',0.5)<br> plot( [px(j) px(j)],[py(j)-pe(j) py(j)+pe(j)],':', 'Color', line_color(i,:),'LineWidth',0.5)<br> end<br> end<br> xlabel('\Delta_y [D]')<br> ylabel('$ \langle TI \rangle_{1}^{19} [\%]$','Interpreter','Latex')<br> box('on')<br> xlim([-0.1 2.6])<br> legend1 = legend(legend_items');<br> set(legend1,'Location','northeast');<br> print(f3, 'WF_TI_all','-dpng','-r300')</p> <p>%% plot average unsteady loading end of farm<br> row_start = 16;<br> row_end = 19;<br> f4 = figure;<br> set(gcf,'paperposition',[0,0,8.4,4.9])<br> hold on<br> for i = 1 : length(WF_cases_selected)<br> tmp_TI = zeros(size(WF_data.shift{i}));<br> for j = 1:WF_cases_n(i)<br> tmp_TI(j) = mean(mean(WF_data.WF_U_rms{i}{j}(row_start:row_end,:)./WF_data.WF_Um{i}{j}(row_start:row_end,:)))*100;<br> end<br> plot( WF_data.shift{i} , tmp_TI , line_tick{i} ,'Color', line_color(i,:) ,'MarkerFaceColor', line_color(i,:))<br> end</p> <p>% manualy plot errorbars<br> for i = 1:length(WF_cases_selected)<br> tmp_TI = zeros(size(WF_data.shift{i}));<br> for j = 1:WF_cases_n(i)<br> tmp_TI(j) = mean(mean(WF_data.WF_U_rms{i}{j}(row_start:row_end,:)./WF_data.WF_Um{i}{j}(row_start:row_end,:)))*100;<br> end<br> px = WF_data.shift{i} ;<br> py = tmp_TI;<br> pw = 0.05;<br> pe = zeros(size(px))+ 0.01*tmp_TI;%for uncertainty value see Bossuyt et al. (2018) Physical Review Fluids. <br> for j = 1:WF_cases_n(i)<br> plot( [px(j)-pw/2 px(j)+pw/2] , [py(j)+pe(j) py(j)+pe(j)],'-', 'Color', line_color(i,:),'LineWidth',0.5)<br> plot( [px(j)-pw/2 px(j)+pw/2] , [py(j)-pe(j) py(j)-pe(j)],'-', 'Color', line_color(i,:),'LineWidth',0.5)<br> plot( [px(j) px(j)],[py(j)-pe(j) py(j)+pe(j)],':', 'Color', line_color(i,:),'LineWidth',0.5)<br> end<br> end<br> xlabel('\Delta_y [D]')<br> ylabel('$ \langle TI \rangle_{16}^{19} [\%]$','Interpreter','Latex')<br> box('on')<br> xlim([-0.1 2.6])<br> legend1 = legend(legend_items');<br> set(legend1,'Location','northeast');<br> print(f4, 'WF_TI_end','-dpng','-r300')</p> <p> </p>
Turbulent kinetic energy over large wind farms observed and simulated by the mesoscale model WRF (3.8.1)
<p>This repository contains the WRF configuration files necessary to reproduce the simulations <br> as described in Siedersleben et al. 2019 (https://doi.org/10.5194/gmd-2019-100)</p> <p>The file windturbines_GMD.txt contains the locations of <br> all windturbines implemented in the simulations. The corresponding attributes of each <br> wind turbine type is described in the wind-turbine-xx.tbl. Be aware that all windturbines use the same power and thrust coefficients only the different hub heights and rotor diameters are taken into account as described in Siedersleben et al. (2019).</p> <p>The namelist.input_nameOfSimulation files necessary to run the simulations are provided in this repository as well. You may notice that <br> there are less namelist files than simulations. The simulations not using a TKE source use the same namelists as the ones with a TKE a source. However, the WRF model needs to be recompiled using the manipolated module_wind_fitch.F (you find this file in this repository). The sensitivity studies investigating the impact of the uncertainties in the power and thrust coefficients use the namelist of the control simulation CNTRb, but with manipulated wind-turbine-x_modMin/Max.tbl wind turbine files.</p> <p>The two python files get_era5*.py can be used to retrieve the ERA5 data, driving the WRF model. <br> Note that the dates and pathes have to be adjusted in the python files. <br> After downloading the surface and model level data some postprocessing <br> is necessary as described nicely here: "http://valcap74.blogspot.com/2017/10/how-to-run-wrf-model-driven-by-era5-on.html". For this<br> purpose the simple script called postProcessERA5 (based on the blog entry mentioned above) can be used.</p>
Supplementary material (part 2): "Evaluation of AROME Model Valley Wind Simulations in the Inn Valley, Austria"
<p><em>Part 2</em> of supplementary material for the Master's Thesis: "Evaluation of AROME Model Valley Wind Simulations in the Inn Valley, Austria" (Wibmer 2024, available <a href="https://resolver.obvsg.at/urn:nbn:at:at-ubi:1-151801">here</a>).</p> <p>Due to memory constraints, the supplementary material consists of two parts:</p> <ul> <li><em><strong>Part 1: </strong></em>(available <a href="https://doi.org/10.5281/zenodo.10849397" target="_blank" rel="noopener">here</a>) Includes Python scripts and model setup files, along with the first part of the datasets, including ERA-reanalysis data, observational data, and the preprocessed AROME model output (NetCDF files) of the <em>0.5-km</em> simulation.</li> <li><em><strong>Part 2: </strong></em>Includes the preprocessed AROME model output (NetCDF files) of the <em>1.0-km </em>and <em>2.5-km</em> simulations (see description below).</li> </ul> <p>To reproduce part of the figures, users must download the Python scripts and the preprocessed AROME model datasets (NetCDF files). <br>The Python scripts should be placed in the same parent folder because some of them depend on each other <strong>(!! Important !!).</strong><br>Original AROME model output files (GRIB2 format) are not published due to their large size.</p> <p>The naming convention for the AROME simulations uses OP* (where * represents the grid spacing in meters) to differentiate the model runs based on their horizontal<br>grid spacing:</p> <ul> <li><strong><em>OP2500:</em></strong> for 2.5 km</li> <li><em><strong>OP1000: </strong></em>for 1.0 km</li> <li><em><strong>OP500:</strong></em> for 0.5 km</li> </ul> <h3><strong>Datasets Part 2</strong></h3> <p>Due to memory constraints, the datasets needed for the analyses are split up into two parts. The second part of the supplementary material contains:</p> <ul> <li><strong>datasets_OP1000.tar.xz</strong>: Contains the preprocessed AROME-Aut model output data (NetCDF files) of the <em>1.0-km</em> simulation. </li> <li><strong>datasets_OP2500.tar.xz</strong>: Contains the preprocessed AROME-Aut model output data (NetCDF files) of the <em>2.5-km</em> simulation. </li> </ul> <p>The datasets of the<em> 0.5-km</em> simulation (<strong>datasets_OP500.tar.xz</strong>)<strong> </strong>can be found in Part 1 of the supplementary material (available <a href="https://doi.org/10.5281/zenodo.10849397" target="_blank" rel="noopener">here</a>).<br>The NetCDF datasets of the performed AROME-Aut simulations are packaged and compressed into <code><em><strong>.tar.xz</strong></em></code> files.<br>The Python scripts, available <a href="https://doi.org/10.5281/zenodo.10849397" target="_blank" rel="noopener">here</a>, require these NetCDF datasets for plotting and analyses routines.<br>The provided NetCDF datasets are preprocessed from the <em>GRIB2</em> output of the AROME-Aut simulations. <br>For the scripts to function properly, you need to adjust the path to the datasets within <code><strong>path_handling.py</strong></code><strong>.</strong></p> <p>Each <strong><code>datasets_OP*.tar.xz</code></strong> file contains NetCDF files for different type of levels: <em>surface, hybridPressure </em>(model levels)<em>, isobaricInhPa </em>(pressure levels),<em> meanSea </em>(mean sea level)<em>, heightAboveGround </em>(constant height levels)<em>. </em> The following naming convention for the datasets is used:</p> <ul> <li><strong>ds_OP*_<em>var</em>_hybridPressure_<em>[lon1, lon2, lat1, lat2]</em>.nc</strong>: Contains data on <em>hybrid pressure model levels</em> for a specific variable (<em>var</em>; e.g., <em>u, v, z, pres, q, t</em>) for the geographical extent defined in the brackets. </li> <li><strong>ds_OP*_interp_hybridPressure_<em>(lon,lat)</em>.nc</strong>: Combined dataset on <em>hybrid pressure model levels.</em> The data is bilinearly interpolated to the specified location <em>(lon, lat)</em>.</li> <li><strong>ds_OP*_<em>var</em>_surface_<em>whole</em>.nc</strong>: Contains data on <em>model surface </em>for a specified variable (<em>var</em>; e.g., <em>z, sp, t, tcc</em>) for the <em>whole </em>available domain extent.</li> <li><strong>ds_OP*_heightAboveGround_instant_<em>whole</em>.nc</strong>: Combined dataset on <em>height levels </em>(e.g., <em>2-m and 10-m</em>) for the <em>whole </em>available domain extent.</li> <li><strong>ds_OP*_<em>var</em>_meanSea_<em>whole</em>.nc</strong>: Contains data on <em>mean sea level</em> for specified variable (<em>var;</em> e.g., <em>prmsl</em>) for the <em>whole</em> available domain extent. </li> </ul> <p>The original GRIB2 files are not provided due to their large size. For further information about the GRIB2 files or the NetCDF datasets, please feel free to contact me.</p>
Tri-hourly dataset of wind and wave anomalies of the GFS and WAVEWATCH III models in the entire tropic region (TROPWA).
<p>This dataset contains the anomalies of the total height and peak period of the waves and of the zonal and meridional components of the wind at 10 m above the sea surface obtained from the outputs of the GFS and WAVEWATCH III coupled global models in the entire tropical region (180°W to 178.75°E longitude/30°S to 30°N latitude), with a spatial resolution of 1.25°x1°. It is made up of two files in NetCDF format, where the data for wind anomalies (speed module and its zonal and meridional components) and wave anomalies (total wave height and peak period) are contained separately. This dataset was created in order to study all types of tropical storms, cold fronts and other physical processes that influence significant changes in wave parameters, as well as for the design and construction of coastal engineering works. The authors thanks to Tropical Atlantic Interdisciplinary Laboratory on physical, biogeochemical, ecological and human dynamics (IJL TAPIOCA).</p>
Three-component modelling of O-rich AGB star winds I. Effects of drift using forsterite – dataset
<p>The data provided here include all parameter files, log files, and a set of the<br> binary output files that are the basis for the publication in A&A.</p> <p>The file 'file_listing.txt' contains a complete list of files and directories<br> in all gzipped tar files. Each individual gzipped tar file is formatted as<br> follows:</p> <p> Mm.m_Ll.ll_Ttttt.tar.gz</p> <p>where<br> m.m :: the assumed mass of the model, in solar masses<br> l.ll :: The assumed luminosity, in log10(solar luminosities)<br> tttt :: The effective temperature of the star, in Kelvin.</p> <p><br> The contents of the tar files vary according to the model, but here is the<br> general directory structure:</p> <p> nodr/ :: non-drift / PC models<br> drift/ :: drift models</p> <p> nodr/init<br> drift/init :: Initial model files created using John Connor.</p> <p><br> File suffixes are the following:</p> <p> .par :: Plain-text parameter file that contains all parameters that are<br> different from the respective default value in the model.<br> Consequently, to see what parameters were actually used, it is<br> necessary to look in the log file (see below).</p> <p> .bin :: Binary file that contains output of converged models. Each model is<br> stored in two versions, first the previous time step and then the<br> current time step (having access to the model code T-800, data of both<br> time steps are needed to restart model calculations at that time<br> step).</p> <p> The initial model file only contains one model; where the previous<br> time step data are the same as the current time step data.</p> <p> We provide a tool to read this file, see below.</p> <p> Note! These files can get pretty large and are therefore only<br> available for a smaller number of the models in the Zenodo dataset.<br> Please ask the corresponding author for the missing files is the<br> need should appear.</p> <p> .log :: Plain-text log file that shows the used model parameters and a number<br> of key properties for each converged model.<br> The encoding of this file is UTF-8.</p> <p> .inf :: Plain-text secondary log file that contains the header of the<br> [primary] log file as well as timing information.<br> The encoding of this file is UTF-8.</p> <p> .tpb :: Secondary binary file that contains a number of properties specified<br> at the outer boundary, typically for each consecutive time step.</p> <p> We provide a tool to read this file, see below.</p> <p> .lis :: Plain-text file with the iteration history. Unavailable here.</p> <p> .liv :: Plain-text file with values specified for a number of properties at<br> each gridpoint. Unavailable here.</p> <p> .inp :: Plain-text file that is used to launch a model; some are present.<br> This file is automatically generated by the tool that launches T-800<br> and is typically removed when T-800 launches. Unavailable here.</p> <p> .eps :: Encapsulated PostScript file created by John Connor when calculating<br> the initial model.</p> <p><br> Model evolution structure - file endings before the suffix:</p> <p> _rlx :: Files related to relaxing the T-800 calculations on the initial model<br> created by John Connor.</p> <p> _exp :: Files related to expanding the initially compact model to using the<br> full radial domain.</p> <p> _fix :: Files related to the intermediate stage where calculations are changed<br> from expansion to outflow.<br> <br> _out :: Files related to the outflow stage of the calculations; this is what<br> you want to look at to see the wind evolution. Results in the paper<br> are calculated using these data.</p> <p> Note! Some outflow stage calculations continue the evolution of the previous<br> set of files. The underlying reason for continued calculations is typically<br> that the calculated time interval is too short. Such files are typically<br> given the extension '_cont.lin_out', '_cont2.lin_out', etc.</p> <p><br> Stored data in the binary files:</p> <p> The binary files (suffix '.bin') contain the full radial structure in the<br> following 10 (PC models) or 11 (drift models) primary variables:</p> <p> mr: radius<br> mm: integrated [gas] mass<br> md: gas density<br> mu: gas velocity<br> me: internal energy<br> mj: radiative energy<br> mh: radiative flux<br> n0: dust moment, forsterite (Fo)<br> nm: number density of magnesium atoms<br> ns: number density of silicon atoms<br> v0: dust velocity, forsterite (only drift models)</p> <p> Other properties are derived from these primary variables using auxiliary code<br> that isn't part of this dataset.</p> <p><br> Load files:</p> <p> Two tools are provided here that can load the binary data files using the<br> Interactive Data Language (IDL):</p> <p> sc_load_bin (for files with the suffix '.bin'):</p> <p> Loads the full content of a T-800 binary file and returns a structure<br> with the data.</p> <p><br> sc_load_tpb (for files with the suffix '.tpb'):</p> <p> Loads the full content of a T-800 'tpb' binary file and returns a<br> structure with the data.</p> <p> Note! Due to the way models run on clusters, this file is sometimes<br> incomplete; this happens when the model code T-800 is stopped as the<br> cluster-specific walltime is reached. If this is the case, it is<br> necessary to use the binary file instead, where data are saved<br> typically every 20:th time step.</p> <p> Alternative tools for use with Python and Julia could be considered for<br> writing, but where not yet available when this dataset was made public.<br> Please contact the corresponding author for a current status on this issue.</p> <p> </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>
ScienceDex guides
Understand access before you commit
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.