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17 results for “Wind Loads”
High-frequency dissolved oxygen, water temperature, wind speed, and radiation data; stream and in-lake nutrient concentration data; and daily metabolism and nutrient loading estimates for 16 lakes in North America and Northern Europe.
In lakes, ecosystem structure and processes are influenced by gross primary production (GPP), ecosystem respiration (R), and net ecosystem production (NEP). The rates of these metabolic processes are often controlled by resource availability, which often reflects catchment loads. Although the relationship between catchment loads and in-lake nutrient concentrations may be well defined in specific lakes, we explored how watershed vs. in-lake predictors of metabolism compare across lake types. To do this, we combined stream loads of carbon (C), nitrogen (N), and phosphorus (P) with high frequency in situ monitoring of lake metabolism and in-lake C, N, and P concentrations from 16 lakes spanning a range of latitudes (39 to 64 degrees N), inflowing stream (0 - 6 streams), and trophic status (oligotrophic to eutrophic). The data package includes high-frequency dissolved oxygen, water temperature, wind speed, and solar radiation data as well as daily estimates of GPP, R, and NEP derived from those data. In addition, the data package includes in-lake and stream concentrations of dissolved organic carbon, total nitrogen, and total phosphorus and stream discharge data. The package also includes estimates of daily carbon, nitrogen and phosphorus loading to each lake derived from the stream concentrations and discharge.
Historical Weather, Load, Wind, and Solar Data for the Salt River Project
<p>We created and curated a dataset of historical (1980-2019) hourly meteorology, load, wind, and solar data for the Salt River Project (SRP) region. The data was created by PNNL's <a href="https://godeeep.pnnl.gov/">GODEEEP</a> project. Each row in the dataset is a single hour and each column is a variable. All meteorological variables are spatially-averaged over the SRP service territory. The variables and their units are as follows:</p><ol><li>"Time_UTC"; Coordinated Universal Time (UTC); Time of day.</li><li>"T2"; Fahrenheit; 2-m air temperature.</li><li>"Q2"; kg/kg; 2-m water vapor mixing ratio.</li><li>"SWDOWN"; W/m^2; Downwelling shortwave radiative flux at the surface.</li><li>"GLW"; W/m^2; Downwelling longwave radiative flux at the surface.</li><li>"WSPD"; m/s; 10-m wind speed.</li><li>"Scaled_2019_Load"; MWh; Simulated hourly demand for electricity that is scaled to 2019 levels of annual energy. This load estimate does not account for historical changes in population and economics within the SRP service territory. It is included to make it easier to isolate weather impacts on load without having to consider long-term changes.</li><li>"Load"; MWh; Simulated hourly demand for electricity.</li><li>"Agua_Fria_Solar_Capacity"; N/A; Solar capacity factor for the SRP Agua Fria project with plant configurations taken from the EIA-860 database.</li><li>"Phoenix_Solar_Capacity"; N/A; Solar capacity factor for hypothetical solar plants derived using the grid cell nearest to Phoenix, AZ.</li><li>"Flagstaff_Solar_Capacity"; N/A; Solar capacity factor for hypothetical solar plants derived using the grid cell nearest to Flagstaff, AZ.</li><li>"Phoenix_Wind_Capacity"; N/A; Wind capacity factor for hypothetical 80-m plants derived using the grid cell nearest to Phoenix, AZ.</li><li>"Flagstaff_Wind_Capacity"; N/A; Wind capacity factor for hypothetical 80-m plants derived using the grid cell nearest to Flagstaff, AZ.</li></ol>
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>
Stiffness of randomly sampled stainless steel frames under gravity and gravity plus wind load scenarios
<p>Data was generated using the general purpose finite element software ABAQUS and performing advanced nonlinear analyses. The database is comprised of vertical and lateral system stiffness values corresponding to different random samples of six different nominal stainless steel frames under gravity and gravity plus wind load combinations. The values of the random variable assignments are given for each case. </p> <p>The full details of the finite element model can be found in: Arrayago, I.; Rasmussen, K.J.R. Reliability of stainless steel frames designed using the Direct Design Method in serviceability limit states. Journal of Constructional Steel Research 196, 107425, 2022. DOI: https://doi.org/10.1016/j.jcsr.2022.107425</p> <p>The data included in the dataset corresponds to the vertical & lateral stiffness of each frame under different load conditions.</p> <p>Although the data has been generated using the finite element software ABAQUS, no special software is required to read or interpret the data.</p>
Figures and data: Combining wake redirection and derating strategies in a load-constrained wind farm power maximization
<p><strong>Figures from the publication <em>Combining wake redirection and derating strategies in a load-constrained wind farm power maximization.</em></strong></p> <p> </p> <p>*.fig files can be opened in <code>Matlab</code></p> <p>*.csv files can be opened through a standard text editor (e.g.,<code> Notepad++</code>), imported and visualized in <code>Matlab</code> through the functions <code>>>readmatrix()</code> and <code>>>readtable()</code></p>
Data supplement for Wind Energy Science Paper 'Implementation of the blade element momentum model on a polar grid and its aeroelastic load impact'
<p>Contains the data for most figures in the article, as well as a plotting file written in python that generates the figures.</p>
Experimental and numerical data of an article on the prediction of wind load on pylons for a neutral atmospheric boundary layer ow over two successive hills
<p>his deposit contains experimental and numerical data related to the article entitled "Towards prediction of wind load on pylons for a neutral atmospheric boundary layer flow over two successive hills" that will be publised in Journal of Wind Engineering & Industrial Aerodynamics. It consists in post-processed experimental data (turbulence statistics) for flows around 2D hills, measured by PIV, and numerical date of k-epsilon simulations of the same configirations. All data are provided in matlab metadata format. Matlab functions are given to plot all the figures of the article.</p>
Ultimate load of randomly sampled stainless steel frames under gravity plus wind loads
<p>Data was generated using the general purpose finite element software ABAQUS and performing advanced nonlinear analyses. The database is comprised of ultimate load factors corresponding to different random samples of six different nominal stainless steel frames under gravity and wind load combinations. The values of the random variable assignments are given for each case.</p> <p>The full details of the finite element model can be found in: Arrayago, I.; Rasmussen, K.J.R.; Zhang, H. System-based reliability analysis of stainless steel frames subjected to wind loads. "Structural Safety", July 2022, vol. 97, art. No. 102211.</p> <p>DOI: https://doi.org/10.1016/j.strusafe.2022.102211</p>
Data-driven surrogate model for wind turbine damage equivalent load
<p>There are four zip files in this data set:</p> <ul> <li>PythonCode_OpenFAST: The code used to generate 32768 OpenFAST fst files to build the database.</li> <li>ML_TrainingCode: The code that used to train the TCN-FCNN and FCNN models for both free stream and wake</li> <li>Trained_Models: All the trained models are saved in Keras format. The models with max in their filenames were trained on maximum values. The models with XY in their naming were trained on wind in the X and Y directions.</li> <li>data: It includes all the CSV files for training and testing.</li> </ul>
Multidirectional load multipliers: application for shared anchors for floating wind turbines
<div> <h1>Multidirectional load multipliers</h1> </div> <p>The load multipliers presented in this dataset are based on the multiline anchor loads calculated for a spar floating wind turbine presented by:</p> <p>Balakrishnan, K., Arwade, S.R., DeGroot, D.J., Fontana, C., Landon, M., Aubeny, C.P., 2020. <em>Comparison of multiline anchors for offshore wind turbines with spar and with semisubmersible</em>. Journal of Physics: Conference Series, <strong>1452</strong>, 012032. <a href="https://doi.org/10.1088/1742-6596/1452/1/012032" rel="nofollow">doi:10.1088/1742-6596/1452/1/012032</a>.</p> <div> <h2>Files</h2> </div> <p>Load multipliers for three load cases grouped in Wind Wave and Current directions. Those are the loads arriving at a multiline anchor.</p> <div> <h3>DLC 0WWC: Wind Wave and Current at 0 degrees</h3> </div> <ul> <li>DLC_0deg_push.txt</li> <li>DLC_0deg_push2.txt</li> <li>DLC_0deg_push3.txt</li> <li>DLC_0deg_step.txt</li> <li>DLC_0deg_step2.txt</li> <li>DLC_0deg_step3.txt</li> </ul> <div> <h3>DLC 30WWC: Wind Wave and Current at 30 degrees</h3> </div> <ul> <li>DLC_30deg_push.txt</li> <li>DLC_30deg_push2.txt</li> <li>DLC_30deg_push3.txt</li> <li>DLC_30deg_step.txt</li> <li>DLC_30deg_step2.txt</li> <li>DLC_30deg_step3.txt</li> </ul> <div> <h3>DLC 60WWC: Wind Wave and Current at 60 degrees</h3> </div> <ul> <li>DLC_60deg_push.txt</li> <li>DLC_60deg_push2.txt</li> <li>DLC_60deg_push3.txt</li> <li>DLC_60deg_step.txt</li> <li>DLC_60deg_step2.txt</li> <li>DLC_60deg_step3.txt</li> </ul>
Dataset and Code for "Bayesian spline method for assessing extreme loads on wind turbines"
<p>Here are two files. One file contains the datasets and the other contains the computer code used to generate the results in the paper, Lee, Byon, Ntaimo, and Ding, 2013, “Bayesian spline method for assessing extreme loads on wind turbines,” <em> Annals of Applied Statistics</em>, Vol. 7, pp. 2034-2061.</p>
Database of Short Term Damage Equivalent Loads (DEL) of IWT7.5MW wind turbine depending on wind, TI, yaw and derating
<p>Two datasets for short term damage equivalent loads (DEL) and energy production of the IWT7.5 MW research wind turbine (https://doi.org/10.24406/IWES-N-518562) are provided. dataset_Derating is a subset of dataSet_Derating_withYaw.</p> <p>The damage equivalent loads are computed with the rainflow counting algorithm applied to time series loads. They are based on Miner's rule. Further details can be found in: <br> Requate, N., Meyer, T., and Hofmann, R.: From wind conditions to operational strategy: Optimal planning of wind turbine damage progression over its lifetime, Wind Energ. Sci., accepted for publication, 2023. </p> <p>In the paper, the dataset_Derating was used to create surrogate models.</p> <p>A fullfactorial approach to simulate different parameter-combination was applied. <br> Information about the signals and units is provided in the yml files.<br> The range of parameters is given by python code in the yml file. <br> For each set of parameters, 6 different 10 minute-realizations of a turbulent wind field ("seeds") were created. The turbine was then simulated separately for each turbulent wind field.<br> Each folder contains the short term DELs for each 10 minute simulation and the aggregated short term DELs over all 6 wind realizations.</p> <p>Outliers from simulation error were removed.<br> The data was originally intended to create surrogate models of the variation of outputs values throughout the parameter space. Further usage is possible and encouraged.</p>
Hourly wind speed, solar radiation and load demand data
<p>Hourly data for wind velocity, solar radiation and load demand as time series of 10 years length, used within the simulation of a hybrid renewable energy system in the island of Sifnos, Greece. </p>
Site-specific Design Load Cases for floating offshore wind turbine applications I : Historical data
<p>This document includes a brief description of the <a href="https://leopard.tu-braunschweig.de/receive/dbbs_mods_00077703" target="_blank" rel="noopener">first database</a> on the site-specific Design Load Cases (DLCs) based on historical metocean data. The dataset includes metocean data, statistical analysis and site-specific DLCs across the three areas of study defined in the INF4INiTY project: Scottish Sea, Baltic Sea and Adriatic Sea. In addition to the dataset, this deliverable includes a Graphical User Interface (GUI) for the analysis of specific locations within these three areas and the generation of the site-specific DLCs.<br>The aim of this initial version of the database is to provide a first characterisation of the areas of interest in order to use the DLCs on the design of the different innovations planned in various work packages (WPs) INF4INiTY. As the project proceeds, the second database will extend the site-specific DLCs including forecasted data for different horizons and under diverse climate change scenarios.<br>The deliverable is divided into six brief sections describing the (i) the GUI, (ii) characteristics of the data, (iii) the three areas of study and technological requirements, (iv) historical metocean data, (v) site-specific statistical analysis and reporting, and (vi) site-specific DLCs.</p>
Real-Time Wind Response Prediction and Load Inversion for Skyscraper Using Deep Learning
<p>Dataset and code for a forthcoming article. Any questions, please consult 2110051@tongji.edu.cn</p>
Resource and Load Compatibility Assessment of Wind Energy Offshore of Humboldt County, California: Data and Software
<p>These files contain the raw data and code used to analyzed wind resource and local load compatibility of offshore wind in Humboldt, California.</p>
The fatigue damage evolution in the load-carrying composite laminates of wind turbine blades
<p>Video and data-set behind the figures used in the book chapter:</p> <p>Mikkelsen, LP, The fatigue Damage evolution in the load-carrying composite laminates of wind turbine blades, chapter 16 in “Fatigue life prediction of composites and composite structures”, Edited by A.P. Vassilopoulos, Second edition, Elsevier Ltd, To be published in 2019. <a href="https://doi.org/10.1016/B978-0-08-102575-8.00016-4">https://doi.org/10.1016/B978-0-08-102575-8.00016-4</a>.</p> <p>The files are organised based on the first characters in the filename with fist two numbers and than a "a" for video and "b,c.." for data behind that video. The video have the following link to the figures in the book chapter:</p> <p>Figure 16.19: 03a-Eps05-2-Zoom.mp4 , <a href="https://youtu.be/PynQ0IEyne8">https://youtu.be/PynQ0IEyne8</a><br> Figure 16.21A: 01a-2Dprojection.avi , <a href="https://youtu.be/sr1J_5oc2EE">https://youtu.be/sr1J_5oc2EE</a><br> Figure 16.21B: 02a-Video 3D 360.mpg , <a href="https://youtu.be/riqtI6fARko">https://youtu.be/riqtI6fARko</a><br> Figure 16.25: 11a-Eps05-2.mp4 , <a href="https://youtu.be/RJwfpiiLws0">https://youtu.be/RJwfpiiLws0</a><br> Figure 16.28A: 21a-LFoVexsitu.mp4 , <a href="https://youtu.be/PmUWAI9qfMQ">https://youtu.be/PmUWAI9qfMQ</a><br> Figure 16.28B: 22a-SFoV1G.mp4 ,<a href="https://youtu.be/sFBgVyF1Bfw"> https://youtu.be/sFBgVyF1Bfw</a><br> Figure 16.29: 23a-SFoVGreen.mp4 ,<a href="https://youtu.be/sFBgVyF1Bfw"> </a><a href="https://youtu.be/wJMV9bP5ntw">https://youtu.be/wJMV9bP5ntw</a><br> Figure 16.30: 24a-SFoVRed.mp4 ,<a href="https://youtu.be/sFBgVyF1Bfw"> </a><a href="https://youtu.be/9m1Q0MrzJrY">https://youtu.be/9m1Q0MrzJrY</a><br> Figure 16.31: 25a-RotatingSegmentedDamage.mpg ,<a href="https://youtu.be/sFBgVyF1Bfw"> </a><a href="https://youtu.be/OHPkwwaTUSM">https://youtu.be/OHPkwwaTUSM</a><br> Figure 16.34: 31a-Stained.mpg ,<a href="https://youtu.be/sFBgVyF1Bfw"> </a><a href="https://youtu.be/Sddg0sbzw6w">https://youtu.be/Sddg0sbzw6w</a><br> Figure 16.35: 41a-RLmovie.mpg ,<a href="https://youtu.be/sFBgVyF1Bfw"> </a><a href="https://youtu.be/H7aZrIYjKeQ">https://youtu.be/H7aZrIYjKeQ</a><br> Figure 16.36A: 51a-SFoV-Tension-Compression-T.mpg ,<a href="https://youtu.be/AdsjDkq8QE8"> https://youtu.be/AdsjDkq8QE8</a><br> Figure 16.36B: 52a-SFoV-Tension-Compression-W.mpg , <a href="https://youtu.be/ddSqQnW8jMo">https://youtu.be/ddSqQnW8jMo</a></p>
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