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56 results for “Offshore wind”
Intelligent Physical Exercise Training (IPET) in the Offshore Wind Industry: A Feasibility Study
ClinicalTrials.gov study NCT04995718. IPD Sharing: NO. Countries: 1. Publications: 1.
Data from: Three dimensional tracking of a wide-ranging marine predator: flight heights and vulnerability to offshore wind farms
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Data from: The design of an intelligent fault-tolerant control for floating offshore wind turbine with blade faults
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Data from: Supportive wind conditions influence offshore movements of Atlantic Coast piping plovers (Charadrius melodus melodus) during fall migration
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Data from: Sound exposure in harbour seals during the installation of an offshore wind farm: predictions of auditory damage
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Data from: A global review of Procellariiform flight height, flight speed and nocturnal activity: Implications for offshore wind farm collision risk
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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>
Supplementary data (Paradox lens on sustainable offshore wind development – A systematic literature review)
<p>Supplementary data for research article.</p>
Inland-Offshore Wind Farm Dataset2
<p>The wind turbine data in these two datasets include observations during the first four years of the turbines' operations. They are 10-minute data. The inland turbine data are from 2008 to 2011, whereas the offshore data are from 2007 to 2010. The measurements for the inland wind farm include the same x's as in the <a href="https://zenodo.org/record/5516552">Inland Wind Farm Dataset1</a> and those for the offshore wind farm include the same x's as in the <a href="https://zenodo.org/record/5516552">Offshore Wind Farm Dataset1</a>. Most of the environmental measurements are taken from the met mast closest to the turbine, with the exception of wind speed and turbulence intensity which are measured on the wind turbine. The mast measurements are used either because some variables are only measured at the mast (such as air pressure and ambient temperature, which are used to calculate air density) or because the mast measurements are considered more reliable (such as wind direction).</p>
Inland-Offshore Wind Farm Dataset1
<p>Data included in these two datasets are 10-minute data generated from six wind turbines and three met masts and are arranged in six files, each of which is associated with a turbine. The six turbines are named WT1 through WT6, respectively. The layout of the turbines and the met masts is shown in Fig. 5.6 of the <a href="https://aml.engr.tamu.edu/book-dswe/">Data Science for Wind Energy</a> book. On the offshore wind farm, all seven environmental variables as mentioned above are available, namely x =(V, D, rho, H, I, Sa, Sb), whereas on the inland wind farm, the humidity measurements are not available, nor is the above-hub wind shear, meaning that x =(V, D, rho, I, Sb). Variables in x were measured by sensors on the met mast, whereas y was measured at the wind turbines. Each met mast has two wind turbines associated with it, meaning that the x's measured at a met mast are paired with the y's of two associated turbines. For WT1 and WT2, the data were collected from July 30, 2010 through July 31, 2011 and for WT3 and WT4, the data were collected from April 29, 2010 through April 30, 2011. For WT5 and WT6, the data were collected from January 1, 2009 through December 31, 2009.</p> <p>Meaning of variables; V: wind speed; D: wind direction; rho: air density; H: humidity; I: turbulence intensity; S: vertical wind shear; Sa: above-hub height wind shear, Sb: below-hub height wind shear.</p>
Towed chain datasets and input files for simulations used in the manuscript "Increased mixing and turbulence in the wake of offshore wind farm foundations"
<p><strong>Contents</strong></p> <p>1. File S01_S12 Input files for simulations (precursor runs and main runs)</p> <p>2. File S13 Topography file for simulations with monopile</p> <p>2. Data sets ds01 to ds06 (towed chain data collected in May 25, 2015)</p> <p>3. Data sets ds07 to ds14 (towed chain data collected in July 19, 2017)</p> <p>4. Data sets d15 to ds16 (ADCP data collected in May 25, 2015 and July 19, 2017)</p> <p><strong>Introduction </strong></p> <p>This package contains the input parameters used in each of the precursor (S01 - S04) and main runs (S05 - S12) presented in the manuscript “Increased mixing and turbulence in the wake of offshore wind farm foundations”. These input files are found in the PDF file "S01_S12".</p> <p>The main runs, in which the wake of a monopile was simulated (S05, S07, S09, S11), require a topography file, which is a NETCDF-file that has been uploaded separately (S13). All simulations were run using the Parallelized Large-Eddy Simulation Model for atmospheric and oceanic flows (PALM, version 4.0, revision 2504).</p> <p>Further, this package contains the data sets collected using the towed chain in 2015 (ds01-ds06, ds15) and 2017 (ds07-ds14, ds16), which have been uploaded as separate NETCDF-files.</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>
Figures and graphics used in "Optimizing Strength of Shared Anchors in an Array of Floating Offshore Wind Turbines"
<p>All figures and graphics used in tables are included in PDF format here. Instructions on how to reproduce the MATLAB-generated plots (or similar plots) are included in the README of the software package, also cited in the paper.</p>
Skillful bias correction of offshore near-surface wind speed and wind direction forecasting based on a multi-task machine learning model
<h3>Dataset</h3> <p>1. observation data over 14 weather stations</p> <p>Variables: hourly near-surface 2-min average wind speed, wind direction </p> <p>2. ECMWF-IFS forecast data over 14 weather stations</p> <p>Variables: hourly predictors at surface level and upper level in next 48 hours (shown in Table 1. and Table 2.)</p> <p>Table 1. ECMWF-IFS forecast data at surface level</p> <div> <table> <tbody> <tr> <td> <p>Predictors</p> </td> <td> <p>Abbreviation</p> </td> <td> <p>Unit</p> </td> </tr> <tr> <td> <p>Temperature at 2 m</p> </td> <td> <p>2t</p> </td> <td> <p>℃</p> </td> </tr> <tr> <td> <p>Sea surface temperature</p> </td> <td> <p>sst</p> </td> <td> <p>℃</p> </td> </tr> <tr> <td> <p>Dewpoint temperature at 2 m</p> </td> <td> <p>2d</p> </td> <td> <p>℃</p> </td> </tr> <tr> <td> <p>Convective precipitation in the past hour</p> </td> <td> <p>cp</p> </td> <td> <p>mm</p> </td> </tr> <tr> <td> <p>Mean sea level pressure</p> </td> <td> <p>msl</p> </td> <td> <p>hPa</p> </td> </tr> <tr> <td> <p>Zonal component of wind speed at 10 m</p> </td> <td> <p>10u</p> </td> <td> <p>m s<sup>-1</sup></p> </td> </tr> <tr> <td> <p>Meridional component of wind speed at 10 m</p> </td> <td> <p>10v</p> </td> <td> <p>m s<sup>-1</sup></p> </td> </tr> <tr> <td> <p>Wind speed at 10 m</p> </td> <td> <p>10ws</p> </td> <td> <p>m s<sup>-1</sup></p> </td> </tr> <tr> <td> <p>Wind direction at 10 m</p> </td> <td> <p>10wd</p> </td> <td> <p>°</p> </td> </tr> <tr> <td> <p>Zonal component of wind speed at 100 m</p> </td> <td> <p>100u</p> </td> <td> <p>m s<sup>-1</sup></p> </td> </tr> <tr> <td> <p>Meridional component of wind speed at 100 m</p> </td> <td> <p>100v</p> </td> <td> <p>m s<sup>-1</sup></p> </td> </tr> <tr> <td> <p>Wind speed at 100 m</p> </td> <td> <p>100ws</p> </td> <td> <p>m s<sup>-1</sup></p> </td> </tr> <tr> <td> <p>Wind direction at 100 m</p> </td> <td> <p>100wd</p> </td> <td> <p>°</p> </td> </tr> </tbody> </table> </div> <div> </div> <p>Table 2. ECMWF-IFS forecast data at upper level</p> <table> <tbody> <tr> <td> <p>Predictors</p> </td> <td> <p>Abbreviation</p> </td> <td> <p>Unit</p> </td> </tr> <tr> <td> <p>Relative humidity at xxx hPa</p> </td> <td> <p>r_Lxxx</p> </td> <td> <p>%</p> </td> </tr> <tr> <td> <p>Temperature at xxx hPa</p> </td> <td> <p>t_Lxxx</p> </td> <td> <p>℃</p> </td> </tr> <tr> <td> <p>Vertical velocity of wind at xxx hPa</p> </td> <td> <p>w_Lxxx</p> </td> <td> <p>Pa s<sup>-1</sup></p> </td> </tr> <tr> <td> <p>Zonal component of wind at xxx hPa</p> </td> <td> <p>u_Lxxx</p> </td> <td> <p>m s<sup>-1</sup></p> </td> </tr> <tr> <td> <p>Meridional component of wind at xxx hPa</p> </td> <td> <p>v_Lxxx</p> </td> <td> <p>m s<sup>-1</sup></p> </td> </tr> <tr> <td> <p>Wind speed at xxx hPa</p> </td> <td> <p>ws_Lxxx</p> </td> <td> <p>m s<sup>-1</sup></p> </td> </tr> <tr> <td> <p>Wind direction at xxx hPa</p> </td> <td> <p>wd_Lxxx</p> </td> <td> <p>°</p> </td> </tr> </tbody> </table> <div> </div> <p>3. key variables constructed by feature engineering</p> <p>(1) sort-term statistics, including <em>maximum, minimum, mean </em>and <em>variance</em> of key variables (<em>2t</em>,<em> 10u</em>, <em>10v </em>and <em>10ws</em>) from ECMWF-IFS model during the next 48 hours,</p> <p> (2) long-term statistics, including <em>mean </em>and <em>deviation</em> of key variables (<em>2t</em>,<em> 10u</em>, <em>10v </em>and <em>10ws</em>) from ECMWF-IFS model during history 3-yr period (January 2020–December 2022),</p> <p> (3) thermodynamic factors, including the low-level wind shear between <em>10ws</em> and <em>100ws</em>, vertical wind shear between 200 hPa and 850 hPa<em>, </em>the differences between <em>sst</em><em> </em>and <em>2t</em><em>.</em></p> <h3>Scripts</h3> <p>1. Random Forest model training code</p> <p>2. LightGBM model training code</p> <p>3. XGBoost model training code</p> <p>4. TabNet-MTL model training code</p> <p> </p>
Interview data for 'Risks in the offshore wind supply chain and tendering process impacts: Insights from industry expert elicitations'
<p>Updated 3 category labels to reduce potential for confusion. (v3)</p> <p>Interview data with restored functionality of some unused data analysis methods. (v2)</p> <p>Original upload. (v1)</p>
Calculations - Financing marine restoration through offshore wind investments
<p>Calculations - Financing marine restoration through offshore wind investments</p>
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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.