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95 results for “Wind energy”
Wind energy taxonomies and restricted vocabularies
<p>This is the update version of the wind energy taxonomies and restricted vocabularies which has been implemented in DTU Data (https://data.dtu.dk/DTU_Wind_Energy) with the purpose of accurately describing published data sets and data collections. The work on updating and implementing the wind energy taxonomies and restricted vocabularies has been done as a part of an internally funded 'FAIR digitalization' project of DTU Wind Energy(see 10.5281/zenodo.1493874). The work on updating the taxonomies and restricted vocabularies is a continuation of the work previously done under the IRPWind Open Data initiative (see 10.5281/zenodo.1199489). </p>
SparBOFWEC Spar Buoy for Offshore Floating Wind Energy Conversion - Data Storage Report
<p>The present work describes the experiences gained from the design methodology and operation of a 3D physical model experiment aimed to investigate the dynamic behaviour of a spar buoy (SB) off-shore floating wind turbine (WT) under different wind and wave conditions. The physical model tests have been performed at Danish Hydraulic Institute (DHI) off-shore wave basin within the European Union-Hydralab+ Initiative, in April 2019. The floating WT model has been subjected to a combination of regular and irregular wave attacks and wind loads.</p>
Virtual sensors for wind energy applications benchmark study data - preliminary version
<p>Test version of the time series data for the wind energy virtual sensing benchmark study data.</p>
Figures 2–5 in One size doesn′t fit all: Singularities in bat species richness and activity patterns in wind-energy complexes in Brazil and implications for environmental assessment
Figures 2–5. Bat activity based on the pooled number of echolocation pulses per hour in four wind-energy complexes in northeastern Brazil, from September 2015 to January 2017: (2) Curva dos Ventos, municipality of Caetité, state of Bahia; (3) Cristal, municipality of Morro do Chapéu, state of Bahia; (4) Modelo, municipality of João Câmara, state of Rio Grande do Norte; (5) Fonte dos Ventos, municipality of Tacaratu, state of Pernambuco.
Figure 1 in One size doesn′t fit all: Singularities in bat species richness and activity patterns in wind-energy complexes in Brazil and implications for environmental assessment
Figure 1. Wind-energy complexes in northeastern Brazil studied for the presence and activity of insectivorous bats from September 2015 to January 2017.
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>
Constraining an eddy energy dissipation rate due to relative wind stress for use in energy budget-based eddy parameterisations
<p>Code and data to reproduce results in the EGU Ocean Science journal paper, entitled 'Constraining an eddy energy dissipation rate due to relative wind stress for use in energy budget-based eddy parameterisations'.</p> <p>The data and corresponding scripts in this repository are:</p> <ul> <li>MITgcm simulation data for an anticyclone and cyclone under absolute and relative wind stress. <ul> <li>ACE_eta_{absolute or relative}_10km_A25_v2_ext.nc</li> <li>ACE_tau_{absolute or relative}_10km_A25_v2_ext.nc</li> <li>ACE_temp_{absolute or relative}_10km_A25_v2_ext.nc</li> <li>CE_eta_{absolute or relative}_10km_A25_v2.nc</li> <li>CE_tau_{absolute or relative}_10km_A25_v2.nc</li> <li>CE_temp_{absolute or relative}_10km_A25_v2.nc</li> </ul> </li> <li>Mean energetics computed using eddy_energy_mean.m. User may need to comment out lines of code relating to wvel. This also requires geostrophic_uv.m, rho_ref.mat, and ocean_vertical_grid.nc. <ul> <li>{ACE or CE}_energy_{absolute or relative}_total_10km_A25_mean.nc</li> </ul> </li> <li>Predicted eddy energy computed using predict_*.m scripts. n.b. viscous term left in absolute scripts, though we only need the first index of total eddy energy in abs cases. The abs cases are really just making the abs.mat files, see time_series_plots.py.</li> <li>Time-series figures (3 and 5) produced using time_series_plots.py.</li> <li>Initial figures (1 and 2) produced using initial_plots.py.</li> <li>Relative vorticity in Fig. 4 computed and plotted using eddy_evolution_plot.py.</li> <li>Dissipation rates computed in calc_dissipation_rate.m. Requires dynmodes.m and GSW toolbox to run. Also needs data Chelton2011_Le.csv and Chelton2011_Rd.csv. <ul> <li>diss_rate*.nc</li> </ul> </li> <li>Dissipation rate and climatology figures (6, 7, 8, and 9) made in diss_rate_plot.py</li> </ul> <p>Information and data needed to run the MITgcm can be found <a href="https://github.com/thomaswilder/jpo_eddy-scripts">here</a>.</p>
Data for Potential feeding sites for seabirds and marine mammals reveal large conflicting areas with offshore wind energy development worldwide
<p>This dataset contains;</p> <p>1) The dataset (sample_point_data.Rdata) to develop the Structural Equation model </p> <p>2) The spatial tiff bivariate maps for small-ranged seabird and marine mammals and fish and zooplankton biomass respectively</p> <p>3) The Potential Feeding Sites likelihood map in a tiff format</p> <p>4) The global power density at 200m </p> <p>5) Dataset of risk category and corresponding values and coordinates for spatial representation</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>
Data for Wind Energy Science paper "Modal dynamics of structures with bladed isotropic rotors and its complexity for 2-bladed rotors"
<p>The files are data files with model input and Matlab files with model parameters and function that sets up the block matrices of the dynamic model used in the paper. The Matlab script "test_repo.m" shows how to this function with all input.</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>
Data and results related to "Fattori et al. 2017 - High Solar Photovoltaic Penetration in the Absence of Substantial Wind Capacity: Storage Requirements and Effects on Capacity Adequacy - Energy"
<p>The file includes data used for the analysis and results coming from the study (which was focused on the Italian "Nord" bidding zone). In particular:</p> <p>(i) Series of hourly load data [MW], from 01.01.2006 to 31.12.2015. The data come from elaborations based on ENTSO-E (https://www.entsoe.eu/db-query/country-packages/production-consumption-exchange-package) and Terna S.p.A. (http://www.terna.it/en-gb/sistemaelettrico/transparencyreport/load/actualload.aspx). All the elaborations are described in details on the paper.</p> <p>(ii) Data related to the penetration of PV. Installed capacity of PV is assumed to increase from zero up to the capacity needed so that the average annual PV generation (based on the years 1986-2015) potentially equals the average annual demand (based on the years 2006-2015).</p> <p>(iii) Synthesis of the results about: residual load (with and w/o storage), ramps (with and w/o storage), excess energy (with and w/o storage), storage requirements</p>
Wind energy development can lead to guild-specific habitat loss in boreal forest bats
<p>Forest management rarely considers protecting bats in Fennoscandian regions although all species rely on forest habitat at some point in their annual cycle. This issue is especially evident as wind parks have increasingly been developed inside Fennoscandian forests, against the advice of international bat conservation guidelines. In this study, we aimed to describe and explain bat community dynamics at a Norwegian wind park located in a boreal forest, especially to understand potential avoidance or attraction effects. The bat community was sampled acoustically and described using foraging guilds (short, medium, and long-range echolocators; SRE, MRE, LRE) as well as behavior (commuting, feeding and social calls). Sampling was undertaken at two locations per turbine: (i) the turbine pad and (ii) a paired natural habitat at ground level, as well as from a meteorological tower. We used a recently developed method for camera trapping nocturnal flying insects synchronously with bat acoustic activity. Our results reveal trends in feeding and general bat activity across foraging guilds in relation to insect availability, habitat type, wind, temperature, and seasonality. We show how seasonal patterns in behavior across guilds were affected by habitat type, temperature, and wind. We found that SRE commuting and especially feeding activity was highest in natural habitats, whereas LRE overall activity at habitats more season dependent. We found that nocturnal insect availability was positively correlated with total bat feeding activity throughout the night. Our results provide evidence for both direct and indirect risks to bat communities by wind parks: SRE bat habitat is lost to wind energy infrastructure and LRE bat may have an increased risk of fatality. Our findings provide important insights on seasonal and spatial variability in bat activity, which can inform standardizing monitoring of bats acoustically in boreal forests, at wind parks, and in combination with non-invasive insect monitoring.</p>
Data from: Geographic source of bats killed at wind-energy facilities in the eastern United States
<p>Bats subject to high rates of fatalities at wind-energy facilities are of conservation concern, but the impact on broader bat populations is difficult to assess. One reason is the poor understanding of the geographic source of individual fatalities and whether they constitute local resident individuals or migrants. Here, we used stable hydrogen isotopes, trace elements and species distribution models to determine the summer geographic origins of three different bat species (<em>Lasiurus borealis</em>, <em>L. cinereus</em>, and <em>Lasionycteris noctivagans</em>) killed at wind-energy facilities in Ohio and Maryland in the eastern United States. In Ohio, 58.4%, 78.7%, and 97.8% of all individuals of <em>L. borealis</em>, <em>L. cinereus</em>, and <em>L. noctivagans</em>, respectively, lacked evidence of movement and were likely residents. In contrast, in Maryland 22.7%, 62.9% and 72.7% of these same species were classified as residents. Our results suggest that a substantial portion of bats killed at a given wind facility are likely derived from resident populations. Finally, there is variation in the proportion of residents killed between seasons for some species and evidence of philopatry to summer roosts. Overall, these results indicate that impact of wind-energy facilities on resident bat populations may be greater than previously appreciated, but this impact is likely to vary across species and sites. Similar studies should be conducted across a boarder geographic scale to understand the impacts on bat populations from wind-energy facilities.</p>
India Onshore Wind Energy Atlas Accounting for Altitude and Land Use Restrictions and Co-Located Solar
<p>India faces the simultaneous challenges of meeting rising energy demand and reducing carbon emissions. To address these, India must transition to renewable energy sources. These high-resolution maps are used to quantify available areas for wind farms, after accounting for restrictions, including airports, buildings, protected land use, military zones, railways, roads, water bodies, waterways, wildlife and nature, high elevation and slope, and existing solar farms, to which policy-informed setback distances are applied. This study finds the wind and solar potential within available areas considering three altitudes (100 m, 150 m, 200 m) and four wind speed thresholds (5-8 m/s), and modern wind turbine and solar array dimensions. The raster files included here indicate available areas after aggregating restrictions for different combinations of altitude and wind speed threshold. Availability is indicated with a binary system in which available land is designated with a value of zero and restricted land is designated with a value of one.</p>
Data Supplement for 'Curled Wake Development of a Yawed Wind Turbine at Turbulent and Sheared Inflow' - Wind Energy Science Journal
<p>Data Supplement for 'Curled Wake Development of a Yawed Wind Turbine at Turbulent and Sheared Inflow' - Wind Energy Science Journal</p> <p>This database contains the measurement using a model wind turbine with 0.6m diameter(D) in a wind tunnel. A short-range Lidar WindScanner facilitated mapping the wake with a high spatial and temporal resolution in vertical, cross-stream planes at different downstream locations and in a horizontal plane at hub height.</p> <p>The measurement campaign was conducted in the large wind tunnel at ForWind-University of Oldenburg. The wind tunnel has a test section cross-section with the dimensions of 3m x3m. For this study three movable test section elements of 6m length were attached for a total enclosed length of 18m. The roof of the test section was adjusted to compensate for boundary layer growth to achieve a zero pressure gradient for the target wind speed of the experiments, nominally 7.5m/s, with an empty tunnel with no grid or turbine installed. The three-bladed MoWiTO 0.6 wind turbine model(Schottler et al.(2016)), with a hub height (h) of 0.77m and a diameter of 0.58m was placed at a distance of 2.4D downstream of the test section inlet, where the distance was measured to the centre of the rotor. In addition, the distance between the rotor center and the tower center is 110mm.<br> The flow blockage, based on rotor swept area and tower flow-facing area, was 2.7%. The wind turbine controller is based on the torque of the generator (Petrovi ́c et al. (2018)) leading to a tip speed ratio of 5.7 at the operational point during non-misaligned cases with no grid. More information can be found in the paper.</p> <p>The folder contains 12 unique .mat files each containing a matlab structure. The matlab structure conatins the vertical and horizontal scan for each inflow and operational condition:<br> With the upstream turbine installed:<br> - Yaw0_Uniform_NoGrid<br> - 1D, 2D, 3D, 5D, 13D, 16D, Horizontal<br> - Yaw30_Uniform_NoGrid<br> - 1D, 2D, 3D, 5D, 13D, 16D, Horizontal<br> - Yawneg30_Uniform_NoGrid<br> - 1D, 2D, 3D, 5D, 13D, 16D, Horizontal</p> <p> - Yaw0_Uniform_PassiveGrid<br> - 1D, 2D, 3D, 5D, 7D, 10D, Horizontal<br> - Yaw30_Uniform_PassiveGrid<br> - 1D, 2D, 3D, 5D, 7D, 10D, Horizontal<br> - Yawneg30_Uniform_PassiveGrid<br> - 1D, 2D, 3D, 5D, 7D, 10D, Horizontal </p> <p> - Yaw0_BoundaryLayer_PassiveGrid<br> - 1D, 2D, 3D, 5D, 7D, 10D, Horizontal<br> - Yaw30_BoundaryLayer_PassiveGrid<br> - 1D, 2D, 3D, 5D, 7D, 10D, Horizontal<br> - Yawneg30_BoundaryLayer_PassiveGrid<br> - 1D, 2D, 3D, 5D, 7D, 10D, Horizontal </p> <p>Without the upstream turbine installed:<br> - NoTurbine_Uniform_NoGrid<br> - 1D, 2D, 3D, 5D, 13D, 16D<br> - NoTurbine_Uniform_PassiveGrid<br> - 0D, 1D, 2D, 3D, 5D, 7D, 10D<br> - NoTurbine_BoundaryLayer_PassiveGrid<br> - 0D, 1D, 2D, 3D, 5D, 7D, 10D </p> <p>Within each substructure the following parameters are provided:<br> - v_los [m/s] ----------> Line of sight velocity<br> - sigma [m/s] ----------> Spectrum width<br> - x_Global_frame [m] ---> x-coordinate referenced at the lower grid midpoint<br> - y_Global_frame [m] ---> y-coordinate referenced at the lower grid midpoint<br> - z_Global_frame [m] ---> z-coordinate referenced at the lower grid midpoint<br> - xx [m] ---------------> Grid of the x-coordinate referenced at the lower grid midpoint<br> - yy [m] ---------------> Grid of the y-coordinate referenced at the lower grid midpoint<br> - zz [m] ---------------> Grid of the z-coordinate referenced at the lower grid midpoint<br> - uu [m/s] -------------> Horizontal wind speed at the position of the gridded coordinates, these data have been interpolated onto the grid<br> </p> <p>When using this database please reference to the journal paper.</p> <p>All data has been included without warranty, express or implied.</p> <p>For further questions, please contact the corresponding author.<br> </p>
A predictive flight-altitude model for avoiding future conflicts between an emblematic raptor and wind energy development in the Swiss Alps
<p>Deployment of wind energy is proposed as a mechanism to reduce greenhouse gas emissions. Yet, wind energy and large birds, notably soaring raptors, both depend on suitable wind conditions. Conflicts in airspace use may thus arise between wind energy development and wildlife protection due to the risks of collisions of birds with the blades of wind turbines. Using locations of GPS-tagged bearded vultures, a rare scavenging raptor reintroduced into the Alps, we built a spatially-explicit model to predict potential areas of conflict with future wind turbines deployments in the Swiss Alps. We modelled the probability of bearded vultures flying within or below the rotor-swept zone of wind turbines as a function of wind and environmental conditions, including food supply (presence of wild ungulates). Flight activity at potential risk of collision was generally high, concentrating on south-exposed mountainsides, especially in areas where ibex carcasses have a high occurrence probability, with critical areas covering vast expanses throughout the Swiss Alps. Our model provides a spatially-explicit decision tool that will guide authorities and energy companies for planning the deployment of wind farms in a proactive manner to reduce risk to emblematic Alpine wildlife.</p>
Datasets for the publication " Enhancing drought resilience and energy security through complementing hydro by offshore wind power - the case of Brazil"
<p>This repository contains the datasets for the publication "Enhancing drought resilience and energy security through complementing hydro by offshore wind power - the case of Brazil".</p> <ul> <li><strong>Bias correction</strong></li> <li>Technical data of existing farms (ABBEólica) </li> <li>Bias correction factors at the farm level</li> </ul> <p> </p> <ul> <li><strong>Demand</strong></li> <li>Simulated wind and solar power</li> <li>Biomass, nuclear, and small hydropower generation in 2019</li> <li>Raw demand data</li> <li>Updated demand </li> </ul> <p> </p> <ul> <li><strong>Hydropower time series</strong></li> <li>Affluent Natural energy of run-of-rivers (fio d'água, in Portuguese) and reservoirs (reservatórios, in Portuguese), installed capacity, and maximal storage</li> </ul> <p> </p> <ul> <li><strong>Offshore wind farms</strong></li> <li>Locations, coordinates, water depth, available areas, water depth, distance to shore, technology,and maximal capacity;</li> <li>Code to estimate offshore wind farm capex and opex.</li> </ul> <p> </p> <ul> <li><strong>Results of Calliope model </strong></li> <li>capacity</li> <li>carrier_prod (power generation)</li> <li>storage</li> <li>costs</li> <li>emissions</li> </ul> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Datasets used in the Paper of "Analysis of leading edge protection application on wind turbine performance through energy and power decomposition approaches"
<p>These are the datasets used in the <em>Wind Energy</em> paper "Analysis of leading edge protection application on wind turbine performance through energy and power decomposition approaches." The paper can be accessed <a href="https://onlinelibrary.wiley.com/doi/10.1002/we.2722">here</a>. The computer code used to produce the results in the paper can be found <a href="../records/6321157">here</a>.</p>
Coastal Upwelling Modulates Winds and Air-Sea Fluxes, Impacting Offshore Wind Energy
<p>Model Output supporting the paper "Coastal Upwelling Modulates Winds and Air-Sea Fluxes, Impacting Offshore Wind Energy"</p> <p>The dataset includes four WRF runs, with upwelling (labeled 'operational') and with upwelling removed (labeled 'experimental'). Two of the runs have parameterized wind turbines, labeled "Fitch". </p> <p>This work was supported by NJ Board of Public Utilities. </p> <p> </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.
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DANDI Archive for NWB datasets
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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.