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108 results for “wind modelling”
Data set used in article: Model Predictive Control for Wake Redirection in Wind Farms: a Koopman Dynamic Mode Decomposition Approach
<p>Step-wise yaw deflection in 2 wind turbines in SOWFA. More information in the article.</p>
Dataset for An efficient multivariate deep learning model for monitoring mooring line tension of floating wind turbine
<p>Reference data needed for mooring line tensions prediction of a 15 MW wind turbine.<br> <br> This dataset contains OpenFAST outputfiles for different design load cases used in the paper.</p> <p>These data files are designed to be used together with the python code, which is available publicly on https://github.com/ramisetti/ 3SDLMooringPrediction</p>
Data from: Cross-scale interactions among bark beetles, climate change and wind disturbances a landscape modeling approach
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Data from: "Genome-wide microsatellite marker development from next-generation sequencing of two non-model bat species impacted by wind turbine mortality: Lasiurus borealis and L. cinereus (Vespertilionidae)" in Genomic Resources Notes accepted 1 October 2013 to 30 November 2013
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CESM1.2 simulation output for: The role of westerly wind bursts during different seasons versus ocean heat recharge in the development of extreme El Niño in a climate model
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High-resolution modelling of uplift landscapes can inform micro-siting of wind turbines for soaring raptors
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Data from: A predictive model for improving placement of wind turbines to minimise collision risk potential for a large soaring raptor
<p><span><span><span><span><span><span><span><span><span><span><span>1. With the rapid growth of wind energy developments worldwide, it is critical that the negative impacts on wildlife are considered and mitigated. This includes minimising the numbers of large soaring raptors which are killed when they collide with wind turbines.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>2. To reduce the likelihood of raptor collisions, turbines should be placed at locations which are least used by sensitive species. For resident or breeding species, this is often delineated crudely through the use of circular buffers centred on nest sites, which assume uniform habitat use around a nest site.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>3. Using GPS tracking data together with a digital elevation model we build and cross-validate a simple generalizable model, to classify the spatial likelihood of wind turbine collisions for resident adult Verreaux's eagles in any landscape where there are known nests. We apply our methods to operational developments in South Africa to validate the model and demonstrate its ability in predicting actual collision mortalities.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>4. Our Collision Risk Potential (CRP) model included the variables distance to nest, distance to conspecific nest, slope, distance to slope and elevation. Using our model, rather than a circular buffer, resulted in ca. 4–5% improvement in eagle protection while excluding development from the same amount (but not shape) of area. For an equal level of eagle protection, our model can make ca. 20–21% more area available for wind energy development compared to a circular buffer.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>5. Exploring collisions at operational wind farms in South Africa we show that our CRP model correctly predicted 87% of known collisions, while circular buffers (5.2km radius) only captured 50% of collisions.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>6. <i>Synthesis and applications</i>: We show that by using predictive models to account for habitat use, a greater area of land can be made available for wind energy development without increased mortality risk to raptors. Our predictive model can be used to provide robust guidance on wind turbine placement in South Africa in a way which minimizes the conflict between a vulnerable raptor species and the development of renewable energy. </span></span></span></span></span></span></span></span></span></span></span></p>
Dataset for Snow Transport Modeling in the Northern Wind River Range
<p>This dataset includes materials related to research on snow transport distances in alpine terrain. The project used a simple process-based snow model (modified version of DHSVM snow sub-model) and a differentiable network to constrain the minimum transport distances and source areas required to explain deep snow accumulation zones assuming a relatively smoother snowfall pattern.</p> <p>This dataset includes:</p> <ol> <li>Scripts for processing workflow and figure generation</li> <li>Spatial inputs and outputs from neural network for each of 4 tested snowfall patterns plus 3 sublimation tests</li> <li>Differentiable modeling scripts</li> </ol> <p>Additional data included in prior version of archive:</p> <ol> <li>DHSVM source code, inputs, config files, etc.</li> <li>WindNinja simulation data</li> <li>Additional spatial data (watershed boundaries, streams, etc.)</li> </ol>
Effects of upper mantle wind on mantle plume morphology and hotspot track: numerical modeling
<p>This is the dataset for the paper "Effects of upper mantle wind on mantle plume morphology and hotspot track: numerical modeling"</p>
Wind profile in the wave boundary layer and its application in a coupled atmosphere-wave model
<p>The simulation data for the study</p>
Figures and tables with datasets in "A wind-induced snow redistribution study considering contact based on a bidirectional coupled model of wind and discrete snow particles"
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Effects of wind straining on estuarine stratification: A combined observational and modeling study
<p>Field data and model output</p>
Data for 'SPH modelling of wind-companion interactions in eccentric AGB binary systems'
<p>Additional material to "Malfait et al. 2021, SPH modelling of wind-companion interactions in eccentric AGB binary systems": <a href="https://ui.adsabs.harvard.edu/link_gateway/2021A&A...652A..51M/arxiv:2107.01074" target="_blank" rel="noreferrer noopener">arXiv:2107.01074</a></p> <p>This contains input files and final output dumps of the Phantom simulations of this paper, and additional movies of 4 of the simulations.</p> <p>The code used to perform the simulations is available at: <a href="https://github.com/danieljprice/phantom">https://github.com/danieljprice/phantom.</a></p> <p>Splash (<a href="https://github.com/danieljprice/splash">https://github.com/danieljprice/splash</a> ) and Plons (<a href="https://github.com/Ensor-code/plons">https://github.com/Ensor-code/plons</a> ) were used to create figures and plots from this data.</p>
X-ray spectrum for CAT3D-WIND clumpy model
<p>In Buchner et al. (in prep) computations of a clumpy X-ray obscurer of AGN is computed. Here the same method is applied to some CAT3D-WIND models.</p> <p>See https://github.com/JohannesBuchner/xars for more information.</p>
Multiscale modeling of oat and wheat stems under wind stress
<p>Lodging impedes the successful cultivation of cereal crops. Complex anatomy, morphology and environmental interactions make identifying reliable and measurable traits for breeding challenging. Therefore, we present a unique collaboration among disciplines for plant science, modeling and simulations, and experimental fluid dynamics in a broader context of breeding lodging resilient wheat and oat. We ran comprehensive wind tunnel experiments to quantify the stem bending behavior of both cereals under controlled aerodynamic conditions. Measured phenotypes from experiments concluded that the wheat stems response is stiffer than the oat. However, these observations did not in themselves establish causal relationships of this observed behavior with the physical traits of the plants. To further investigate we created an independent finite element simulation framework integrating our recently developed multiscale material modeling approach to predict the mechanical response of wheat and oat stems. All the input parameters including chemical composition, tissue characteristics, and plant morphology have a strong physiological meaning in the hierarchical organization of plants, and the framework is free from empirical parameter tuning. This feature of our simulation framework reveals the multiscale origin of the observed wide differences in the stem strength of both cereals that would not have been possible with purely experimental approach.</p>
Open Research Data for "A New Framework for Evaluating Model Simulated Inland Tropical Cyclone Wind Fields"
<p>The (1) NOAA GFDL T-SHiELD outputs, (2) processed ASOS data, and (3) observation-based, theory-driven wind profiles data used in the manuscript "A New Framework for Evaluating Model Simulated Inland Tropical Cyclone Wind Fields". </p>
Multiscale modeling of oat and wheat stems under wind stress
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Data from: A predictive model for improving placement of wind turbines to minimise collision risk potential for a large soaring raptor
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Data from: Collector motion affects particle capture in physical models and in wind pollination
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Metop-B ASCAT Inter-Calibrated ESDR Level 2 Observed and Modeled Spatial Derivatives of Surface Wind and Wind Stress Version 1.0
This dataset contains the curl and divergence of ocean surface equivalent neutral wind and wind stress, derived from satellite-based scatterometer observations (the MetOp-B ASCAT scatterometer), representing the first science quality release of these data (post-provisional after v1.0) funded under the MEaSUREs program. This product from MetOp-B ASCAT has been intercalibrated with similar scatterometer measurements from instruments on the MetOp-A, ScatSat-1, and QuikScat satellites, all of which can be found on the MEaSUREs OSVW Project Page. These Level 2 data are provided on a non-uniform grid within the satellite swath at ~12.5 km pixel resolution. Each L2 file corresponds to a specific orbital revolution number, which begins at the southernmost point of the ascending orbit - the thumbnail preview shows data for all orbits over a day (typically 14 orbits). Estimates for the curls and divergences are computed over several spatial domains with varying radii from the point of interest, and included as separate variables.<br><br>The dataset represents the first science quality release funded under the MEaSUREs (Making Earth System Data Records for Use in Research Environments) program. The primary purpose of this release is for science evaluation by the NASA International Ocean Vector Winds Science Team (IOVWST). This V1.0 of the data was derived from V1.1 of the L2 wind and stress product.
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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.