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261 results for “Turbine”
TinyWT: A Large-Scale Wind Turbine Dataset of Satellite Images for Tiny Object Detection
<p>This dataset is from the paper "TinyWT: A Large-Scale Wind Turbine Dataset of Satellite Images for Tiny Object Detection", which has been accepted by the WACV 2024 CV4EO Workshop.</p>
Internet Appendix for: "Disentangling wake and projection effects in the aerodynamics of wind turbines with curved blades''
<p>This is the internet appendix of the paper: "Disentangling wake and projection effects in the aerodynamics of wind turbines with curved blades"</p>
Wind turbines without curtailment produce large numbers of bat fatalities throughout their lifetime: A call against ignorance and neglect
<p>Bats are protected by national and international legislation in European countries, yet many species, particularly migratory aerial insectivores, collide with wind turbines which counteracts conservation efforts. Within the European Union it is legally required<br> to curtail the operation of wind turbines at periods of high bat activity, yet this is not practiced at old wind turbines. Based on data from the national carcass repository in Germany and from our own carcass searches at a wind park with three turbines west<br> of Berlin, we evaluated the magnitude of bat casualties at old, potentially mal-sited wind turbines operating without curtailment. We report 88 documented bat carcasses collected by various searchers over the 20-year operation period of this wind park from<br> 2001 to 2021. Common noctule bats ( Nyctalus noctula ) and common pipistrelles (Pipistrellus pipistrellus ) were most often found dead at these turbines. Our search campaign in August and September 2021 yielded a total of 18 carcasses. We estimated that at least 209 bats were likely killed during our field survey, yielding more than 70 casualties/wind turbine or 39 casualties/MW in two months. Since our campaign covered only part of the migration season, we consider this value as an underestimate. The 20-year period of the wind park emphasises the substantial impact old turbines may have on bat individuals and populations when operating without curtailments. We call for reconsidering the operation procedures of old wind turbines to stop the continuous loss of bats in Germany and other countries where turbine curtailments are even less practiced than in Germany.</p>
Diet analysis of bats killed at wind turbines suggest large-scale losses of trophic interactions
<p>Agricultural practice has led to landscape simplification and biodiversity decline, yet recently, energy producing infrastructures, such as wind turbines, have been added to these simplified agroecosystems, turning them into multi-functional energy-agroecosystems. Here, we studied the trophic interactions of bats killed at wind turbines using a DNA metabarcoding approach to shed light on how turbine-related bat fatalities may possibly feedback on local habitats. Specifically, we identified insect DNA in the stomachs of common noctule bats (<em>Nyctalus noctula</em>) killed by wind turbines in Germany to infer in which habitats these bats hunted. Common noctule bats consumed a wide variety of insects from different habitats, ranging from aquatic to terrestrial ecosystems (e.g. wetlands, farmland, forests, and grasslands). Agricultural and silvicultural pest insects made up about 20% of insect species consumed by the studied bats. Our study suggests that the potential damage of wind energy production goes beyond the loss of bats and the decline of bat populations. Bat fatalities at wind turbines may lead to the loss of trophic interactions and ecosystem services provided by bats, which may add to the functional simplification and impaired crop production, respectively, in multi-functional ecosystems.</p>
Plots for the publication "Lidar-assisted model predictive control of wind turbine fatigue via online rainflow-counting considering stress history"
<p>These are the raw plot files from the publication "Lidar-assisted model predictive control of wind turbine fatigue via online rainflow-counting considering stress history".</p> <p>The files have been created with MATLAB 2019, and labeled according to their corresponding figure number(s) in the publication.</p>
High-Resolution TURBINE fMRI Dataset 1
<p>Isotropic 0.67 mm visual cortex-slab TURBINE raw dataset 1 (in ISMRMRD format) for "Ultra-High Resolution fMRI at 7T using Radial-Cartesian TURBINE sampling" published in Magnetic Resonance in Medicine.</p>
High-Resolution TURBINE fMRI Dataset 2
<p>Isotropic 0.67 mm visual cortex-slab TURBINE raw dataset 2 (in ISMRMRD format) for "Ultra-High Resolution fMRI at 7T using Radial-Cartesian TURBINE sampling" published in Magnetic Resonance in Medicine.</p> <p> </p>
Synchronous PIV measurements of a self-powered blood turbine and pump couple for right ventricle support
<p>A blood turbine-pump system (iATVA) was proposed as a self-driven, motorless mechanical right-heart assist device. In this study, the iATVA system is investigated with particular emphasis on the blood turbine flow dynamics. A time-resolved 2D particle image velocimetry (PIV) set-up allowed simultaneous recordings from both the turbine and pump impellers. Results showed that magnetically coupled impellers operated synchronously. As the turbine flow rate increased from 1.6 to 2.4 LPM, the rotational speed and relative inlet flow angle increased from 630 to 900 rpm, and 38 to 55% respectively. At the trailing edges, backflow region spanned 3/5 of the total passage outlet flow, and an extra leakage flow was observed at the leading edge. For this initial turbine design, ~75% of the turbine blade passage was not contributing to the impulse operation mode. The maximum non-wall shear rate was ~2288 s<sup>-1</sup> near to the inlet exit, which is significantly lower than the commercial blood pumps, encouraging further research and blood experiments of this novel concept. Experimental results will improve the hydrodynamic design of the turbine impeller and volute regions and will be useful in computational fluid dynamics validation studies.</p>
MATLAB Implementation for Wind Turbine Prognosis Using Uncertainty Bayesian-Optimized Lightweight Neural Network
<p>These MATLAB codes accompany the paper titled "---," currently submitted to the 11th International Electronic Conference on Sensors and Applications (ECSA-11). The paper presents a novel approach to wind turbine prognosis for maintenance purposes using the Uncertainty Bayesian-Optimized Extreme Learning Machine (UBO-ELM) algorithm.</p> <p>The codes provided here implement the methodology described in the paper, including data preprocessing, model training and evaluation, uncertainty quantification, and visualization of results. These codes are intended for researchers and practitioners in the field of wind energy systems and predictive maintenance.</p> <p>Please note that the paper is currently under review at ECSA-11. Once the paper is approved and the embargo is lifted, these codes will be accessible openly. Users are kindly requested to cite our paper when utilizing these codes for their research.</p>
MATLAB codes for paper: UBO-EREX: Uncertainty Bayesian-Optimized Extreme Recurrent EXpansion for Degradation Assessment of Wind Turbine Bearings
<p>These codes belong to the following paper. Please cite our work.</p> <p>Berghout T, Benbouzid M. UBO-EREX: Uncertainty Bayesian-Optimized Extreme Recurrent EXpansion for Degradation Assessment of Wind Turbine Bearings. <em>Electronics</em>. 2024; 13(12):2419. https://doi.org/10.3390/electronics13122419</p>
Midpoint Characterization Factors to assess impacts of turbined water use from hydropower production
<p>This repository contains "Supporting Information 2 (SI2)" and a shapefile with the CFs at basin level for the publication titled "Midpoint Characterization Factors to assess impacts of turbine water use from hydropower production", published in "The International Journal of Life Cycle Assessment" .<br><br></p> <p>Content:</p> <p>SI2 is an excel file with the following sheets: </p> <ul> <li>Midpoints_CFs_Basin <ul> <li>Midpoint CF values [HDOR·y/m3] for each basin, including TWU, RV and Q input parameters and sensitivity results</li> </ul> </li> <li>TE <ul> <li>TE values used for TWU calculations</li> </ul> </li> <li>Midpoints_CFs_Country <ul> <li>Midpoint CF values [HDOR·y/m3] aggregated to country level (Weighted average based on TWU). Country delination obtained from: The Word Bank. (2021). World International Borders - Very High Definition. https://datacatalog.worldbank.org/search/dataset/0038272</li> </ul> </li> </ul> <div> <ul> <li>Case_Study <ul> <li>Underlying data for the case study performed in Sections 2.6 and 3.3 of the main publication</li> </ul> </li> </ul> </div> <p>CF_Basin_shapefile.zip contains:</p> <ul> <li>Shapefile with CF values at basin level. <br> <ul> <li>Basins obtained from from Lin et al. (2021), https://doi.org/10.1038/s41597-021-00819-9 </li> <li>basid matches with "Midpoints_CFs_Basin"</li> </ul> </li> </ul> <p> </p> <p> </p> <p> </p> <p> </p>
Additional material for the Wind Energy manuscript "An engineering approach for the estimation of slewing bearing stiffness in Wind Turbine Generators"
<p>This documment compiles the additional material for the manuscript “An engineering approach for the estimation of slewing bearing stiffness in Wind Turbine Generators”. The document shows the results for all the considered design points, comparing the the proposed approach with the Finite Element model.</p>
DTU 10MW reference turbine HAWC2 simulations for Model-free estimation of available power with deep learning training
<p>The time series of DTU 10MW HAWC2 model simulations of two channels: hub-height wind speed and produced power. They are generated to train model-free estimation of available power approach, using wind speed and its moving standard deviation as inputs. They include 3-hour length 100Hz simulations of 3 mean wind speeds (7 m/s, 9m/s and 11m/s) as well as 3 levels of turbulence intensity (TI = 7%, 10% and 20%). </p> <p>The dataset and the training algorithm can also be found here: <a href="https://gitlab.windenergy.dtu.dk/tuhf/deep-learning-for-available-power-estimation/tree/master">https://gitlab.windenergy.dtu.dk/tuhf/deep-learning-for-available-power-estimation/tree/master</a></p>
NETTUNO Experiment 1 – Wake Development in Floating Wind Turbines
<p>This dataset, collected as part of the NETTUNO research project, includes measurements from wind tunnel tests on a 1:75 scale model wind turbine. The primary focus of the experiment was to analyze how platform motion in different directions affects the aerodynamics of the wind turbine rotor and the development of its wake. The dataset consists of two components:<br>• Measurements of the aerodynamic forces and moments experienced by the rotor under various platform motion conditions.<br>• Wind speed measurements collected at multiple downstream distances from the rotor, capturing the velocity profiles and turbulence characteristics within the turbine's wake.<br>The dataset is designed to serve as a comprehensive benchmark for researchers and engineers working on floating wind turbine aerodynamics, offering valuable insights for optimizing wind farm layouts and for developing simulation tools.</p>
High-resolution modelling of uplift landscapes can inform micro-siting of wind turbines for soaring raptors
<p>Collision risk of soaring birds is partly associated with updrafts to which they are attracted. To identify risk-enhancing landscape features, a micro-siting tool was developed to model orographic and thermal updraft velocities from high-resolution remote sensing data. The tool was applied to the island of Hitra, and validated using GPS-tracked white-tailed eagles (<i>Haliaeetus albicilla</i>). Resource selection functions predicted that eagles preferred ridges with high orographic uplift, especially at flight altitudes within the rotor-swept zone (40-110 m). Flight activity was negatively associated with the widely distributed areas with high thermal uplift at lower flight altitudes (<110 m). Both the existing wind-power plant and planned extension are placed at locations rendering maximum orographic updraft velocities around the minimum sink rate for white-tailed eagles (0.75 m/s) but slightly higher thermal updraft velocities. The tool can contribute to improved micro-siting of wind turbines to reduce environmental impacts, especially for soaring raptors.</p>
The influence coefficients used in Wind Energy Science paper "A computationally efficient engineering aerodynamic model for swept wind turbine blades"
<p>The influence coefficients for the convective correction with full double-precision floating-point accuracy. This is the supplement for the research article: "A computationally efficient engineering aerodynamic model for swept wind turbine blades", submitted to Wind Energy Science journal.</p> <p>Code language: Fortran</p>
Drivers of bat activity at wind turbines advocate for mitigating bat exposure using multicriteria algorithm-based curtailment
<p>data used for the paper</p>
Multivariate prediction on wake-affected wind turbines using graph neural networks (Eurodyn) database
<p>Database consisting of graphs generated using randomized layouts and PyWake simulations used in '<em>Multivariate prediction on wake-affected wind turbines using graph neural networks</em>', contribution to Eurodyn 2023. </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>
NREL 5MW wind turbine blade and tower degradation
<p><strong>Problem</strong></p><p>➔Track the degradation of one rotor blade and tower base over over as 10 months period under wind inflow and operational uncertainties</p><p><strong>Scenario</strong></p><p>➔Continuous wind turbine blade and tower stiffness degradation, emulating blade root delamination resulting in dynamic instability, leading to the tower base excess fatigue.</p><p><strong>Simulation environment and setup in FAST v8 :</strong></p><p>➔In the blade input file: modify flap and edge stiffnesses in damage regions, blade root (only 1 in 3 blades is affected). Assumed equal degradation in both edgewise and flapwise directions</p><p>➔In the blade input file: modify mode shapes coefficients (1st flap, 2nd flap and 1st edge modes)</p><p>➔In the tower input file: modify FA and SS stiffnesses in damage regions, tower base</p><p>➔In the tower input file: modify mode shapes coefficients (1st & 2nd FA and 1st & 2nd SS modes)</p><p>➔Note that the tower degradation only "shows up" in the last 2 monitoring periods</p><p>➔In the turbulence input file: modify wind speed, tuburlence intensity, shear exponent, horizontal and vertical inflow angles.</p><p>➔Further complication by assuming that the average input environmental conditions are not stationary over the monitoring period, emulating seasonal variations.</p><p>➔This means 120 environmental samples (and consequenly input wind field time series) are sampled from different distributions each period during the degradation process</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.