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524 results for “blade”
Vibration-based Monitoring of a Small-scale Wind Turbine Blade Under Varying Climate Conditions. Part I: An Experimental Benchmark
<p>This repository contains all publicly available data related to the experimental part of <a href="https://onlinelibrary.wiley.com/doi/epdf/10.1002/stc.2660">Sonkyo-Benchmark</a>. The data of each experimental case (R, A, B, C, D, E, F, G, H, I, J, K, L) and temperature point (-15, -10, -5, 0, 5, 10, 15, 20, 25, 30, 35, 40) are stored in a zip file named "Case_<em>X</em>_(<em>T</em>)", where <em>X</em> denotes the case label and <em>T</em> refers to the temperature value. Each file "Case_<em>X</em>_(<em>T</em>).zip" contains two folders "Case_<em>X</em>_(<em>T</em>)_1" and "Case_<em>X</em>_(<em>T</em>)_2", wherein the test results from the two sensor layouts are stored. </p>
SmartBlades 2.0 Rotor Blade Nastran Models
<p>The <a href="https://www.iwes.fraunhofer.de/en/research-projects/finished-projects-2020/smart-blades-2.html">SmartBlades 2.0 project</a> was funded by the Federal Ministry for Economic Affairs and Energy (BMWi) under the Funding number: 0324032.</p> <p>The reference finite element model in which this dataset is based upon consists of a validated wind turbine blade model for this 20-meters blade:</p> <blockquote> <p>Christian Willberg. (2020). Smartblades 2 finite element reference wind turbine blade model (1.1) [Data set]. Zenodo. <a href="http://doi.org/10.5281/zenodo.4604693">https://doi.org/10.5281/zenodo.4604693</a></p> </blockquote> <p>This dataset includes four finite element models of the SmartBlades 2.0 Rotor Blade, described below. All input files "*.bdf" were used in MSC Nastran version 2018.2 to generate the output files "*.h5".</p> <p>1) SmartBlades2 Rotor Blade Model V02 Topology Update</p> <p><a href="http://doi.org/10.5281/zenodo.4604693">Willberg, C. model</a> updated with topology features in the trailing edge, and spar-web joint regions. Including additional strucutral and test sensor masses, and refined mesh of 50mm element size.</p> <p>1A) Model incorporating a clamped root boundary condition and test sensor masses:</p> <ul> <li>Input: <a href="https://zenodo.org/record/5729717/files/SmartBlades2_Rotor_Blade_Nastan_Model_V02-Topology_Update-Root_Clamped.bdf">SmartBlades2_Rotor_Blade_Nastan_Model_V02-Topology_Update-Root_Clamped.bdf</a></li> <li>Output: <a href="http://zenodo.org/record/5729717/files/SmartBlades2_Rotor_Blade_Nastan_Model_V02-Topology_Update-Root_Clamped.h5">SmartBlades2_Rotor_Blade_Nastan_Model_V02-Topology_Update-Root_Clamped.h5</a></li> </ul> <p>1B) Model incorporating a free-free boundary condition:</p> <ul> <li>Input: <a href="http://zenodo.org/record/5729717/files/SmartBlades2_Rotor_Blade_Nastan_Model_V02-Topology_Update-Free_Free.bdf">SmartBlades2_Rotor_Blade_Nastan_Model_V02-Topology_Update-Free_Free.bdf</a></li> <li>Output: <a href="http://zenodo.org/record/5729717/files/SmartBlades2_Rotor_Blade_Nastan_Model_V02-Topology_Update-Free_Free.h5">SmartBlades2_Rotor_Blade_Nastan_Model_V02-Topology_Update-Free_Free.h5</a></li> </ul> <p><br> 2) SmartBlades2 Rotor Blade Model V02b Sparweb Free Joint Variant</p> <p>The V02 Topology Update model including RBE2 connections along the spar-web joints with decoupled rotation in the spanwise axis.</p> <p>2A) Model incorporating a clamped root boundary condition and test sensor masses:</p> <ul> <li>Input: <a href="http://zenodo.org/record/5729717/files/SmartBlades2_Rotor_Blade_Nastan_Model_V02b-Sparweb_Free_Joint-Root_Clamped.bdf">SmartBlades2_Rotor_Blade_Nastan_Model_V02b-Sparweb_Free_Joint-Root_Clamped.bdf</a></li> <li>Output: <a href="http://zenodo.org/record/5729717/files/SmartBlades2_Ro tor_Blade_Nastan_Model_V02b-Sparweb_Free_Joint-Root_Clamped.h5">SmartBlades2_Ro tor_Blade_Nastan_Model_V02b-Sparweb_Free_Joint-Root_Clamped.h5</a></li> </ul> <p>2B) Model incorporating a free-free boundary condition:</p> <ul> <li>Input: <a href="http://zenodo.org/record/5729717/files/SmartBlades2_Rotor_Blade_Nastan_Model_V02b-Sparweb_Free_Joint-Free_Free.bdf">SmartBlades2_Rotor_Blade_Nastan_Model_V02b-Sparweb_Free_Joint-Free_Free.bdf</a></li> <li>Output: <a href="http://zenodo.org/record/5729717/files/SmartBlades2_Rotor_Blade_Nastan_Model_V02b-Sparweb_Free_Joint-Free_Free.h5">SmartBlades2_Rotor_Blade_Nastan_Model_V02b-Sparweb_Free_Joint-Free_Free.h5</a><br> </li> </ul> <table> <caption>General data of the blade (from <a href="https://elib.dlr.de/131955/1/AG_FVW_in_der_Windenergie_191024_Stueve.pdf">https://elib.dlr.de/131955/1/AG_FVW_in_der_Windenergie_191024_Stueve.pdf</a>)</caption> <tbody> <tr> <td>Diameter of the rotor</td> <td>46.61m</td> </tr> <tr> <td>Nominal rotational speed</td> <td>37.1 rpm</td> </tr> <tr> <td>Length of rotor blade</td> <td>19.99 m</td> </tr> <tr> <td>Maximum chord length</td> <td>2.38 m</td> </tr> <tr> <td>Max. pre-bend</td> <td>1 m</td> </tr> <tr> <td>Surface of main shell</td> <td>69.8 m²</td> </tr> <tr> <td>Blade nominal mass</td> <td> <p>Fiber mass (dry) 889.5 kg</p> <p>Infusion Resin 579.3 kg</p> <p>Bonding Resin 44 kg</p> <p>Other materials (e.g. Foam) 80.5 kg</p> <p>Extra masses (e.g. Sensors) 123.4 kg</p> <p>Total mass of the blade 17168 kg</p> </td> </tr> </tbody> </table> <p> </p>
Fatigue properties of wind turbine rotor blade hybrid epoxy adhesives
<p>This dataset includes the tensile data at two different strain rates and tensile-tensile fatigue data of epoxy adhesives used in wind turbine rotor blades. SPABOND™ 820HTA (non-toughened) and SPABOND™ 840HTA (toughened) epoxy adhesives are combined at different weight proportions to develop the hybrid adhesives. The hybrid and ASTM D638-22 tensile specimen geometry (Type I and Type II) effects on fatigue performance are determined through instrumented experiments. </p>
SBC LTER: REEF: Macrocystis pyrifera blade area and loss in Southern California
These data describe losses of giant kelp (Macrocystis pyrifera) blade tissue observed in the Santa Barbara Channel (Isla Vista Reef) during the summer of 2012. Data are contained in two tables: 1) a time series of measured change in blade area over time at different depths and locations in the kelp forest, 2) modeled estimates of partial blade losses specific to blades on subsurface fronds and blades in the water column and canopy sections of surface reaching fronds.
SBC LTER: Kelp blade characteristics to support turnover dynamics of giant kelp, Macrocystis pyrifera in the Santa Barbara Channel
These data describe the age-specific changes in the size and related traits of giant kelp (Macrocystis pyrifera) blades in the Santa Barbara Channel (Isla Vista Reef) during the spring and summer of 2012-2013. Data are contained in three tables: 1) a time series of change in blade size with age due to growth and senescence for blades growing at different depths and locations in the kelp forest, 2) physical traits of blades (biomass density, nitrogen, chlorophyll a) of different ages collected at different depths and locations in the kelp forest, and 3) photosynthetic performance (Pmax, alpha) of blades of different ages collected at different depths and locations in the kelp forest. These data were used to test whether spatial variation in the turnover dynamics of giant kelp blades could be could be explained by leaf life span theory developed for higher plants. The data were also used in a mathematical model of frond growth and senescence to investigate the relative and absolute magnitude of blade senescence in a kelp forest, and to examine how these losses were affected by light availability. Analyses using these data were published in Rodriguez, G. E. 2014. Turnover dynamics of the giant kelp, Macrocystis pyrifera. Ph.D., University of California, Santa Barbara. ProQuest, UMI Dissertations Publishing, 2014. 3682967.
Wind turbine blade simulations under changing environment for benchmarking SHM algorithms
<p>This data set contains the flapwise vibration response simulation of a wind turbine blade under <em>Environmental and Operational Variability</em> (EOV) as well as increasing damage. The blade’s dynamics are represented by means of a 4 element FEM of a cantilever beam, while dynamic loading corresponds to a discretized turbulent wind field calculated with the help of the software <em>TurbSim</em> for prescribed 10-minute average wind speed and turbulence. Rotation effects are ignored. The wind loading is coupled with the structural dynamics considering aeroelastic interactions, based on lift and drag forces calculated from a NACA 64-618 airfoil. Ambient temperature (10-minute average) is used to set the elasticity (Young’s) modulus of the blade material.</p> <p>While on the healthy state, the vibration response of the blade is simulated over a year of temperature and wind speed variations according to the average values measured in an area of north-central Switzerland. In addition, a week of extreme weather (abnormally high temperature in summer) and a month where the blade is subject to increasing damage are also simulated. Damage is represented as a decrement of the stiffness on a single FEM element located on the blade’s root. Damage increments linearly from 0 to 25% decrease of the total stiffness during a period of two weeks, while on the remaining two weeks a 25% stiffness decrement is sustained.</p> <p>The main aim of this data set is to be used as a benchmark of vibration based SHM methods, particularly on damage detection and localization under EOV. To this end, both the blade’s vibration response and the environmental and operational parameters (temperature and wind) used to simulate each response are provided. Further details can be found in the publication attached.</p>
Melting glass fibres recovered from wind turbine blades into new glass fibres for wind turbine blades: Dataset
<p><strong><span>Melting glass fibres recovered from wind turbine blades into new glass fibres for wind turbine blades: Dataset.</span></strong></p> <p><span>In the study titled “Melting glass fibres recovered from wind turbine blades into new glass fibres for wind turbine blades”, four different types of glass fibres were manufactured with varying fractions of recycled fibre powder, 0 wt%, 1.64 wt%, 1.90 wt% or 1.96 wt%. Furthermore, these glass fibre types were used to manufacture composite specimens and characterised by static tension tests in fibre and transverse directions. </span></p> <p><span>This dataset is a collection of 13 Excel files.</span></p> <p><span>The “Glass fibre properties and Weibull analysis” Excel file summarise the single fibre tensile testing of glass fibre types and strength analysis using unimodal 2-parameter Weibull theory. There are four sheets in the Excel files for 0 wt%, 1.64 wt%, 1.90 wt% or 1.96 wt% glass fibres. </span></p> <p><span>The Excel files “Single glass fibres-Stress-strain curves-0 %, 1.64 %, 1.90 %, 1.96 %” contains the raw data of individual fibres obtained from the single fibre tensile testing experiments.</span></p> <p><span>There are eight Excel files containing the raw data of static tensile tests in the fibre and transverse direction of the composites made with glass fibres were manufactured with varying fractions of recycled fibre powder, 0 wt%, 1.64 wt%, 1.90 wt% or 1.96 wt%.</span></p>
Research compendium for 'Refitting the Context: A Reconsideration of Cultural Change among Early Homo sapiens at Fumane Cave through Blade Break Connections, Spatial Taphonomy, and Lithic Technology'
<div> <h3>Compendium DOI:</h3> <p><a href="../doi/10.5281/zenodo.10965413">https://zenodo.org/doi/10.5281/zenodo.10965413</a> </p> </div> <p>The content available at the above provided URL will reproduce the results as documented in the publication. Instead, the files hosted at <a href="https://github.com/ArmandoFalcucci/Refitting-The-Context">https://github.com/ArmandoFalcucci/Refitting-The-Context</a> represent the developmental versions and might have undergone modifications since the paper's publication.</p> <div> <h3>Maintainer of this repository:</h3> </div> <p>Armando Falcucci (<a href="mailto:armando.falcucci@uni-tuebingen.de">armando.falcucci@uni-tuebingen.de</a>)</p> <div> <h3>Published paper:</h3> </div> <p>Armando Falcucci, Domenico Giusti, Filippo Zangrossi, Matteo De Lorenzi, Letizia Ceregatti, Marco Peresani. Refitting the Context: Revisiting the Aurignacian sequence at Fumane Cave through blade fragment connections, spatial taphonomy, and lithic technology. <em>Journal of Paleolithic Archaeology</em> (2024). DOI: <a href="https://doi.org/10.1007/s41982-024-00203-0" rel="nofollow">10.1007/s41982-024-00203-0</a></p> <div> <h3>Abstract:</h3> </div> <p>High-resolution stratigraphic frameworks are crucial for unraveling the biocultural processes behind the dispersals of Homo sapiens across Europe. Detailed technological studies of lithic assemblages retrieved from multi-stratified sequences allow archaeologists to precisely model the chrono-cultural dynamics of the early Upper Paleolithic. However, it is of paramount importance to verify the integrity of these assemblages before building explanatory models of cultural change. In this study, multiple lines of evidence suggest that the stratigraphic sequence of Fumane Cave in northeastern Italy experienced minor post-depositional reworking, establishing it as a pivotal site for exploring the earliest stages of the Aurignacian. By conducting a systematic search for break connections between blade fragments and applying spatial analysis techniques, we identified three well-preserved areas of the excavation containing assemblages suitable for renewed archaeological investigations. Subsequent technological analyses, incorporating attribute analysis, reduction intensity, and multivariate statistics, have allowed us to discern the spatial organization of the site during the formation of the Protoaurignacian palimpsest A2–A1. Moreover, diachronic comparisons between three successive stratigraphic units prompted us to reject the hypothesis of techno-cultural continuity of the Protoaurignacian in northeastern Italy after the onset of the Heinrich Event 4. Based on the variability of the lithic and osseous artifacts, the most recent assemblage analyzed, D3b alpha, is now ascribed to the Early Aurignacian, aligning the evidence from Fumane with the current understanding of the development of the Aurignacian across Europe. Overall, this study demonstrates the high effectiveness of the break connection method when combined with detailed spatial analysis and lithic technology, providing a methodological tool particularly amenable to be applied to sites excavated in the past with varying degrees of recording accuracy.</p> <div> <h3>Keywords:</h3> </div> <p>Protoaurignacian; Early Aurignacian; Lithics; Refittings; Assemblage integrity; Spatial analysis; Italy</p> <div> <h3>Overview of contents and how to reproduce:</h3> </div> <p>Within this repository, various folders house data (<code>data</code>), code (<code>script</code>), and output files (<code>output</code>) pertinent to the paper. The data folder encompasses the blank and core datasets from the Aurignacian of Fumane Cave and the dataset of the blade fragment connection study. To replicate the results, download the entire repository and employ <code>Refitting-The-Context.Rproj</code> and open the folder <code>script</code>. For ensuring reproducibility, the <code>renv</code> package (v. 1.0.3) was utilized, following the procedures detailed in its vignette. All analyses and visualizations in the paper were conducted using R 4.3.1 on Microsoft Windows 10.0.19045 (64-bit). As the necessary packages are available in the <code>renv</code> folder, they are not explicitly listed here.</p> <div> <h3>Licenses:</h3> </div> <p>Code: <strong>MIT</strong> <a href="http://opensource.org/licenses/MIT" rel="nofollow">http://opensource.org/licenses/MIT</a>, copyright holder: Armando Falcucci (2024).</p> <p>Data and intellectual work: <strong>Creative Commons Attribution 4.0 International License</strong> (<a href="http://creativecommons.org/licenses/by/4.0/" rel="nofollow">http://creativecommons.org/licenses/by/4.0/</a>), copyright holder: the authors (2024).</p>
Turbine Blade Microstructure
<p>This microstructure has been created using tools and algorithms developed at the Technion, and are part of the IRIT geometric modeling kernel (<a href="https://www.cs.technion.ac.il/~irit/">https://www.cs.technion.ac.il/~irit/</a>).</p> <p>This turbine is a functional composition of trivariate spline tiles inside a macro trivariate shape of a turbine.</p> <p>Model is provided in IGES format, as B-spline surfaces.</p>
Dataset related to the Journal Article 'Efficiency Enhancement of Marine Propellers via Reformation of Blade Tip-Rake Distribution'
<p>This Dataset contains results related to the Graphs shown in the publication titled "Efficiency Enhancement of Marine Propellers via Reformation of Blade Tip-Rake Distribution". The results refer to open water performance curves for the benchmark propeller geometries and the models with optimal tip-rake. In the Folder we provide the data for each figure in a specific folder with the number corresponding to the number of the figure in the published version of the paper. </p>
Wind turbine blade structural health monitoring dataset
<p>The dataset is related to a unique experiment conducted at ETH Zurich in collaboration with the Institute of Fluid Flow Machinery, Polish Academy of Sciences. The synchronisation between fatigue loading and guided wave excitation and sensing is unique. The dataset can be used to construct and test damage indexes for structural health monitoring.</p> <p>The tests were carried out on a Sonkyo Windspot 3.5 kW wind turbine blade equipped with strain gauges, a thermocouple, and five piezoelectric transducers. One piezoelectric transducer was used for Hann windowed sine excitation whereas the remaining piezoelectric transducers were used as sensors. The fatigue loading was induced by using a 1 kN capable Tira shaker. The fatigue program is explained in the readme.txt file and involves overloading the blade with a crane up to the blade's failure. The shaker was excited by a sine signal of frequency around the first resonant frequency of the wind turbine blade. The synchronisation with guided wave excitation was realised during three characteristic moments: (1) at maximum amplitude of sine, (2) at zero crossing, and (2) at the minimum amplitude of sine. This stage of the experiment is called 'dynamic' for short, and the data is stored in respective 'raw' folders. After each set of 1000 cycles, the shaker was stopped until the blade stopped vibrating. Then another set of guided wave measurements was taken at the blade's rest position. This stage of the experiment is called 'static' for short, and the data is stored in respective 'average' folders. It contains signals averaged over 10 measurements. During the whole process strain as well as temperature were measured.</p> <p>Three files are included for data visualization: (1) 'plot_strain_temperature.m', (2) 'read_plot_static.m', and (3) 'read_plot_dynamic.m'. These are MATLAB scripts showing how to load data and visualize the dependence of strains and temperatures on fatigue cycle number or time, plot exemplary signals of guided waves, and construct a damage index for structural health monitoring of the wind turbine blade.</p> <p>The details of experimental setup can be found in the paper.</p>
In Situ Photoluminescence Imaging Dataset of Blade-Coated Perovskite Photovoltaics
<p><strong>Content:</strong></p> <p>The dataset contains time-resolved in situ images acquired during the formation of the perovskite layer which is then built into a perovskite solar cell. The image time series in the dataset encompass the drying and crystallization of the blade-coated perovskite thin-films. An initial exploration of the data presented in the dataset is conducted in the paper <strong><a href="https://doi.org/10.1002/solr.202201114">Process Insights into Perovskite Thin-Film Photovoltaics from Machine Learning with In Situ Luminescence Data</a>.</strong></p> <p>A total of 1,129 solar cells were fabricated using the blade coating deposition method. To monitor the vacuum quenching process of the perovskite layer, a photoluminescence (PL) imaging setup was used to capture four channels of image data. These channels included time series images (2D+t) captured through various spectral filters, with one channel showing reflectance and the other three showing different parts of the PL spectrum. The three PL channels with different spectral transmissions were also used to compute a image time series of spatially resolved PL peak wavelengths. All images were cropped into smaller patches of 65x56 pixels each, which only included the active area of a single solar cell.</p> <p>Different metrics are available as target variables. For each solar cell in the dataset, the photovoltaic performance parameters, namely (1) power conversion efficiency (PCE), (2) open-circuit voltage (<em>V<sub>OC</sub></em>), (3) short-circuit current density (<em>J<sub>SC</sub></em>), and (4) fill factor (FF)), are available (measured backward and forward, as well as the average between forward and backward). Furthermore, information about the perovskite layer thickness of each solar cell’s active area is provided: mean thickness, root-mean-square thickness, and peak-2-valley thickness. Also, additional information like substrate ID and the position of each solar cell within its substrate is provided.</p> <p>All solar cells were fabricated using the same materials, methods, and experimental parameters. As a result, the dataset can be used to apply machine learning techniques to identify variations in the fabrication process between iterations, improve understanding of the process, and predict performance in-line before completing the half-stack into a functional solar cell.</p> <p>Further information on the experimental acquisition procedure can be found in the paper <a href="https://doi.org/10.1002/solr.202201114"><strong>Process Insights into Perovskite Thin-Film Photovoltaics from Machine Learning with In Situ Luminescence Data</strong>.</a></p> <p> </p> <p><strong>Usage:</strong></p> <p>The dataset is made available as a single hdf5-file. The npy-data can be extracted using the notebook “00_extract_data_from_hdf5_file.ipynb” which is provided in the GitHub repository <a href="https://github.com/AI-InSu-Pero/ML-PerovskitePV-InSituLuminescene">https://github.com/AI-InSu-Pero/ML-PerovskitePV-InSituLuminescene</a> </p> <p>The structure of the dataset after extraction from the hdf5-file is depicted below. The dataset (1,129 solar cells) is split into two subfolders, containing train (780 solar cells) and test data (349 solar cell), respectively. For training and test data, the corresponding labels are listed in csv files. In the train and test folders, there are subfolders for each of the substrate assigned to either of the two sets. In the substrate folders, the data for all the patches of a substrate is saved in npy-format with the shape (719, 5, 65, 56), representing (time step, channel, image height, image width). It can be loaded using numpy.load(path_to_file). The order of the five channels is as follows: (0) reflectance, (1) entire PL spectrum, (2) filtered PL spectrum – longer wavelengths remaining, (3) filtered PL spectrum – shorter wavelengths remaining, (4) computed peak wavelength of PL spectrum.</p> <p>In the train folder, an additional folder “cv_splits_5fold” gives the train and validation splits for the 5-fold cross-validation used in the dataset exploration paper. For each fold, the labels are given as csv-files for train and validation split.</p> <p> </p> <pre><code>dataset ├── train │ ├── ACA │ │ ├── 11.npy │ │ ├── 12.npy │ │ ├── 13.npy │ │ ├── 14.npy │ │ ├── 21.npy │ │ └── ... (all other patches of this substrate) │ ├── ACA │ │ ├── 11.npy │ │ ├── 12.npy │ │ ├── 13.npy │ │ ├── 14.npy │ │ ├── 21.npy │ │ └── ... (all other patches of this substrate) │ ├── ... (all other train substrates) │ ├── cv_splits_5fold │ │ ├── fold0 │ │ │ ├── train.csv │ │ │ └── val.csv │ │ └── ... (all other folds) │ └─── labels.csv └── test ├── ACE │ ├── 11.npy │ ├── 12.npy │ ├── 13.npy │ ├── 14.npy │ ├── 21.npy │ └── ... (all other patches of this substrate) ├── ... (all other test substrates) └── labels.csv </code></pre> <p> </p>
Laboratory reflectance spectra and pigments from giant kelp blades
This dataset contains the whole blade reflectance and photosynthetic pigment concentrations from 1700 recently matured blades of giant kelp (Macrocystis pyrifera). The blades were collected at five sites along the California coast from 2012 to 2015.
X-ray CT data: fatigue damage in glass fibre/polyester composite used for wind turbine blades
<p>These data are obtained using a Zeiss Xradia Versa 520 scanner to scan a uni-directional glass fibre reinforced polyester composite made from a non-crimp fabric used for wind turbine blades. The scans were performed to study the fatigue damage progression in this material. The data is published together with the below journal paper, in which more information can be found. The present videos of the data relate directly to the figures in this paper.</p> <p>Jespersen, K. M., Zangenberg Hansen, J., Lowe, T., Withers, P. J., & Mikkelsen, L. P. (2016). <em>Fatigue damage assessment of uni-directional non-crimp fabric reinforced polyester composite using X-ray computed tomography</em>. <em>Composites Science and Technology</em>, <em>136</em>, 94–103. DOI:10.1016/j.compscitech.2016.10.006</p> <p>For use of these data, please remember to cite the above mentioned paper.</p> <p>Corresponding author, K. M. Jespersen, e-mail kmun@dtu.dk</p>
Infrared thermography of turbulence patterns of operational wind turbine rotor blades supported with high-resolution photography: KI-VISIR Dataset
<h2>Abstract</h2> <p><span>With increasing wind energy capacity and installation of wind turbines, new inspection techniques are being explored to examine wind turbine rotor blades, especially during operation. A common result of surface damage phenomena (such as leading-edge erosion) is the premature transition of laminar to turbulent flow on the surface of rotor blades. In the KI-VISIR (Künstliche Intelligenz Visuell und Infrarot Thermografie – Artificial Intelligence-Visual and Infrared Thermography) project, infrared thermography is used as an inspection tool to capture so-called thermal turbulence patterns (TTP) that result from such surface contamination or damage. To compliment the thermographic inspections, high-resolution photography is performed to visualise, in detail, the sites where these turbulence patterns initiate. A convolutional neural network (CNN) was developed and used to detect and localise the turbulence patterns. A unique dataset combining the thermograms and visual images of operational wind turbine rotor blades has been provided, along with the simplified annotations for the turbulence patterns. Additional tools are available to allow users to use the data requiring only basic Python programming skills.</span></p>
Rotor37: a 3D CFD RANS dataset, under geometrical variations of a compressor blade
<p>This dataset contains 3D CFD RANS solutions, under geometrical variations of a compressor blade.</p> <p>A Description is provided in <a href="https://arxiv.org/pdf/2305.12871.pdf">2305.12871.pdf (arxiv.org)</a> Sections 4.1 and Appendix A.1.</p> <p>The file format is PLAID, see the <a href="https://plaid-lib.readthedocs.io/">plaid documentation</a>.</p> <p>The variablity in the samples are 2 input scalars and the geometry (mesh). Outputs of interest are 3 scalars and 3 fields.</p> <p>Eight nested training sets of sizes 8 to 1000 are provided, with complete input-output data. A testing set of size 200 is provided, for which outputs are not provided. </p> <p> </p> <p>Tips to access the data:</p> <p>After decompressing the downloaded file:</p> <p>from plaid.containers.dataset import Dataset<br>from plaid.problem_definition import ProblemDefinition</p> <p>dataset = Dataset()<br>problem = ProblemDefinition()</p> <p>problem._load_from_dir_(os.path.join(/path/to/data,'problem_definition'))<br>dataset._load_from_dir_(os.path.join(/path/to/data,'dataset'), verbose = True)</p> <p>print("problem =", problem)<br>print("dataset =", dataset)</p> <p>sample = dataset[0]<br>print("sample =", sample)</p> <p>for fn in sample.get_field_names():<br> print(f"{fn} =", sample.get_field(fn))<br>for sn in sample.get_scalar_names():<br> print(f"{sn} =", sample.get_scalar(sn))</p> <p>print("nodes =", sample.get_nodes())<br>print("elements =", sample.get_elements())</p>
Wall Resolved Fluid-Structure Interaction Numerical Simulation of a Modern Wind Turbine Blade
<p>Wall-resolved fluid-structure interaction (FSI) numerical simulations of the NREL 5 MW wind turbine blade<br> are compared using two FSI approaches. The first method is based on high-fidelity Nektar++/SHARPy FSI framework,<br> where the fluid governing equations are solved using high-order spectral/hp element method and the turbulent flow is<br> resolved using Large Eddy Simulation (LES) on thick strips, while large-deformation dynamics of the structure are mod-<br> elled using a geometrically exact nonlinear composite beam finite-element model. Thick strip method for the fluid reduces<br> the computational cost by considering a series of smaller domains, each of which has a finite thickness in the spanwise<br> direction. Hence, the overall flow over the blade is treated with a sectional approach, where in each of these sections,<br> strips, the 3D flow is reconstructed locally. Tip-loss correction is used to compensate for the sectional approach over the<br> blade. The second FSI approach is based on OpenFoam/Calculix coupling, where the second-order unstructured finite<br> volume method approach is used for solving the three-dimensional flow equations and the flow turbulence is captured us-<br> ing the k-ω SST model. The structural dynamics are modeled via second-order finite element method using standard solid<br> elements. Effects of the solution fidelity on the prediction of aerodynamic forces as well as on the full three-dimensional<br> flow modelling over the blade versus sectional representation of flow over the blade while incorporating the local three-<br> dimensionality in each section and tip-correction are discussed. Further, significance of two approaches on modelling<br> the slender blade, one using the beam mode and the other utilizing the full 3D solution of structure is addressed. Finally,<br> assessment of computational cost and scalability of the two approaches are presented and discussed.</p>
Improving environmental performances of integrated bladed rotors for aircraft
<p>Supplementary Materials for publication Rupcic et al. 2022, Improving environmental performances of integrated bladed rotors for aircraft</p>
Finite Element model data for Academic Rotor bladed-disc system
<div> <div> <div> <p>A computational finite element based technique is proposed for developing a stochastic reduced order model for rotating bladed disc with spatial random inhomogeneities. The spatial inhomogeneities imply the system to be randomly mistuned. The formulation assumes the availability of a high fidelity finite element (FE) model for the tuned system. The corresponding FE matrices are antisymmetric on account of the Coriolis forces due to rotation. The spatial inhomogeneities, available from limited point measurements on the blades, are modelled as non-Gaussian random fields with arbitrary distributions. A low order stochastic computational model is developed by projecting the FE model onto a reduced dimensional state space defined in terms of specified observable nodal points and expressing the stochasticity through an arbitrary polynomial chaos (aPC) basis. This model enables probabilistic quantification of the variabilities in the system response and estimating failure probabilities. The methodology enables drastic reduction in the state space and stochastic dimensions, addresses the practical difficulties with having limited measurable data points, antisymmetric FE matrices, aPC representation in complex irregular geometries and carrying out probabilistic analyses on industrial systems, at significantly reduced computational costs. The methodology is illustrated through an academic rotor and an industrial rotor blade.</p> </div> </div> </div>
Text-fig. 4. Charred grass from diatomite of Saint-Bauzile. a: Overview of diatomite slab with one larger specimen of charred grass (left) and several smaller, lath-shaped charcoal fragments; SM.B 22260; scale bar = 1 cm. b: Detail of vein exhibited on split grass blade, with stomata oriented parallel to vein. c: Stomata oriented in rows and bands parallel to veins exposed on split grass blade. d: Surface of grass leaf with rectangular, elongated cells with strongly undulating margins in an intercostal area. in Evidence For Wildfires During Deposition Of The Late Miocene Diatomites Of The Konservat-Lagerstätte Lake Saint-Bauzile (Ardèche, France) - Preliminary Results
Text-fig. 4. Charred grass from diatomite of Saint-Bauzile. a: Overview of diatomite slab with one larger specimen of charred grass (left) and several smaller, lath-shaped charcoal fragments; SM.B 22260; scale bar = 1 cm. b: Detail of vein exhibited on split grass blade, with stomata oriented parallel to vein. c: Stomata oriented in rows and bands parallel to veins exposed on split grass blade. d: Surface of grass leaf with rectangular, elongated cells with strongly undulating margins in an intercostal area.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.