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524 results for “blade”
Text-fig. 5. Reconstruction drawing of Pterigophycos sp. thallus growing on a rock surface (blades slightly simplified, details deemphasized). in A Whole-Plant Specimen Of The Marine Macroalga Pterigophycos From The Eocene Of Bolca (Veneto, N-Italy)
Text-fig. 5. Reconstruction drawing of Pterigophycos sp. thallus growing on a rock surface (blades slightly simplified, details deemphasized).
Text-fig. 4: Pterigophycos sp., details of specimen in Text-fig. 3a. a: Blades B5–7; b: Close-up of (a), focusing on attachment of small blades B5–7 to holdfast structure; c: Detail of holdfast with several linear elements extending from proximal portion; d: Detail of blade B2, showing midrib and spathulate lamina segments; e: Detail of blade B1, showing lowermost, smallest lamina segments; f: tiny bivalve shell on stipe of blade B1, scale bar = 5 mm; g: Detail of blade B2, showing proximal beginning of lamina segmentation. Scale bars = 1 cm unless otherwise stated. in A Whole-Plant Specimen Of The Marine Macroalga Pterigophycos From The Eocene Of Bolca (Veneto, N-Italy)
Text-fig. 4: Pterigophycos sp., details of specimen in Text-fig. 3a. a: Blades B5–7; b: Close-up of (a), focusing on attachment of small blades B5–7 to holdfast structure; c: Detail of holdfast with several linear elements extending from proximal portion; d: Detail of blade B2, showing midrib and spathulate lamina segments; e: Detail of blade B1, showing lowermost, smallest lamina segments; f: tiny bivalve shell on stipe of blade B1, scale bar = 5 mm; g: Detail of blade B2, showing proximal beginning of lamina segmentation. Scale bars = 1 cm unless otherwise stated.
Text-fig. 3. a: Pterigophycos sp., whole-plant specimen, coll. No. 22.116, larger blades denoted B1–4 (for details, see text), scale bar = 2 cm; b: Close-up of blades B1 and B2, which resemble P. spectabilis A.MASSAL. and P. canossae A.MASSAL., respectively; c: Closeup of blade B3 resembling P. canossae; d: Close-up of blade B4 resembling P. gazolanus A.MASSAL. or Laminarites irideaephyllus A.MASSAL. Scale bars = 1 cm unless otherwise stated. in A Whole-Plant Specimen Of The Marine Macroalga Pterigophycos From The Eocene Of Bolca (Veneto, N-Italy)
Text-fig. 3. a: Pterigophycos sp., whole-plant specimen, coll. No. 22.116, larger blades denoted B1–4 (for details, see text), scale bar = 2 cm; b: Close-up of blades B1 and B2, which resemble P. spectabilis A.MASSAL. and P. canossae A.MASSAL., respectively; c: Closeup of blade B3 resembling P. canossae; d: Close-up of blade B4 resembling P. gazolanus A.MASSAL. or Laminarites irideaephyllus A.MASSAL. Scale bars = 1 cm unless otherwise stated.
Radar-based sensing of wind turbines blades based on 35 GHz FMCW sensors installed at operational wind turbine towers
<p>The dataset contains radar-based measurements of rotor blades from three operational wind turbines as part of a structural health monitoring system. For this purpose, a sensor box with a 35 GHz radar sensor (about 1 000 measurements per second) and a camera system (about 100 images per second), is mounted on each wind turbine tower at approximately 100 m height. In order to distinguish individual rotor blades, a machine-readable marker printed on a self-adhesive film was applied on the blade’s surface. When a rotor blade passes the sensor, the camera captures an image of the marker while the radar records a measurement. The marker is then identified and the recorded data is assigned to a particular rotor blade. The measurements demonstrate that the damage detection methodology can be transferred to an image processing problem. The challenge is to manage the strong influence from variable environmental and operational conditions, e.g. wind speed, azimuth orientation, that modify the rotor blade appearance in the radargram significantly. The dataset contains measurements from the intact turbine blade conditions, because it was not possible to introduce structural damage.</p>
Dataset: Blade Air Mobility, Inc. (BLDEW) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Blade Air Mobility, Inc. (BLDE) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Figure 2 in Allometric equations for estimating the leaf area of Thespesia populnea by linear dimensions of leaf blades
Figure 2. Linear leaf dimensions [maximum length (L) and maximum width (W)] used to estimate the leaf area of Thespesia populnea.
Figure 1 in Allometric equations for estimating the leaf area of Thespesia populnea by linear dimensions of leaf blades
Figure 1. Geographical location of the municipality of Canguaretama, state of Rio Grande do Norte, Northeastern Brazil.
Рис. 5. ΔенΑрограмма меры разброса значений ΑΛя показатеΛей вΛияния опушенности и тоΛщины Λистовой пΛастинки картофеΛя на прожорΛивость Λичинок картофеΛьной коровки Fig. 5. Dendrogram representing the value scatter for the influence of pubescence and thickness of the potato leaf blade on the voracity of potato ladybug larvae in Role of potato immune factors in the trophic responses of Henosepilachna vigintioctomaculata Motschulsky, 1858
Рис. 5. ΔенΑрограмма меры разброса значений ΑΛя показатеΛей вΛияния опушенности и тоΛщины Λистовой пΛастинки картофеΛя на прожорΛивость Λичинок картофеΛьной коровки Fig. 5. Dendrogram representing the value scatter for the influence of pubescence and thickness of the potato leaf blade on the voracity of potato ladybug larvae
Repository of WESC2017 paper data - Benefits of subcomponent testing over full- scale blade testing elaborated on a trailing-edge bond line design
<p>Title:<br> <a href="https://doi.org/10.5194/wes-3-1-2018">Benefits of subcomponent over full-scale blade testing elaborated on a trailing-edge bond line design validation</a></p> <p>Authors:<br> <a href="mailto:malo.rosemeier@iwes.fraunhofer.de">Malo Rosemeier</a>, Gregor Basters, and Alexandros Antoniou</p> <p>Affiliation:<br> Division Structural Components, Fraunhofer IWES, Fraunhofer Institute for Wind Energy Systems, Am Seedeich 45, 27572 Bremerhaven, Germany</p> <p>The following data used for the study is provided:</p> <ul> <li> data_blade.zip contains input data for fusedwind-dev (https://github.com/FUSED-Wind/fusedwind-dev) of the DTU10MW blade.</li> <li>data_becas.zip contains input/ output data obtained for/ from BECAS with BECASWrapper (https://gitlab.windenergy.dtu.dk/HAWTOpt2/BECASWrapper).</li> <li>data_rfoil.zip contains polars obtained from Rfoil.</li> <li>data_feproc.zip contains the ANSYS APDL models obtained with FEPROCWrapper (https://gitlab.iwes.fraunhofer.de/git/bdt/FEPROCWrapper).</li> <li>*.sqlite data bases contain all results data which can be accessed with the `post.py` script.</li> <li>result_plots.zip contain the results generated by the `post.py` script.</li> </ul> <p>For further details see the manuscript.</p>
Observations of microscale tensile fatigue damage mechanisms of composite materials for wind turbine blades
<p>A scout and zoom dataset including video-versions of the figures behind the following paper to where the references should be given:</p> <p>Mikkelsen, L.P. Observations of microscale tensile fatigue damage mechanisms of composite materials for wind turbine blades, IOP Conf. Series: Materials Science and Engineering <strong>380</strong> (2018) 012006 , http://iopscience.iop.org/article/10.1088/1757-899X/388/1/012006.</p> <p>The SFoV data-set is saved as both a 3D and a 2D (zipped) tiff stack.</p>
SiWiRoRa - Simulated Wind-turbine Rotor-blade Radargrams
<h1>SiWiRoRa</h1> <p>SiWiRoRa stand for '<strong>Si</strong>mulated <strong>Wi</strong>nd-turbine <strong>Ro</strong>tor-blade <strong>Ra</strong>dargrams'.</p> <h2>Overview</h2> <p>A novel kind of dataset comprised of 9504 grayscale images (quadratic, 224 px) representing simulated radargrams. SiWiRoRa enables analytical machine-learning experiments in the emerging area of radar-based computer-vision research on wind-turbine rotor blades. Here, radargrams are images showing distance on the horizontal x-axis (increasing from left to right) and time on the vertical y-axis (increasing from top to bottom) as well as reflected intensity given by the colorscale (increasing from black to white).</p> <h2>Details</h2> <p>Geometries have been modeled using Cyberbotics Webots (1188 different configurations) and stochastic elements have been added in post-processing (additional 8 independent repetitions per 1 configuration). Besides this aforementioned stochastic noise, the dataset has a full-factorial design consisting in... </p> <ul> <li>9 distinct levels of rotor speeds (clockwise rotation when viewed from the exterior onto the rotor and the radar behind it)</li> <li>11 levels of the yaw angles (between radar direction and nacelle orientation)</li> <li>6 variations of wind pressure (forcing the rotor closer to the radar)</li> <li>2 different time offsets (corresponding to alternative triggers of the radar)</li> </ul> <p>Geometries have been chosen so as to yield non-axisymmetrical radargrams (even apart from noise).</p> <h2>Acknowledgements</h2> <p>The present dataset complements and has been inspired by the field measurements published under</p> <p>https://doi.org/10.5281/zenodo.8366654</p> <p>(Hyperlink is provided in the References section).</p>
Ambient vibration test of wind turbine blade in OWI-lab's Large Climate Chamber
<p><strong>Ambient vibration test of wind turbine of wind turbine blade in OWI-lab's Large Climate Chamber</strong></p> <p>Selected data from the large scale icing experiment as conducted in OWI-lab's large climate chamber on 15/11/2022. Results were presented during Eurodyn 2023 in :"Large scale test of vibration based icing detection for wind turbines", Weijtjens et.al. </p> <p><em>Data is (summarized, more details are given below):</em></p> <p>- 24 Ten minute acceleration data files collected (MO04_acceleration_YYYYmmdd_HHMMSS.csv) <br> - Pictures during the experiment timestamped (local time: UTC+1)<br> - Temperature measurements of the climate chamber's inflow temperatures<br> - Modal parameter results for X and Z direction </p> <p>All times are in UTC unless mentioned otherwise.</p> <p><strong>Measurement concept</strong></p> <p>The measurement data is collected during an experiment as conducted as part of the <a href="https://www.sirris.be/nl/joint-project/fighting-icing">COOCK fighting icing </a> project led by Sirris. In the OWI-lab climate chamber a wind turbine blade was subjected to icing conditions. The test comprises the collection of ambient vibration data using three tri-axial accelerometers installed on the blade. During the day the blade is cooled and cold water is sprayed on the blade to simulate the growth of ice on the blade. The steps of the experiment are:</p> <pre><code>2022-11-15 10:04:00+00:00: Start cooling to -10°C 2022-11-15 11:23:00+00:00: Start spray 2022-11-15 12:13:00+00:00: Accelerate spray 2022-11-15 12:49:00+00:00: End of spray 2022-11-15 13:13:00+00:00: Start heating 2022-11-15 14:14:00+00:00: Start cooling to -10°C 2022-11-15 15:03:00+00:00: Start spray 2022-11-15 15:43:00+00:00: End of spray</code></pre> <p>For more information on the </p> <p><strong>Ten minute acceleration data</strong></p> <p>Twentyfour ten minute samples of the 3 accelerometers on the blade sampled at 250Hz. The data is two 2-hour blocks, one at night before the testing, the second 2 hour block is during the spraying.</p> <p>The 10.1m long blade was instrumented with three tri-axial MEMS accelerometers (Micromega IAC-UHRS-Ud-03, ±3g) on the suction side of the blade. In which the X-direction corresponded to the edgewise motion of the blade, the Z-direction to the flapwise direction and the Y-direction to the less relevant lengthwise motion. The three sensors were installed at approximately 1/4, 5/8 of the blade length and 130cm from the tip of the blade.</p> <p><strong>Modal parameter data</strong></p> <p>The resulting modal parameter data ( for the entire day of testing) , in both X and Z direction are provided in MO04_mpe_*_20221115.csv. The data has following shape:</p> <table> <thead> <tr> <th> </th> <th>mean_frequency</th> <th>std_frequency</th> <th>mean_damping</th> <th>std_damping</th> <th>size</th> <th>algorithm</th> <th>timestamp</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1.649219</td> <td>0.001952</td> <td>1.128411</td> <td>0.103419</td> <td>51</td> <td>lscf</td> <td>2022-11-15 00:00:00+00:00</td> </tr> <tr> <th>1</th> <td>2.028879</td> <td>0.000927</td> <td>0.432596</td> <td>0.068354</td> <td>60</td> <td>lscf</td> <td>2022-11-15 00:00:00+00:00</td> </tr> <tr> <th>2</th> <td>2.923999</td> <td>0.009472</td> <td>2.677156</td> <td>0.615895</td> <td>19</td> <td>lscf</td> <td>2022-11-15 00:00:00+00:00</td> </tr> <tr> <th>3</th> <td>3.779268</td> <td>0.003649</td> <td>3.462347</td> <td>0.715628</td> <td>7</td> <td>lscf</td> <td>2022-11-15 00:00:00+00:00</td> </tr> <tr> <th>4</th> <td>5.786029</td> <td>0.005541</td> <td>0.539010</td> <td>0.261049</td> <td>7</td> <td>lscf</td> <td>2022-11-15 00:00:00+00:00</td> </tr> </tbody> </table> <p>In which `mean_frequency` and `std_frequency` are the cluster mean frequency, are the cluster std. frequency and cluster std. damping and the cluster size (size). The LCSF algorithm is used. The algorithm used is described in <a href="https://journals.sagepub.com/doi/abs/10.1177/1475921714556568?journalCode=shma">Source</a>.</p> <p>Note: multiple rows share one timestamp, this is because the algorithm can detect multiple modes per timestamp.</p> <p><strong>Temperature data</strong></p> <p>The inflow air temperatures are shared in a separate .csv files: ClimateChamber_20221115.csv</p> <p><strong>Pictures</strong></p> <p>Picture are collected during the test are shared. Each picture is timestamped in local time (UTC+1)</p> <p> </p>
Finite Element model data for Academic Rotor bladed-disc system
Open the record for dataset details and reuse information.
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>
Embolus blade shaped in ventral view (I2) in An of Zelotibia (Araneae, Gnaphosidae), a spider genus with a species swarm in the Albertine Rift
Embolus blade shaped in ventral view (I2)
Supplemental Material to Article "A practical approach for the peel stress prediction in the trailing-edge adhesive joint of wind turbine blades"
<p>This set supplements the figure data to the article "A practical approach for the peel stress prediction in the trailing-edge adhesive joint of wind turbine blades", DOI: .</p>
Blade (20234)
A flint blade. Percussion flaked. Orange brown color. Retouched. Flat on one side and worked on the other side. https://stanford.pastperfectonline.com/webobject/76B9A2F9-93EE-4230-94E6-748075409511 Source: Objaverse 1.0 / Sketchfab
Blade (20238)
A flint blade. Percussion flaked. Tan to dark brown color. Retouched. Flat on one side, worked on the other. https://stanford.pastperfectonline.com/webobject/1C86E547-3C05-4D97-BF84-715751066648 Source: Objaverse 1.0 / Sketchfab
Hardaway Blade (690a471)
**Hardaway Blade spear point** Location: Hardaway site (31St4), Stanly County, North Carolina. Period: Late Paleoindian (8500-7900 BC). Material: metavolcanic rock. Dimensions: length, 67.5 mm; width, 34.8 mm; thickness, 8.3 mm. Notes: Catalog no. 690a471, North Carolina Archaeological Collection, Research Laboratories of Archaeology, University of North Carolina at Chapel Hill. Illustrated in *The Formative Cultures of the Carolina Piedmont,* by Joffre L. Coe, Transactions of the American Philosophical Society vol. 54, pt. 5, 1964, Figure 56. Model by Stephanie Grant and Steve Davis. Source: Objaverse 1.0 / Sketchfab
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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.