Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
23
datasets available to search
ShareScore release 0.9.0
Dataset results
23 results for “fatigue damage”
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>
Data for "High-temperature low-cycle fatigue and fatigue-creep behaviour of Inconel 718 superalloy: Damage and deformation mechanisms"
<p>Title of dataset: Data for "High-temperature low-cycle fatigue and fatigue-creep behaviour of Inconel 718 superalloy: Damage and deformation mechanisms"<br>Name/institution/contact information: Dr. Michal Bartošák, Czech Technical University in Prague - Faculty of Mechanical Engineering, email: michal.bartosak@fs.cvut.cz<br>Date of data collection: The data were collected from the start of 2021 to the end of 2023.<br>File name structure: The data within the folder "SEM" are images of microstructural observations of selected specimens. The data within the folder "FATIGUE_LIFE" include the fatigue lifetimes, as well as the stress and strain amplitudes at mid-life, of all investigated specimens.</p> <p>See https://doi.org/10.1016/j.ijfatigue.2024.108369 for the associated article and a detailed description of the methods.</p>
DATA FOR VERY HIGH FREQUENCY INDUCED FATIGUE DAMAGE
<p>Dataset for publication in which you can find experimental data measured during the quasi-static and dynamic tensile tests and low and high-frequency fatigue tests, and figures describing the structural changes in the material due to the loading. Also, the files providing information about the experimental setup and conditions are coved by the dataset.</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>
Quantifying Fatigue-Damage and Failure-Precursors using Ultrasonic Coda Wave Interferometry
<p>Data sets for the Publication "Quantifying Fatigue-Damage and Failure-Precursors using Ultrasonic Coda Wave Interferometry".</p>
Internal large field of view observations with optical microscope of fatigue damage in composite materials during bending loading
<p>Stitched microscope pictures ...</p> <p>References to this data-set should include a reference to one of the following two papers in where the data has been used.</p> <p>Mortensen U., Andersen, T.L., Mikkelsen, L.P., Observation of Edge Effect in Flexural Fatigue Test of Composites using Large Field of View Microscopy, Journal of Composite Materials. https://doi.org/10.1177/0021998320902233, 2020</p> <p>Mortensen, U.A., Mikkelsen, L.P., Andersen, T.L. Observation of the interaction between transverse cracking and fibre breaks in uni-directional non-crimp fabric composites subjected to cyclic bending fatigue damage mechanism, submitted, Feb. 2022.</p> <p>The naming of the pictures are build up by the follwing elements</p> <p>EL_05 is the plate ID,<br> J01-J09 is the sample ID where the following load level are defined:</p> <ul> <li>J01: 1 000 cycles</li> <li>J02: 2 500 cycles</li> <li>J03: 5 000 cycles</li> <li>J04: 10 000 cycles</li> <li>J05: 50 000 cycles</li> <li>J06: 100 000 cycles</li> <li>J07: 250 000 cycles</li> <li>J08: 500 000 cycles</li> <li>J09: 1 000 000 cycles</li> </ul> <p>S01-04: is the 4 surfaces inside the sample as shown in the picture saved in: xxx. The picture show the sample where the red planes indicate the polished surfaces where the microscopy pictures from the internal surfaces are taken.</p> <p>The pictures are oriented with the compression side upward and the tensile side downward similar to the orientation of the actual 4-point fatigue bending test sample.</p>
Time-lapse helical X-ray computed tomography (CT) data of tensile fatigue damage in GFRP
<p>The X-ray CT data here is published with the paper - </p> <p>Wang, Y.; Mikkelsen, L.P.; Pyka, G.; Withers, P.J. Time-Lapse Helical X-ray Computed Tomography (CT) Study of Tensile Fatigue Damage Formation in Composites for Wind Turbine Blades. <em>Materials</em> <strong>2018</strong>, <em>11</em>, 2340.</p> <p>https://doi.org/10.3390/ma11112340</p> <p>More information about the data and the material can be found in the paper above.</p> <p> </p> <p>If using the data here, please cite the above paper.<em> </em></p> <p>Contact details for author: Ying Wang, ying.wang-4@manchester.ac.uk</p>
Recovery of Performance, Muscle Damage and Neuromuscular Fatigue Following Muscle Power Training
ClinicalTrials.gov study NCT03936595. IPD Sharing: Not stated. Countries: 1. Publications: 6.
Relationship Between Fatigue and Mitochondrial Damage in Patients With HIV/AIDS
ClinicalTrials.gov study NCT00106795. IPD Sharing: Not stated. Countries: 1. Publications: 3.
The fatigue damage evolution in the load-carrying composite laminates of wind turbine blades
<p>Video and data-set behind the figures used in the book chapter:</p> <p>Mikkelsen, LP, The fatigue Damage evolution in the load-carrying composite laminates of wind turbine blades, chapter 16 in “Fatigue life prediction of composites and composite structures”, Edited by A.P. Vassilopoulos, Second edition, Elsevier Ltd, To be published in 2019. <a href="https://doi.org/10.1016/B978-0-08-102575-8.00016-4">https://doi.org/10.1016/B978-0-08-102575-8.00016-4</a>.</p> <p>The files are organised based on the first characters in the filename with fist two numbers and than a "a" for video and "b,c.." for data behind that video. The video have the following link to the figures in the book chapter:</p> <p>Figure 16.19: 03a-Eps05-2-Zoom.mp4 , <a href="https://youtu.be/PynQ0IEyne8">https://youtu.be/PynQ0IEyne8</a><br> Figure 16.21A: 01a-2Dprojection.avi , <a href="https://youtu.be/sr1J_5oc2EE">https://youtu.be/sr1J_5oc2EE</a><br> Figure 16.21B: 02a-Video 3D 360.mpg , <a href="https://youtu.be/riqtI6fARko">https://youtu.be/riqtI6fARko</a><br> Figure 16.25: 11a-Eps05-2.mp4 , <a href="https://youtu.be/RJwfpiiLws0">https://youtu.be/RJwfpiiLws0</a><br> Figure 16.28A: 21a-LFoVexsitu.mp4 , <a href="https://youtu.be/PmUWAI9qfMQ">https://youtu.be/PmUWAI9qfMQ</a><br> Figure 16.28B: 22a-SFoV1G.mp4 ,<a href="https://youtu.be/sFBgVyF1Bfw"> https://youtu.be/sFBgVyF1Bfw</a><br> Figure 16.29: 23a-SFoVGreen.mp4 ,<a href="https://youtu.be/sFBgVyF1Bfw"> </a><a href="https://youtu.be/wJMV9bP5ntw">https://youtu.be/wJMV9bP5ntw</a><br> Figure 16.30: 24a-SFoVRed.mp4 ,<a href="https://youtu.be/sFBgVyF1Bfw"> </a><a href="https://youtu.be/9m1Q0MrzJrY">https://youtu.be/9m1Q0MrzJrY</a><br> Figure 16.31: 25a-RotatingSegmentedDamage.mpg ,<a href="https://youtu.be/sFBgVyF1Bfw"> </a><a href="https://youtu.be/OHPkwwaTUSM">https://youtu.be/OHPkwwaTUSM</a><br> Figure 16.34: 31a-Stained.mpg ,<a href="https://youtu.be/sFBgVyF1Bfw"> </a><a href="https://youtu.be/Sddg0sbzw6w">https://youtu.be/Sddg0sbzw6w</a><br> Figure 16.35: 41a-RLmovie.mpg ,<a href="https://youtu.be/sFBgVyF1Bfw"> </a><a href="https://youtu.be/H7aZrIYjKeQ">https://youtu.be/H7aZrIYjKeQ</a><br> Figure 16.36A: 51a-SFoV-Tension-Compression-T.mpg ,<a href="https://youtu.be/AdsjDkq8QE8"> https://youtu.be/AdsjDkq8QE8</a><br> Figure 16.36B: 52a-SFoV-Tension-Compression-W.mpg , <a href="https://youtu.be/ddSqQnW8jMo">https://youtu.be/ddSqQnW8jMo</a></p>
Edge based large field of view observations with optical microscope of fatigue damage in composite materials during bending loading
<p>Stitched microscope pictures ...</p> <p>References to this data-set should include a reference to the following paper in where the data has been used.</p> <p>[1] Mortensen U., Andersen, T.L., Mikkelsen, L.P., Observation of Edge Effect in Flexural Fatigue Test of Composites using Large Field of View Microscopy, <em>Journal of Composite Materials.</em> <a href="https://doi.org/10.1177/0021998320902233">https://doi.org/10.1177/0021998320902233</a>, 2020</p> <p>The naming of the files is given in the following structure EI_05_A03_kxxx_stitch.tif where</p> <p>EI_05_A03 is the sample ID<br> kxxx indicate after how many cycles the stitched microscopy picture are taken where e.g.</p> <ul> <li>k000 is the initial picture,</li> <li>k001 is after 1 000 cycles </li> <li>...</li> <li>k220 is after 220 000 cycles </li> </ul>
High-dose Ascorbic Acid Intravenous Injection Decreases Mitochondrial DNA Damage in Chronic Fatigue Patients: Randomized-controlled Study
ClinicalTrials.gov study NCT01926132. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Effect of Carbohydrate Consumption on Fatigue and Muscle Damage in Jiu-Jitsu Athletes
ClinicalTrials.gov study NCT03203785. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Phase Angle as a Marker of Muscle Damage, Muscle Fatigue and Inflammation, After Eccentric Exercise
ClinicalTrials.gov study NCT05583500. IPD Sharing: NO. Countries: 1. Publications: 0.
Fatigue Damage Prognosis in FRP Composites by Combining Multi-Scale Degradation Fault Modes in an Uncertainty Bayesian Framework
In this work, a framework for the estimation of the fatigue damage propagation in CFRP composites is proposed. Macro-scale phenomena such as stiffness and strength degradation are predicted by connecting micro-scale and macro-scale damage models in a Bayesian filtering framework that also allows incorporating uncertainties in the prediction. The approach is demonstrated on data collected from a run-to-failure tension-tension fatigue experiment measuring the evolution of fatigue damage in CRFP cross-ply laminates. Results are presented for the prediction of expected end of life for a given panel with the associated uncertainty estimates.
Entropy-based probabilistic fatigue damage prognosis and algorithmic performance comparison
In this paper, a maximum entropy-based general framework for probabilistic fatigue damage prognosis is investigated. The proposed methodology is based on an underlying physics-based crack growth model. V arious uncertainties from measurements, modeling, and parameter estimations are considered to describe the stochastic process of fatigue damage accumulation. A probabilistic prognosis updating procedure based on the maximum relative entropy concept is proposed to incorporate measurement data. Markov Chain Monte Carlo (MCMC) technique is used to provide the posterior samples for model updating in the maximum entropy approach. Experimental data are used to demonstrate the operation of the proposed probabilistic prognosis methodology. A set of prognostics-based metrics are employed to quantitatively evaluate the prognosis performance and compare the proposed method with the classical Bayesian updating algorithm. In particular, model accuracy, precision and convergence are rigorously evaluated in* addition to the qualitative visual comparison.
DYNAMIC STRAIN MAPPING AND REAL-TIME DAMAGE STATE ESTIMATION UNDER BIAXIAL RANDOM FATIGUE LOADING
DYNAMIC STRAIN MAPPING AND REAL-TIME DAMAGE STATE ESTIMATION UNDER BIAXIAL RANDOM FATIGUE LOADING SUBHASISH MOHANTY*, ADITI CHATTOPADHYAY*, JOHN N. RAJADAS**, AND CLYDE COELHO* Abstract. Fatigue damage and its prediction is one of the foremost concerns of structural integrity research community. The current research in structural health monitoring (SHM) is to provide continuous (or on demand) information about the state of a structure. The SHM system can be based on either active or passive sensor measurements. Though the current research on ultrasonic wave propagation based active sensing approach has the potential to estimate very small damage, it has severe drawbacks in terms of low sensing radius and external power requirements. To alleviate these disadvantages passive sensing based SHM techniques can be used. Currently, few efforts have been made towards, time-series fatigue damage state estimation over the entire fatigue life (stage-I, II & III). A majority of the available literature on passive sensing SHM techniques demonstrates the clear trend in damage growth during the final failure regime (stage-III regime) or during when the damage is comparatively large enough. The present paper proposes a passive sensing technique that demonstrates a clear trend in damage growth almost over the entire stage-II and III damage growth regime. A strain gauge measurement based passive SHM frameworks that can estimate the time-series fatigue damage state under random loading is proposed. For this purpose, a Bayesian Gaussian process nonlinear dynamic model is developed to map the reference condition dynamic strain at a given instant of time. The predicted strains are compared with the actual sensor measurements to estimate the corresponding error signals. The error signals estimated at two different locations are correlated to estimate the corresponding fatigue damage state. The approach is demonstrated for an Al-2434 complex cruciform structure applied with biaxial random loading.
Entropy-based Probabilistic Fatigue Damage Prognosis and Algorithmic Performance Comparison
In this paper, a maximum entropy-based general framework for probabilistic fatigue damage prognosis is investigated. The proposed methodology is based on an underlying physics-based crack growth model. V arious uncertainties from measurements, modeling, and parameter estimations are considered to describe the stochastic process of fatigue damage accumulation. A probabilistic prognosis updating procedure based on the maximum relative entropy concept is proposed to incorporate measurement data. Markov Chain Monte Carlo (MCMC) technique is used to provide the posterior samples for model updating in the maximum entropy approach. Experimental data are used to demonstrate the operation of the proposed probabilistic prognosis methodology. A set of prognostics-based metrics are employed to quantitatively evaluate the prognosis performance and compare the proposed method with the classical Bayesian updating algorithm. In particular, model accuracy, precision and convergence are rigorously evaluated in* addition to the qualitative visual comparison. It is shown that the proposed maximum relative entropy methodology has narrower confidence bounds of the remaining life prediction than classical Bayesian updating algorithm.
An Energy-Based Prognostic Framework to Predict Fatigue Damage Evolution in Composites
In this work, a prognostics framework to predict the evolution of damage in fiber-reinforced composites materials under fatigue loads is proposed. The assessment of internal damage thresholds is a challenge for fatigue prognostics in composites due to inherent uncertainties, existence of multiple damage modes, and their complex interactions. Our framework, considers predicting the balance of release strain energies from competing damage modes to establish a reference threshold for prognostics. The approach is demonstrated on data collected from a run-to-failure tension-tension fatigue experiment measuring the evolution of fatigue damage in carbon-fiber-reinforced polymer (CFRP) cross-ply laminates. Results are presented for the prediction of expected degradation by micro-cracks for a given panel with the associated uncertainty estimates.
Integrated fatigue damage diagnosis and prognosis under uncertainties
An integrated fatigue damage diagnosis and prognosis framework is proposed in this paper. The proposed methodology integrates a Lamb wave-based damage detection technique and a Bayesian updating method for remaining useful life (RUL) prediction. First, a piezoelectric sensor network is used to detect the fatigue crack size near the rivet holes in fuselage lap joints. Advanced signal processing and feature fusion is then used to quantitatively estimate the crack size. Following this, a small time scale model is introduced and used as the mechanism model to predict the crack propagation for a given future loading and an estimate of initial crack length. Next, a Bayesian updating algorithm is implemented incorporating the damage diagnostic result for the fatigue crack growth prediction. Probability distributions of model parameters and final RUL are updated considering various uncertainties in the damage prognosis process. Finally, the proposed methodology is demonstrated using data from fatigue testing of realistic fuselage lap joints and the model predictions are validated using prognostics metrics.
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.