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20 results for “fatigue loads”
Data on the material characterization of cast and additively manufactured IN939 subjected to room-temperature low-cycle fatigue load
<p>The original data to the research paper termed "Room-temperature low-cycle fatigue behaviour of cast and additively manufactured IN939 superalloy" are enclosed. Two specimen orientations of L-PBF IN939 - horizontal and vertical, and two thermodynamical states - without subsequent heat treatment (non-treated) and standard aged according to Delargy et al., 1986, were investigated. The paper concerns the low-cycle fatigue performance of cast and additively manufactured IN939 superalloy. It brings a comprehensive account on the damage and deformation behaviour of the tested alloy, combining the test analyses with high-resolution SEM and TEM observations.</p>
Ex-situ X-ray computed tomography data for a non-crimp fabric based fibre composite under fatigue loading
<p>Ex-situ X-ray CT fatigue testing data sets published as a data in brief:</p> <p>"<em>Ex-situ X-ray computed tomography data for a non-crimp fabric based fibre composite under fatigue loading</em>", Data in brief, 2017, doi.org/10.1016/j.dib.2017.10.074.</p> <p>Together with the following article:</p> <p>K. M. Jespersen and L. P. Mikkelsen, “Three dimensional fatigue damage evolution in non-crimp glass fibre fabric based composites used for wind turbine blades,” <em>Compos. Sci. Technol. </em> (In press), 2017, 10.1016/j.compscitech.2017.10.004.</p>
Data for "Using physics-informed neural networks to predict the lifetime of laser powder bed fusion processed 316L stainless steel under multiaxial low-cycle fatigue loading"
<p>Title of dataset: Data for "Using physics-informed neural networks to predict the lifetime of laser powder bed fusion processed 316L stainless steel under multiaxial low-cycle fatigue loading".</p> <p>Name/institution/contact information: Dr. Michal Bartošák, Czech Technical University in Prague - Faculty of Mechanical Engineering, email: michal.bartosak@fs.cvut.cz.</p> <p>Date of data collection: The data were collected between 2021 and 2024.</p> <p>File name structure: The data consists of two files: "316L_fatigue_and_defects.xls," which contains fatigue lifetime data and defect characteristics, and an associated description file, "read_me.txt."</p> <p>See "https://doi.org/10.1016/j.ijfatigue.2024.108608" for the associated article and a detailed description of the methods.</p>
Ex-situ X-ray computed tomography data from two regions of non-crimp fabric based fibre composite under fatigue loading
<p>Ex-situ X-ray CT fatigue testing data published with data in brief: </p> <p>Jespersen, K. M., Glud, J. A., Zangenberg, J., Hosoi, A., Kawada, H., & Mikkelsen, L. P. (2018). <em>Ex-situ X-ray computed tomography, tension clamp and in-situ transilluminated white light imaging data of non-crimp fabric based fibre composite under fatigue loading. Data in Brief.</em></p> <p>as a part of the below journal paper.</p> <p>Jespersen, K. M., Glud, J. A., Zangenberg, J., Hosoi, A., Kawada, H., & Mikkelsen, L. P. (2018). Uncovering the fatigue damage initiation and progression in uni-directional non-crimp fabric reinforced polyester composite. Composites Part A.</p> <p>If using the data, please refer to one of the two.</p>
Tension clamp X-ray CT data of non-crimp fabric based fibre composite under fatigue loading
<p>Tension clamp X-ray CT fatigue testing data published with data in brief: </p> <p>Jespersen, K. M., Glud, J. A., Zangenberg, J., Hosoi, A., Kawada, H., & Mikkelsen, L. P. (2018). <em>Ex-situ X-ray computed tomography, tension clamp and in-situ transilluminated white light imaging data of non-crimp fabric based fibre composite under fatigue loading. Data in Brief.</em></p> <p>as a part of the below journal paper.</p> <p>Jespersen, K. M., Glud, J. A., Zangenberg, J., Hosoi, A., Kawada, H., & Mikkelsen, L. P. (2018). Uncovering the fatigue damage initiation and progression in uni-directional non-crimp fabric reinforced polyester composite. Composites Part A.</p> <p>If using the data, please refer to one of the two.</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>
In-situ transilluminated white light imaging data of non-crimp fabric based fibre composite under fatigue loading
<p>In-situ transilluminated white light imaging data published with data in brief: </p> <p>Jespersen, K. M., Glud, J. A., Zangenberg, J., Hosoi, A., Kawada, H., & Mikkelsen, L. P. (2018). Ex-situ X-ray computed tomography, tension clamp and in-situ transilluminated white light imaging data of non-crimp fabric based fibre composite under fatigue loading.<em> Data in Brief.</em></p> <p>as a part of the below journal paper.</p> <p>Jespersen, K. M., Glud, J. A., Zangenberg, J., Hosoi, A., Kawada, H., & Mikkelsen, L. P. (2018). Uncovering the fatigue damage initiation and progression in uni-directional non-crimp fabric reinforced polyester composite. <em>Composites Part A.</em></p> <p>If using the data, please refer to one of the two.</p>
Sensitivity of Load-Velocity Measures for Detecting Fatigue During Smith-Machine Squats
ClinicalTrials.gov study NCT07307963. IPD Sharing: YES. Countries: 1. Publications: 0.
Effect of Prolonged Military Exercises With High Load Carriage, on Neuromuscular Fatigue and Physiological/Biomechanical Responses
ClinicalTrials.gov study NCT01127191. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Inspiratory Resistive Loading and Diaphragm Fatigue
ClinicalTrials.gov study NCT01505517. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Guanidinoacetic Acid Loading for Chronic Fatigue Syndrome
ClinicalTrials.gov study NCT02213679. IPD Sharing: UNDECIDED. Countries: 1. Publications: 3.
Data from: Fascicles from energy-storing tendons show an age-specific response to cyclic fatigue loading
Some tendons, such as the human Achilles and equine superficial digital flexor tendon (SDFT), act as energy stores, stretching and recoiling to increase efficiency during locomotion. Our previous observations of rotation in response to applied strain in SDFT fascicles suggest a helical structure, which may provide energy-storing tendons with a greater ability to extend and recoil efficiently. Despite this specialization, energy-storing tendons are prone to age-related tendinopathy. The aim of this study was to assess the effect of cyclic fatigue loading (FL) on the microstructural strain response of SDFT fascicles from young and old horses. The data demonstrate two independent age-related mechanisms of fatigue failure; in young horses, FL caused low levels of matrix damage and decreased rotation. This suggests that loading causes alterations to the helix substructure, which may reduce their ability to recoil and recover. By contrast, fascicles from old horses, in which the helix is already compromised, showed greater evidence of matrix damage and suffer increased fibre sliding after FL, which may partially explain the age-related increase in tendinopathy. Elucidation of helix structure and the precise alterations occurring owing to both ageing and FL will help to develop appropriate preventative and repair strategies for tendinopathy.
Data set from a 3D benchmark simulation of delamination in a layered composite component under fatigue loading
<p>This data repository provides the processed data obtained from the benchmark simulation using the method presented in the article entitled "A simulation method for fatigue-driven delamination in layered structures involving non-negligible fracture process zones and arbitrarily shaped crack fronts" by the same authors. The method is based on a cohesive zone model approach and a Paris' law-based expression describing the crack growth rate. The capabilities of the method are evaluated by comparison of the results obtained from a benchmark test on a partially reinforced double cantilever beam (DCB) specimen with varying crack growth rate and front shape. The experimental work is presented in the article entitled "A benchmark test for validating 3D simulation methods for delamination growth under quasi-static and fatigue loading". Experimental data is provided in the Mendeley data set entitled "Experimental data set from a benchmark test of delamination growth with varying crack growth rate and crack front shape under quasi-static and fatigue loading". This data set only contains the simulation results. The test configuration, the methodology and the data processing are addressed in the related articles.</p>
Data from: Fascicles from energy-storing tendons show an age-specific response to cyclic fatigue loading
Open the record for dataset details and reuse information.
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>
Effects of Physical Fatigue on Cognitive Load and Performance Under Dual-Task Environments.
ClinicalTrials.gov study NCT06941701. IPD Sharing: NO. Countries: 1. Publications: 0.
High-Load Blood Flow Restriction (BFR) Training May Decrease Anaerobic Fatigue in Distance Runners
ClinicalTrials.gov study NCT06822595. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Lesion Load and Location in Relation to Cognition, Fatigue and Physical Disability in RRMS
ClinicalTrials.gov study NCT05604092. IPD Sharing: NO. Countries: 1. Publications: 0.
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.
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