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.
6
datasets available to search
ShareScore release 0.9.0
Dataset results
6 results for “Brittle fracture”
Correlation between rock brittleness and fracturing
<p>Birttleness and fracturing behavior are two fundamental properties of rock. Here the brittleness of three marbles were determined by the data of various tests. The fracturing behavior of three marble were analysed in microscale under the mode I fracturing test. </p>
Data from: Brittle sedimentary strata focus a multimodal depth distribution of seismicity during hydraulic fracturing in the Sichuan basin, southwest China
<p>The number of background earthquakes (<em>M<sub>L</sub></em> ≥ 0) in the southern Sichuan basin, southwest China, has increased thirtyfold as a result of hydraulic fracturing. Background events are originally deep (4-6 <em>km</em>) within the sedimentary section but build into a multimodal distribution both at depth and in the shallow stimulated reservoir (2-4 <em>km</em>) - representing a counterpoint to the usual triggering of seismicity on deep sub-reservoir basement faults. Surprisingly, the largest events (<em>M<sub>L</sub></em> ≥ 3) evolve in the deep sedimentary strata (4-6 <em>km</em>) that are hydraulically isolated from the injection zone (2-4 <em>km</em>) by low permeability layers. We evaluate the friction-stability rheology of the strata within the full stratigraphic section to define the feasibility of nucleation within these shallow and deep strata. These show velocity-neutral to velocity-weakening behavior in the shallow reservoir transitioning to more strongly velocity-weakening with increase in both depth and temperature. Poroelastic stress calculations confirms that stress transfer, rather than transmitted fluid pressures, are capable of directly reactivating critically-stressed faults at depth, with fluid pressures the triggering source within the shallow reservoir.</p>
Water weakening and the compressive brittle strength of carbonates: influence of fracture toughness and static friction
<p>This is the ReadMe file corresponding to the study entitled:<br>"Water weakening and the compressive brittle strength of carbonates: influence of fracture toughness and static friction"<br>By Noël C., Fryer B., Baud P. and Violay M.</p> <p>This Read-Me file has been last edited in January 2024</p> <p>This readme file describes the data repository files accompanying the above publication published in IJRMMS. <br>For any further queries please contact corentin.noel@geoazur.unice.fr</p> <p>For each type of experiments (uniaxial compression, fracture toughness and friction), a zip file contains the the file of the experiments. Each file name contain the information of the sample tested and the experimental condition (dry or saturated):<br>- The first leter(s) stands for the limestone tested: In=Indiana, L=Leitha, S=Solnhofen, St=Saint Maximin, T=Tavel.<br>- The number following is for sample referencing.<br>- The next letter is for the experiment performed: U=Uniaxial compression, K=Fracture toughness, F=Friction<br>- After a "_", the experimental conditions are described: dry=dray experiments, sat=water saturated experiments</p> <p><br>*** Uniaxial compression ***<br>Each .txt file contains:<br>- Column 1: Axial stress (MPa)<br>- Column 2: Axial strain (-)<br>- Column 3: Radial strain (-)<br>- Column 4: Volumetric strain (-)</p> <p>Additionally, each experiments also have an accoustic file (.txt ending with _AE) containing:<br>- Column 1: Axial strain (-)<br>- Column 2: Cumulative AE number</p> <p>*** Fracture toughness ***<br>Each .txt file contains:<br>- Column 1: Axial displacement (mm)<br>- Column 2: Mode I stress intensity factor KI (MPa.m(1/2))<br>- Column 3: Crack mouth openning (mm)</p> <p>*** Friction ***<br>Each .txt file contains:<br>- Column 1: Time (s)<br>- Column 2: Shear stress (MPa)<br>- Column 3: Normal stress (MPa)<br>- Column 4: Shear displacement (mm)</p> <p>Additionally, for each experiment and both under dry and water saturated conditions a .xlsx file contains the picked static shear stress at the onset of sliding for each tested normal stress as follow:<br>- Column A: Normal stress (MPa)<br>- Column B: Shear stress at onset of sliding (MPa)<br>- Column C: Shear stress at onset of sliding lower boundary (MPa)<br>- Column D: Shear stress at onset of sliding higher boundary (MPa)<br>- Column E: Uncertainty on the shear stress at onset of sliding (MPa)</p>
A comparative assessment of different adaptive spatial refinement strategies in phase-field fracture models for brittle fracture
<p><strong>Abstract:</strong></p> <p>(from [1])</p> <blockquote> <p>For the smeared approximation of a discrete crack, phase-field fracture simulations of brittle materials require suitable finite element meshes in regions where crack propagation is expected to get an accurate resolution of the phase-field function. The intuitive option is to pre-refine the mesh in regions of the expected crack paths. However, this could lead to very computationally intensive simulations due to the high number of elements. Alternatively, adaptive spatial refinement of the finite element mesh is utilized based on appropriate error indicators to obtain the required accuracy in the areas of crack propagation. Different error indicators can be used: the most common one for phase-field fracture simulations is the threshold-based approach, in which elements are refined depending on the value of the phase-field function. Alternatively, the Kelly error indicator can be used as a criterion for spatial adaptivity. It considers the jumps in the gradients of the phase-field function between the elements. We additionally introduce here an error indicator based on configurational forces, that depend on the Eshelby stress tensor. For mode I loading in linear elastic fracture mechanics, the configurational forces have a close connection to the <span class="math-tex">\(\mathscr{J}\)</span>-Integral and the critical fracture energy <span class="math-tex">\(\mathrm{G}_\mathrm{c}\)</span> , respectively. Therefore, a suitable norm of the configurational forces is introduced as an error indicator here. These three error indicators are introduced and compared to each other in terms of accuracy and efficiency by means of numerical examples for crack growth in the single edge notched shear test.</p> </blockquote> <p><strong>Contact:</strong></p> <p>Maurice Rohracker</p> <p>Institute of Applied Mechanics</p> <p>Friedrich-Alexander-Universität Erlangen-Nürnberg</p> <p>Egerlandstr. 5</p> <p>91058 Erlangen</p> <p><strong>Software:</strong></p> <p>All phase-field fracture simulations were performed with <em>deal.II</em> [2], version 9.2.0, on the HPC cluster <em>Meggie</em> of NHR@FAU. The authors gratefully acknowledge the scientific support and HPC resources provided by the Erlangen National High Performance Computing Center (NHR@FAU) of the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). The hardware is funded by the German Research Foundation (DFG).</p> <p><strong>License:</strong></p> <p>Creative Commons Attribution 4.0 International</p> <p><strong>Context:</strong></p> <p>Dataset supplementing preprint:</p> <p>[1] M.Rohracker, P.Kumar, J.Mergheim, "A comparative assessment of different adaptive spatial refinement strategies in phase-field fracture models for brittle fracture", Forces in Mechanics, 2022, <a href="https://doi.org/10.1016/j.finmec.2022.100157">10.1016/j.finmec.2022.100157</a>.</p> <p>This dataset contains the complete results presented in [1], which include global variables, field variables, and meshes.</p> <p><strong>File structure:</strong></p> <p>The file structure is explained in more detail in the shipped <em>README.md</em> in the dataset folder.</p> <p><strong>References:</strong></p> <p>[1] M.Rohracker, P.Kumar, J.Mergheim, "A comparative assessment of different adaptive spatial refinement strategies in phase-field fracture models for brittle fracture", Forces in Mechanics, 2022, <a href="https://doi.org/10.1016/j.finmec.2022.100157">10.1016/j.finmec.2022.100157</a>.</p> <p>[2] D. Arndt, W. Bangerth, B. Blais, T. C. Clevenger, M. Fehling, A. V. Grayver, T. Heister, L. Heltai, M. Kronbichler, M. Maier, P. Munch, J.-P. Pelteret, R. Rastak, I. Thomas, B. Turcksin, Z. Wang, D. Wells, <strong>The deal.II Library, Version 9.2</strong> Journal of Numerical Mathematics, vol. 28, p. 131-146, 2020.</p>
Data from: Brittle sedimentary strata focus a multimodal depth distribution of seismicity during hydraulic fracturing in the Sichuan basin, southwest China
Open the record for dataset details and reuse information.
Effect of water on sandstone's fracture toughness and frictional parameters: Brittle strength constraints
<p>This is the ReadMe file corresponding to the study entitled:<br> "Effect of water on sandstone’s fracture toughness and frictional parameters: Brittle strength constraints"</p> <p>By Noël C., Baud P. and Violay M.</p> <p>This study has been published in the journal International Journal of Rock Mechanics and Mining Sciences. It is under Under a Creative Commons license CC BY-NC-ND 4.0.<br> doi: 10.1016/j.ijrmms.2021.104916</p> <p>This Read-Me file has been last edited in September 2021</p> <p>This readme file describes the data repository and supplementary files accompanying the above publication. <br> For any further queries please contact corentin.noel@uniroma1.it</p>
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.