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.
10
datasets available to search
ShareScore release 0.7.1
Dataset results
10 results for “Discrete element method”
Validation and Benchmark Dataset for Discrete Element Method Simulations
<p>Verification and Benchmark Dataset for Discrete Element Method Simulations<br>v3 (05/02/2024)<br>Authors: Jose Salomon, Fernando Patino-Ramirez, Catherine O'Sullivan<br>https://doi.org/10.5281/zenodo.10160309<br>Contact: jjs19@ic.ac.uk<br>--------------------------------------------------------------------<br>Description of the repository:</p> <p>This repository contains a collection of datafiles and scripts that can be employed to validate and benchmark new or existing DEM codes. <br>Two validation cases/folders are considered "FCC_packing" and "Rolling_clump". The benchmark dataset is provided in the "Toyoura_sh" folder.<br>All datafiles and scripts are in the corresponding *.zip files. A detailed description of all cases can be found in the related article.</p> <p>In each of these folders, two sub-folders can be found: (1)"Data" and (2)"Scripts". These folders contain:</p> <p>1)"Data": contains the datafiles to perform the validation or benchmark. Two types of data/folders can be found here: "Raw" and "Filtered".<br>The "Raw" folder contains raw data only. The "Filtered" data contains the post-processed data employed to generate the plots found in the related article.<br>Plots in the related article can be reproduced by using the MATLAB files found in the corresponding data folder.</p> <p>2)"Scripts": contains the LAMMPS scripts used to generate the data files contained in "Data".<br>Indications about how to run these scripts can be found in the "README.txt" file in each folder.</p> <p>In order to reproduce the simulations of this repository, LAMMPS must be built including the "GRANULAR" and "RIGID" packages. Please check the README.txt files in each folder for details.</p>
Insight into the Mechanical Coupling Behavior of Loose Sediment and Embedded Fiber-optic Cable using Discrete Element Method
<p>The dataset contains the simulation codes and generated data in the manuscript titled "Insight into the mechanical coupling behavior of loose sediment and embedded fiber-optic cable using discrete element method". The codes (M files) were written in MatDEM, version 3.0 (free access at <strong>www.matdem.com</strong>), and the data is stored in MAT files.</p> <ul> <li>Test2D_2L1.m - codes for initial compacted elements</li> <li>Test2D_2L1.mat - generated data for initial compacted elements</li> <li>Test2D_2L2.m - codes for compacted elements with embedded fiber-optic cable</li> <li>Test2D_2L2.mat - generated data for compacted elements with embedded fiber-optic cable</li> <li>Test2D_2L3.m – codes for confining pressure setting</li> <li>Test2D_2L-0MPa3.mat ~ Test2D_2L-1.0MPa3.mat - generated data for confining pressure setting</li> <li>Test2D_2L4.m – codes for fiber-optic cable pullout tests under various confining pressures</li> <li>Test2D_2L-05-26-20mm-0MPa-un-No1-4.mat ~ Test2D_2L-07-21-20mm-1MPa-un-No1-4.mat - generated data for fiber-optic cable pullout tests under various confining pressures</li> </ul>
Supplementary material for "A Partitioned Finite Element Method for power-preserving discretization of open systems of conservation laws"
<p>This archive contains supplementary material for the paper "A Partitioned Finite Element Method for power-preserving discretization of open systems of conservation laws", containing the source codes for the numerial results presented in the paper. An arXiv pre-print version of the paper is available <a href="https://arxiv.org/abs/1906.05965">here</a>.</p> <p>The following codes are provided:</p> <ul> <li> <p><code>codes/simulation1D_small.jl</code>: small amplitudes (linear) 1D simulation</p> </li> <li> <p><code>codes/simulation1D_large.jl</code>: large amplitudes (nonlinear) 1D simulation</p> </li> <li> <p><code>codes/simulation1D_analytical_gradient</code>: large amplitudes 1D simulation, but using an analytical nonlinear Hamiltonian gradient expression</p> </li> <li> <p><code>codes/simulation2D.jl</code>: large amplitudes (nonlinear) 2D simulation</p> </li> <li> <p><code>codes/convergence1D.jl</code>: convergence analysis of the 1D linear case</p> </li> <li> <p><code>codes/convergence2D.m</code>: convergence analysis of the 2D linear case</p> </li> </ul> <p>A GitHub with the codes and a few instructions on usage is available <a href="http://github.com/flavioluiz/PFEM-article-supplementary-material">here</a>.</p> <p><strong>Acknowledgements</strong></p> <p>This work has been performed in the frame of the Collaborative Research DFG and ANR project INFIDHEM, entitled "Interconnected of Infinite-Dimensional systems for Heterogeneous Media", nº ANR-16-CE92-0028. Further information is available <a href="http://websites.isae-supaero.fr/infidhem/the-project">here</a>.</p>
Buzz pollination: Investigations of pollen expulsion using the discrete element method data
Open the record for dataset details and reuse information.
Simulations scripts for: Strain localization patterns and thrust propagation in 3-D discrete element method (DEM) models of accretionary wedges
<p>High-resolution three-dimensional discrete element method (DEM) simulations of sandbox-scale models of accretionary wedges performed in this study suggest thrusts follow a variety of propagation processes and orientations in the wedges depending on a number of factors including the stage of development of the wedge (precritical vs. critical), basal friction, and type of thrust (forward vs. backward-vergent). In terms of propagation processes, two clear mechanisms are identified. The first involves propagation from the decollement to the wedge top, similar to the standard model of thrust propagation seen in many kinematic models, and in the second, thrusts grow downward from an initial nucleation point just below the top surface of the wedge as well as upward from the decollement joining in the middle. In terms of orientation, forward-vergent thrusts initially form at Roscoe or Arthur orientations, and over shortening, form at Coulomb orientations. To arrive at these results, a wide array of continuum parameters and fields were extracted from the granular assembly of the DEM simulations, including stress, strain, strain rate, kinetic energy, Mohr-Coulomb parameters, and proximity to yielding using the Drucker-Prager criterion to visualize thrust nucleation and propagation. Lastly, the advantages and disadvantages of these continuum proxies for discerning failure in the granular assembly are considered, and the spatial and temporal relationship between proximity to yielding and strain localization (both pre-peak and persistent shear banding) in the granular model of an accretionary wedge is explored.</p>
Artificial Sphere Clusters Transported on a Conveyor Belt Simulated by a Discrete Element Method
<p>This data set comprises sphere clusters that model particles of construction and demolition waste consisting of brick and sand lime brick while they were transported on a conveyor belt. The motion behavior of the sphere clusters was obtained by a discrete element method (DEM) model of the small-scale optical belt sorter Tablesort.</p> <p>The files contain mid-points, radii, and classes of spheres that build sphere clusters, with each cluster modeling a<br> particle. Input images are generated by rendering the sphere clusters in the camera field of view of the simulated area-scan camera. The particles have diameters from 4 to 8 mm. The simulated mass flows were 70 g per s for sand-lime brick and 30 g per s for brick. The belt velocity is approximately 1.1 m per s. Data is recorded at 2000 fps and covers a simulated time period of 120 s.</p> <p>A detailed description of the DEM model can be found in</p> <ul> <li>Albert Bauer, Georg Maier, Marcel Reith-Braun, Harald Kruggel-Emden, Florian Pfaff, Robin Gruna, Uwe Hanebeck, Thomas Längle,<br> <strong>Towards a Feed Material Adaptive Optical Belt Sorter: A Simulation Study Utilizing a DEM-CFD Approach</strong>, Powder Technology, October 2022.</li> </ul> <p> A thorough description of the Tablesort system can be found in</p> <ul> <li><em>Georg Maier, Florian Pfaff, Christoph Pieper, Robin Gruna, Benjamin Noack, Harald Kruggel-Emden, Thomas Längle, Uwe D. Hanebeck, Jürgen Beyerer,</em><br> <strong>Experimental Evaluation of a Novel Sensor-Based Sorting Approach Featuring Predictive Real-Time Multiobject Tracking</strong>,<br> Transactions on Industrial Electronics, February 2020.</li> <li>See also the <a href="https://www.iosb.fraunhofer.de/en/projects-and-products/inside-schuettgut.html">project website</a>.</li> </ul> <p>To this date, publications that used this data include</p> <ul> <li> <p><em>Marcel Reith-Braun, Albert Bauer, Maximilian Staab, Florian Pfaff, Georg Maier, Robin Gruna, Thomas L</em><em>ä</em><em>ngle, </em><em>Jü</em><em>rgen Beyerer, Harald Kruggel-Emden</em><em>, and</em><em> Uwe D. Hanebeck,</em><br> <strong>GridSort: Image-based Optical Bulk Material Sorting Using Convolutional LSTMs</strong>,<br> <em>2</em><em>2nd</em><em> IFAC World Congress</em>, Yokohama, Japan, July 2023.</p> </li> </ul> <p>The used CSV format uses semicolons as separators. There is no header. The first three columns correspond to the x-, y-, and z-coordinate of each sphere in meters, with increasing x-coordinate in the transport direction. The fourth column contains the radii in meters, the fifth column the particle class (2 for sand-lime brick and 1 for brick), and the sixth column the particle ids to which the spheres belong. The file name encodes the simulated time in steps of 0.5 ms. Each file contains as many rows as spheres are visible in the respective time step. The field of view of the simulated camera is 0.442 m - 0642 m in the x-direction and 0.005 m - 0.145 m in the y-direction.</p> <p><strong>Acknowledgment</strong></p> <p>The IGF project 20354 N of the research association Forschungs-Gesellschaft Verfahrens-Technik e.V. (GVT) was supported via the AiF in a program to promote the Industrial Community Research and Development (IGF) by the Federal Ministry for Economic Affairs and Climate Action on the basis of a resolution of the German Bundestag.</p>
Discrete Element Method model results of fracturing and displacements above laccolith intrusions
<p>This data set contains scripts and data in support of the publication: Morand A., Poppe S., Harnett C., Cornillon A., Heap M., Mège D. (2023). Fracturing and dome-shaped surface displacements above laccolith intrusions: Insights from Discrete Element Method modeling. <em>submitted</em>, Journal of Geophysical Research: Solid Earth, July 2023.</p> <p>The data set is composed of results of two-dimensional (2D) Discrete Element Method (DEM) modeling performed by Morand et al., with the licensed commercial <em>Particle Flow Code </em>2D version 7.0 (PFC2D 7.0) from Itasca Consulting Group, Ltd. The DEM modeling simulates a new injection of magma into a preexisting laccolith intrusion and tracks the effect of host rock toughness, stiffness and the source depth. The data set corresponds to exported DEM particle and crack properties of the model results into ASCII files (.txt) and processed particle maximum finite shear strain calculated from particle displacements.</p>
Simulations scripts for: Strain localization patterns and thrust propagation in 3-D discrete element method (DEM) models of accretionary wedges
Open the record for dataset details and reuse information.
Data from: Investigation for the influence mechanism of rock damage on rock fragmentation and cutting performance by the discrete element method
Rock damage is one of the key factors in the design and model choice of the mining machinery. In this paper, the influence of rock damage on rock fragmentation and cutting performance was studied using PFC2D. In PFC2D software, it is feasible to get rock models with different damage factors by reducing the effective modulus, tensile and shear strength of bond by using the proportional factors. A linear relationship was obtained between the proportion factor and damage factor. Furthermore, numerical simulations of rock cutting with different damage factors were carried out. The results show that with the increase of damage factor, the rock cutting failure mode changes from tensile failure to brittle failure, accompanied by the propagation of macro crack, the formation of large debris and a notable decrease in the peak cutting force. The mean cutting force is negatively correlated with the damage factor. Besides, the instability of cutting force was evaluated by the fluctuation index (FI) and the pulse number (PN) of unit displacement. It is found that the cutting force is quite stable when the damage factor is 0.3, which facilitates the reliability of cutting machines. Finally, the cutting energy consumption of rock cutting with different damage factors was analyzed. The results reveal that the increase of damage factor can raise the rock cutting efficiency. The aforementioned findings play a significant role in the development of assisted rock-breaking technologies and the design of cutting head layout of mining machinery.
Data from: Investigation for the influence mechanism of rock damage on rock fragmentation and cutting performance by the discrete element method
Open the record for dataset details and reuse information.
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.