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185 results for “Granular”
Controlling rheology via boundary conditions in dense granular flows
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Sponge-like rigid structures in frictional granular packings
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Data from: Multiscale mechanics of granular biofilms
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Data from: Gardner-like crossover from variable to persistent force contacts in granular crystals
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Particle scale anisotropy controls bulk properties in sheared granular materials
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Loading-dependent microscale measures control bulk properties in granular material: an experimental test of the Stress-Force-Fabric relation
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Evolution is coupled with branching across many granularities of life
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Data set: Grain Reynolds number scale effects in dry granular slides
<p>Scale series of velocity, flow depth and run out data for dry granular materials flowing down a slope with side walls. </p>
Dataset for article titled "The influence of packing structure and interparticle forces on ultrasound transmission in granular media"
<p>Dataset containing supporting materials for article titled "The influence of packing structure and interparticle forces on ultrasound transmission in granular media". An enclosed PDF file describes the data.</p>
Simulation data and scripts for CFD-DEM simulation of granular flows in ambient fluid
<ul> <li>Data sets - contains the raw data required to replicate and validate all plots in the main article. Contains folders in containing measurements of basic flow properties, e.g. velocities, shear rates, etc., of monodisperse and bidisperse granular flows in different flow regimes. Each folder contains a ReadMe.txt briefly explaining the content and lay out of each data set.</li> <li>Sample case - a .zip file which includes codes which are needed to simulate a CFD-DEM case of a steady granular flow in water with cyclic boundaries in the stream wise direction. Also enclosed is a ReadMe.txt file detailing the implementation instructions for both Esys particle and OpenFOAM codes. Download links for Esys particle and OpenFOAM are also included.</li> <li>Geo file generator - a .zip file which includes Esys particle codes that can be used to generate a .geo file specifying the initial position of the particles used in the test simulations. A ReadMe.txt file is enclosed with more detailed implementation instructions.</li> </ul>
Experiments on granular flow behavior and deposit characteristics: implications for rock avalanche kinematics
<p>Seven excel files which includes the data supporting Figure 5, Figure 6, Figure 7, Figure 8, Figure 10, Figure 12 and Figure 14 is uploaded saparately.</p> <p>Data Set S1. Data supporting the relationship between flow height and time in Figure 5.</p> <p>Data Set S2. Data supporting normalized velocity profiles and normalized shear rate profiles in Figures 6(a)-6(d).</p> <p>Data Set S3(a). Data supporting the relationships between mean grain size and global shear rate in Figure 7(a).</p> <p>Data Set S3(b). Data supporting the relationships between mean grain size and Savage number in Figure 7(a).</p> <p>Data Set S4(a). Data supporting the relationships between depth averaged velocity and time in Figure 8(a).</p> <p>Data Set S4(b). Data supporting the relationships between depth averaged velocity and time in Figure 8(b).</p> <p>Data Set S4(c). Data supporting the relationships between mean grain size and equivalent friction coefficient in Figure 8(c).</p> <p>Data Set S5(a). Data supporting Figures 10(a) and 10(b).</p> <p>Data Set S5(b). Data supporting the relationships between relative flow height and λ in Figure 10(c).</p> <p>Data Set S5(c). Data supporting the relationships between relative flow height and λ in Figure 10(d).</p> <p>Data Set S6(a). Data supporting the velocity profiles in Figure 12(a).</p> <p>Data Set S6(b). Data supporting the relationships between global shear rate and equivalent friction coefficient in Figure 12(b).</p> <p>Data Set S7. Data supporting the relationships between normalized flow height and Savage number in Figure 14.</p>
Data from: The variation of grain size distribution in rock granular material in seepage process considering the mechanical-hydrological-chemical coupling effect: An experimental research
<p>As a common solid waste in geotechnical engineering, rock granular material should be properly treated and recycled. Rock granular material often coexists with water when it is used as the filling material in geotechnical engineering. Water flowing in rock granular materials is a complex progress with the mechanical-hydrological-chemical (MHC) coupling effect, i. e. the water scours in the gaps and spaces in the rock granular material structure, produces chemical reactions with rock grains, rock grains squeeze each other under the water pressure and compression leading re-breakage and producing secondary rock grains, the fine rock grains are migrated with water and rushed out. In this process, rock grain size distribution (GSD) changes, it affects the physical and mechanical characteristics of the rock granular materials, and even influences the seepage stability of the rock granular materials. To study the variation of GSD in the rock granular material considering the MHC coupling effect after the seepage process, seepage experiments of rock grain samples are carried out and analyzed in this paper. The result is expected to have a positive impact on further studies of the properties of the rock granular material.</p>
NoT.JS: Blocking Tracking JavaScript at the Function Granularity
<p>Anon</p>
Dataset for the seismically monitored experiments of free-fall granular masses
<p>Datasets related to the paper "Experiments on Landquakes Generated by Free-fall Granular Masses: Implications for Rockfall Impacting Dynamics", submitted to <em>Earth and Space Science</em>.</p> <p>S1_images_the dynamic evolution of the free-fall granular masses tracked by a high-speed camera.</p> <p>S2_data_ data of vertical acceleration signals for all tests recorded by an accelerometer.</p> <p>S3_data_ data of the extracted seismic parameters including maximum seismic amplitude, mean frequency and radiated seismic energy for all tests.</p> <p>S4_data_ data supporting the relationships between intermediate functions associated with the maximum seismic amplitude, mean frequency and seismic energy and number of particles.</p> <p>S5_data_ data supporting the successive velocity profiles and acceleration profiles along the vertical direction of the granular mass.</p> <p>S6_data_ data supporting the relationships between the ratio of components in a granular mass contributing to the maximum seismic amplitude with the number of layers.</p> <p>S7_data_data supporting the relationships between intermediate coefficients associated with the maximum seismic amplitude, mean frequency and seismic energy and the number of layers of granular masses.</p> <p>S8_data_ data of spectrogram of condition D5 computed using Stockwell transform based on the acceleration signals recorded by an accelerometer.</p> <p>S9_data_ data supporting the evolutions of horizontal and vertical motion components of granular masses of tests C1-C5 over time.</p> <p>S10_data_ data supporting the relationships between characteristic frequency and the velocity vector of granular masses of series C, D, E and F.</p>
Basal force fluctuations and granular rheology: Linking macroscopic descriptions of granular flows to bed forces with implications for monitoring signals
<p>Herein lies user defined functions, submission scripts, and intitial particle data necessary to build a discrete element solver (using MFiX-DEM version 21.1.1: https://mfix.netl.doe.gov/products/mfix/) and run a monodisperse pure granular flow with 5mm beads in a shear cell configuration. Contained within this dataset are particle velocity and force data for the entire collection of particles ('VELOCITY<em>'* </em>and 'FORCES*.DAT', respectively). This dataset also contains particle-force data recorded at the bottom force plate, writen at 10 kHz. Two MATLAB scripts are included for post processing the force plate data. </p>
Thermal properties of 'athermal' granular materials
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Mechanoreceptive soft robotic molluscoids made of granular hydrogel-based organoelectronics
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Korean embedding files using the different morphological segmentation granularity of the word
<p>Embedding files using the following segmentation:</p> <ol> <li>wordUD</li> <li>morphUD</li> <li>+morphUD </li> </ol> <p>Based on wordUD there are 9,692,938 sentences and 157,653,628 words (tokenized) including all articles published in The Hankyoreh during 2016 (1.2M sentences), Sejong morphologically analyzed corpus (3M), and Korean Wiki (20201101) (5.3M):</p> <p> </p> <blockquote> <p>./fasttext skipgram -input input -output embedding -dim 300</p> </blockquote>
Data for the original research article 'Enhanced anaerobic digestion of dairy wastewater in a granular activated carbon amended sequential batch reactor'
<p>All data that support the findings of the study 'Enhanced anaerobic digestion of dairy wastewater in a granular activated carbon amended sequential batch reactor' published in GCB Bioenergy Journal are openly available in the file.</p>
Oscillations in the granular layer of a cerebellum cortex model
<p>Oscillations in the granular layer of a cerebellum cortex model</p>
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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.