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17 results for “granular materials”
A phenomenological law for complex granular materials from Mohr-Coulomb theory
<p>The compressed directory contains the data in .csv format used for the PCA analysis for each dataset (1, 2 and 3). </p>
CaliParticles: A Benchmark Standard for Experiments in Granular Materials
<p>Granular materials are discrete particulate media that can flow like a liquid but also be rigid like a solid. This complex mechanical behavior originates in part from the particles shape. How particle shape affects mechanical behavior remains poorly understood. Understanding this micro-macro link would enable the rational design of potentially cheap, light weight or robust materials. To aid this development, we have produced a set of standard particle shapes that can be used as benchmarks for granular materials research. Here we describe the collection of benchmark shapes. Some part of the particles are modeled on superquadrics, others are custom designed. The particles used so far were made from polyoxymethylene (POM) and Thermoplastic elastomers (TPE) whose specifications are also listed. The benchmark shapes are available as molds in a plastics manufacturing company, whose contact information is also included. The company is capable of making other molds as well, giving access to more particle shapes. The same particle shapes can thus also be made in different types of (colored) plastic, and in amounts of 50.000 particles or more, larger than conveniently be produced with a 3D printer. We also provide the associated .step and .stl files in the repository in which this document is included. </p>
GrainLearning: A Bayesian uncertainty quantification toolbox for discrete and continuum numerical models of granular materials
GrainLearning is a Bayesian uncertainty quantification and propagation toolbox for computer simulations of granular materials. The software is primarily used to infer and quantify parameter uncertainties in computational models of granular materials from observation data, also known as inverse analyses or data assimilation. Implemented in Python, GrainLearning can be loaded into a Python environment to process the simulation and observation data, or alternatively, as an independent tool where simulation runs are done separately, e.g., via a shell script.
A phenomenological law for complex granular materials from Mohr-Coulomb theory
<p>The compressed directory contains the data in .csv format used for the PCA analysis for each dataset (1, 2 and 3). </p>
High-Speed Video Recordings of Wheel-Rail Traction Enhancement Using a Full-Scale Testing Platform - Granular Material Candidates
<p>A database of 14 high-speed video recordings of rail-sanding process using a full-scale testing platform is provided in this data note. The videos are recorded for various case studies, namely different positioning of the sander nozzle aiming at the rail, nip, and wheel with various angles, and different materials used as rail-sand. The particle velocities can be extracted from these high-speed videos using particle image velocimetry software. The spread angle of the particles as they flow out of the nozzle can also be measured with the use of image processing software. The data extracted from these high-speed recording can be utilised for calibration, validation, and verification of experimental and numerical set-ups, as well as for training artificial intelligence models.</p>
The (in)sensitivity of granular creep to materials and boundaries
<p>Experimental data reported in the publication "The (in)sensitivity of granular creep to materials and boundaries".</p> <p>jupyter notebook codes are included for data analysis and generating figures in the paper.</p> <p>datasets for Kaolinite material and for smooth and rough boundaries</p>
From creep to flow: Granular materials under cyclic shear
<p>This document includes all figures (with their captions) and SI for the manuscript entitled 'From creep to flow: Granular materials under cyclic shear' (10.48550/arXiv.2301.07309).</p>
Supplemental Material for "Anomalous Shear Stress Variation in Wet Granular Medium: Implications for Landslide Lateral Faults"
<p>Dataset and Constrained Parameters for "<strong><em>Anomalous Shear Stress Variation in Wet Granular Medium: Implications for Landslide Lateral Faults</em></strong>" by Chang et al.</p> <p>This dataset comprises 64 rheological experiments, which have been encapsulated in four experimental groups: Full Height, Half Height, Half Height & Load, and Ethanol-Water. </p> <p>The experiments were conducted using a double-cylinder geometry on three-phase granular media to simulate the lateral faults of slow-moving landslides. The dataset includes Mechanical Data for time-evolution measurements of shear stress, fluid volume fraction under varying conditions, and Image Data for velocity fields derived from PIV (Particle Image Velocimetry) analysis.</p> <p>In addition, it provides constrained parameters, such as steady-state shear stress and the characteristics of the flow structure.</p> <p>Article information:</p> <div> <div>Chang, C., Ohno, K., Schulz, W. H., & Yamaguchi, T. (2025). Anomalous Shear Stress Variation in Wet Granular Medium: Implications for Landslide Lateral Faults. <em>Geophysical Research Letters</em>, <em>52</em>(7), e2024GL113816. <a href="https://doi.org/10.1029/2024GL113816">https://doi.org/10.1029/2024GL113816</a></div> </div>
Scaled laboratory experiments of analogue magma intrusion in granular material: X-ray Computed Tomography imagery and displacement data
<p>This data set contains X-ray Computed Tomography (CT) images and surface displacement data of 15 scaled laboratory experiments of analogue magma intrusion in granular material. The experimental methodology and the experimental results were described in detail by Poppe et al. (2019). Displacement data of experiment SPCTIN14 was used by Poppe et al. (2023).<br> When using the experimental imagery or their derivatives please reference at a minimum Poppe et al. (2019) and this data set (Poppe et al., 2023, Zenodo data set).<br> The included explanatory notice reproduces the experimental method and presents the structure and file types contained in this data set.</p>
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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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>
Thermal properties of 'athermal' granular materials
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Blast Furnace Raw Material Granularity Recognition Model Based on Deep Learning and Multimodal Fusion of 3D Point Cloud
<p>Provide data code</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
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Dataset for: Numerical simulations of sheared granular materials by 3D DEM for analyzing formation of internal structures and relationship between it and frictional behavior
<p>We conducted numerical simulations of sheared granular materials under normal stress by using 3D DEM method in order to analyze internal structures and relationship between their formation and frictional behavior. This dataset includes raw data for plotting figures in our article and run scripts and CAD files for simulations.</p>
Dataset for: Numerical simulations of sheared granular materials by 3D DEM for analyzing formation of internal structures and relationship between it and frictional behavior
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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)
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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.