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26 results for “granular flows”
stationary_granular_flow_seismicity_and_optics
<p>Raw data acquired during the study of seismic sources emitted by a laboratory landslide: a stationary granular flow in an inclined flume. The data consists in images acquired by a fast camera and accelerometers. The scripts to treat the data are also shared.</p>
Simulation data and scripts for CFD-DEM simulation of saturated bi-disperse granular flows
<ul> <li>Data set 1 - contains the raw data required to replicate and validate all plots in the main article. </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>
Data for Roles of Granularity and Timescales in Debris Flow Hazards on Alluvial Fans
<p>This dataset includes the digital elevation models (DEM) for the 9 debris flow fan experiments and the slope map data for the 9 debris flow fan experiments and 2 field cases (the Straight Fan and Piute Fan in White Mountain, CA). These data are stored as GeoTIFF files that include information on mesh coordinates. Please read the Data_Information.pdf for the details of the data file contents, duration, sediment contents, flow/discharge/input rates, and mesh size. </p>
Datasets, codes and video clips for the laboratory flume tests of granular flow
<p>Datasets, video clips, and codes related to the paper 'Insight into granular flow dynamics relying on basal stress measurements: from experimental flume tests', submitted to the Journal of Geophysical Research: Solid Earth.</p> <p>The datasets provides the raw and processed data for the laboratory flume tests of granular flow including parameters reflecting the granular flow behavior, basal normal stresses measured by a force plate, and deposit parameters of the granular flows.</p> <p><strong>S1_data_granular flow_velocity</strong> provides data of the velocity profiles with a 0.1 second time interval, the depth-averaged velocities, the depth-averaged shear rates, and the solid inertial stresses of the granular flows under different experimental conditions. The original data were calculated through particle image velocimetry (PIV) method. The images for PIV analysis were recorded by a high-speed camera.</p> <p><strong>S2_data_granular flow_stress</strong> provides the raw data of the measured basal normal stresses of the granular flows for all tests. The mean and fluctuating stress components extracted by applying a moving window average filter are also listed in the Table.</p> <p><strong>S3_data_granular flow_flow depth</strong> provides the data of the granular flow depth extracted every 0,02 s through a image processing method based on the high-speed photographs. </p> <p><strong>S4_data_granular flow_deposit</strong> provides the parameters of the granular flow deposits for all tests including the apparent friction coefficient and equivalent friction coefficient. The deposit parameters were calculated based on the digital surface model (DSM) of deposit, which were obtained through a oblique photogrammetry method.</p> <p><strong>S5_data_granular flow_density</strong> gives the data of the dynamic bulk flow densities of the granular flows under all experimental conditions. The dynamic bulk densities were calculated according to the measured and calculated normal stresses.</p> <p>The videos of the granular flows under different experimental conditions during their propagation are provided in <strong>'S6_video_granular flow.zip'</strong> to show the granular flow behavior and its evolution. <strong>S6_video_granular flow</strong> includes the side-view of the granular flows under all experimental conditions and front-view of the IMF-223 granular flow . </p> <p><strong>S7_codes_data analysis</strong> provides the computer codes for the extraction of mean and fluctuating components and the calculation of granular flow depth. The former includes one file for conducting moving average filter. The latter contains four files, which are used for median filter, image erosion, threshold segmentation and floodfill, extracting flow depth. </p>
Flow properties of slow granular flows of irregular grains
<p>This repository holds the tomographic data of slow granular flows of irregular grains in shear cell and in silo. We tested the 3D flow properties of realistic (non-spherical) granular materials with special emphasis on the orientational ordering of the grains and its effect on the flowability of the sample. In this collaborative work between the Wigner Research Centre (Budapest) and Otto-von Guericke University (Magdeburg), laboratory experiments have been performed in two geometrical configurations: (I) quasi-static shear flow in a split bottom Couette shear device and (II) slow flow in a silo.<br> <br> The read_me.txt contains the structure of the data. Additional information/data not included in this repository is available upon request.</p>
Data from laboratory granular-flow experiments with acoustic sensors
<p>Experimental data of dynamic pressures generated by dry granular flows moving down and impacting on a plate embedded in an inclined chute facility. The data consists of basal impact pressures measured with a pressure sensor for variable slope angle ranging from 30° to 38° with an initial mass of 100 kg.</p>
Discrete and continuum simulations of bedload transport with kinetic theory of granular flow
<p>This depository contains the simulation results used in the publication Chassagne, R., Chauchat J. & Cyrille B. (2023). A frictional-collisional model for bedload transport based on kinetic theory of granular flows: discrete and continuum approaches.</p> <p>It contains DEM results as well as the results of the continuum model. The results are written in text files containing headers with name and units of the variables.</p>
Dataset of the temperature rise during granular flows in a rotating drum
<p>This dataset contains the temperature rise of granular flows reported in the journal article "<em>Experimental investigation of heat generation during granular flow in a rotating drum using infrared thermography</em>" (<a href="https://doi.org/10.1016/j.powtec.2023.118619">https://doi.org/10.1016/j.powtec.2023.118619</a>).</p> <p>The dataset is separated into 4 files, one for each particle material: <em>plastic_particles.xlsx</em>, <em>plastic_particles.xlsx</em>, <em>plastic_particles.xlsx</em>, <em>plastic_particles.xlsx</em>.</p> <p>Each file has 3 tabs, one for the experiments with each number of particles: 400, 600, 800.</p> <p>Each tab has 3 tables, one for the experiments with each rotation speed: 15 rpm, 35 rpm, 55 rpm.</p> <p>Each table contains the temperature rise obtained in 5 experiments, along with its mean and standard deviation.</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>
Experimental dataset on basal stresses and seismic signals generated by granular flows moving on a 3D-printed bumpy substrate
<p>This dataset provides the data supporting the experimental study of granular flows moving on a 3D-printed bumpy substrate considering the response of basal stresses and seismic signatures.</p> <p>S1_video clips_the side-view of the kinematic behaviors of the granular flows tracked by a high-speed camera.</p> <p>S2_data_stress and seismic signals measured at the instrumented plate</p> <p>S3_data_example of velocity fields downstream and normal to the base<br>S4_data_propagation features of the flow characterized by basal stresses and seismic signals<br>S5_data_variations of the depth-averaged velocity of the granular flows and corresponding nondimensionalized downslope velocity profiles<br>S6_data_profiles of the velocity of the granular flows normal to the base along with their depth-averaged velocities in the basal layers<br>S7_data_ nondimensionalized shear rates of the granular flows for various particle diameters <br>S8_data_depth-averaged shear rates and corresponding inertial numbers for granular flows with different particle sizes<br>S9_data_the mean normal stress and shear stress and stress fluctuations normal and tangential to the base<br>S10_data_characteristics of seismic signals in terms of peak amplitude, mean envelope, seismic deviation factor, and the signal mean frequency<br>S11_data_effective basal friction coefficient and equivalent friction coefficient as functions of particle diameter<br>S12_data_relationships between nondimensionalized normal stress fluctuations and nondimensionalized basal vertical velocity, inertial number, effective basal friction coefficient and equivalent friction coefficient of the flows.<br>S13_data_seismic deviation factor as functions of nondimensionalized normal stress fluctuations, inertial number, effective basal friction coefficient and equivalent friction coefficient</p> <p>S14_data_Comparison of effective basal friction coefficient μ_b and that scaled by the nondimensionalized basal vertical velocity.</p>
Controlling rheology via boundary conditions in dense granular flows
<p>Boundary shape, particularly roughness, strongly controls the amount of wall slip in dense granular flows. We aim to quantify and understand which aspects of a dense granular flow are controlled by the boundary conditions and to incorporate these observations into a cooperative nonlocal model characterizing slow granular flows. To examine the influence of boundary properties, we perform experiments on a quasi-2D annular shear cell with a rotating inner wall and a fixed outer wall; the latter is selected among 6 walls with various roughnesses, local concavity, and compliance. Measuring flow field and stress field throughout the material in experimental studies of granular materials is an ongoing challenge due to the complex nature of these materials. Here, we use innovative techniques to quantify stress field and flow field when different boundaries were used. We find that we can successfully capture the full flow profile using a single set of empirically determined model parameters, with only the wall slip velocity set by direct observation. Through the use of photoelastic particles, we observe how the internal stresses fluctuate more for rougher boundaries, corresponding to a lower wall slip, and connect this observation to the propagation of nonlocal effects originating from the wall. Our measurements indicate a universal relationship between dimensionless fluidity and velocity.</p>
Controlling rheology via boundary conditions in dense granular flows
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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>
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>
Experimental investigation on dynamics and flow resistance of granular-fluid flows
<p><span>Dataset related to the manuscript “Experimental investigation on dynamics and flow resistance of granular-fluid flows”, submitted to the </span><em><span>Journal of Geophysical Research: Earth Surface</span></em><span>.</span></p>
Nonsmooth simulations of 3D Drucker-Prager granular flows and validation against experimental column collapses
<p> </p> <p># About</p> <p>This archive aims to reproduce the results of the article entitled: "***Nonsmooth simulations of 3D Drucker-Prager granular flows and validation against experimental column collapses***" *by Gauthier Rousseau, Thibaut Métivet, Hugo Rousseau, Gilles Daviet, and Florence Bertails-Descoubes*</p> <p> </p> <p>## Contents</p> <p> </p> <p>- The experimental data: raw videos, experimental information, velocity and profiles</p> <p>- The `sand6py` code which is a fork of [Daviet PhD][1] `sand6` code including [diphasic feature][2] and other new features developed for the paper (collapses scenarios with frictional door, hysteresis, python binding with `pybind11`)</p> <p>- Python scripts:</p> <p>- for performing velocimetry measurements</p> <p>- for running the `sand6` 3D simulations of granular column collapses</p> <p>- for plotting paper figures</p> <p><br> </p> <p>The git repository corresponding to `sand6py` is available on [https://gitlab.com/groussea/sand6py](https://gitlab.com/groussea/sand6py)</p> <p> </p> <p>A maintained version of sand6 is available on [https://gitlab.inria.fr/elan-public-code/sand6](https://gitlab.inria.fr/elan-public-code/sand6)</p> <p> </p> <p>## Figures</p> <p> </p> <p>the figures of the article are reproducible from the python scripts in the `python/figurejfm` path</p> <p> </p> <p>python dependencies are `matplotlib opencv jupyter vtk h5py tqdm scikit-image opyf`</p> <p> </p> <p>You may obtain all the required dependencies in a convenient conda environment using the following command line in your terminal:</p> <p> </p> <p>```shell</p> <p>conda create --channel conda-forge -n sand6env python=3.9 matplotlib opencv jupyter vtk h5py tqdm scikit-image</p> <p> </p> <p>conda activate sand6env</p> <p>```</p> <p> </p> <p>Corresponding e-mail: gauthier.rousseau@gmail.com</p> <p> </p> <p>[1]: tel.archives-ouvertes.fr/tel-01684673 "*Modeling and simulating complex materials subject to frictional contact: Application to fibrous and granular media*. Diss. Ph. D. Dissertation. Université Grenoble Alpes. , 2016"</p> <p>[2]: https://hal.inria.fr/hal-01458951 "Simulation of Drucker–Prager granular flows inside Newtonian fluids"</p> <p> </p>
Wave generation by fluidized granular flows: experimental insights into the maximum near-field wave amplitude
<p><strong>Data set for experimental videos modelling the entrance of a fluidised granular flow into the water.</strong></p> <p>The data set includes:</p> <ul> <li>Impact parameters of the fluidised granular flows: mass of the flow, thickness and velocity on the inclined plane. </li> <li>Nondimensional parameters of the flows: impact Froude number, nondimensional thickness and nondimensional mass of the flow.</li> <li>Surface elevation data for all the experiments measured at four positions from the shoreline.</li> <li>Maximum amplitudes of the generated waves. </li> </ul>
Dataset of velocities of dry granular flows in a partially obstructed tilted chute
<p>The dataset presented here corresponds to data collected in an experimental campaign on dry granular flows, in which one experiment was repeated 31 times. The experimental campaign was performed in a 1.5 m length facility sloping at 20 degrees, where a volume of granular material was released from an upstream gate and trapped through a vertical obstruction in the downstream area of the channel, simulating slit dam conditions.</p> <p>The experiments carried out presented the following characteristics: a) uni-sized polystyrene particles (d=1.8mm); b) 3 litres volume of particles and c) the obstruction used had a double distance from the chute lateral walls of twice the diameter of the particles. Images from the experiments were collected by means of one high-speed camera located at the downstream part of the channel with a target frame rate of 300 frames per second and an exposure time of 200µs. </p> <p>The collected data was processed and filtered by means of Matlab algorithms aiming the assembly of a along-chute (u) and wall-normal (w) velocity ensemble database for the total time evaluated of 437 frames.</p> <p> </p>
Video for the high-speed camera photos from granular flow experiments
<p>These are two video for grains mass movement derived from granular flow laboratory experiments (which generated by high speed camera photos).</p> <p>Additionally, the corresponding raw photos are also presented.</p>
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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.