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12 results for “Fractured Porous”
Fracture Caging in a Porous Lab Fault: Complementary Data
<p>This dataset is complementary experiment data to the already published dataset (doi.org/10.5281/zenodo.10951458) related to fracture caging in shear. It includes the viscosity and flow rate variables in fracture caging study in a porous lab fault. The ReadMeFirst.txt file contains the necessary information to understand the data structure. </p> <p>Three more experiments are added to this complementary experiment dataset. Details are provided. </p>
Porous single crystal unit-cell simulation database for ductile fracture by void growth and coalescence
<p>Ductile fracture through void growth to coalescence occurs at the grain scale in numerous metallic alloys encountered in engineering applications. In order to perform mechanical homogenization of porous single crystals, a database of porous single crystal unit-cell simulation results has been gathered through Finite Element Modeling and Fast-Fourrier Transform simulations, respectively performed on Z-set and Amitex_FFTP. In these simulations, a cubic unit-cell with a unique central spherical void undergo axisymmetric mechanical loading. Mechanical simulations are performed within finite strain theory. Input parameters of interest are stress triaxiality, crystallographic orientation, initial porosity and strain hardening law type; results include macroscopic stress, macroscopic deformation gradient, porosity, void aspect ratio, ligament size and cell aspect ratio.</p>
Observed water level from 2015 to 2019 in 4 wells from a porous fractured aquifer in Burgundy, France
<p>This repository hosts the data used for drawing figure 8 from Jeannot et al.(submitted). The dataset is made of observed water levels from 2015 to 2019 in 4 wells from a porous fractured aquifer in Burgundy, France. The sampling time step is 2 hours.</p> <p><strong>Cited bibliography</strong></p> <p>Jeannot, B., Schaper, L., and Habets, F., submitted. Water Level in Observation Wells Simulated from Fracture and Matrix Water Heads Outputed by Dual-continuum Hydrogeological Models : POWeR-FADS. <em>Water Resources research</em></p>
Fracture toughness of mixed-mode anticracks in highly porous materials dataset and data processing
<blockquote> <div>This repository contains the code and datasets used in the data analysis for "Fracture toughness of mixed-mode anticracks in highly porous materials". The analysis is implemented in Python, using Jupyter Notebooks.</div> </blockquote> <h2>Contents</h2> <ul> <li><code>main.ipynb</code>: Jupyter notebook with the main data analysis workflow.</li> <li><code>energy.py</code>: Methods for the calculation of energy release rates.</li> <li><code>regression.py</code>: Methods for the regression analyses.</li> <li><code>visualization.py</code>: Methods for generating visualizations.</li> <li><code>df_mmft.pkl</code>: Pickled DataFrame with experimental data gathered in the present work.</li> <li><code>df_legacy.pkl</code>: Pickled DataFrame with literature data.</li> </ul> <h2>Prerequisites</h2> <ul> <li>To run the scripts and notebooks, you need:</li> <li>Python 3.12 or higher</li> <li>Jupyter Notebook or JupyterLab</li> <li>Libraries: <code>pandas</code>, <code>matplotlib</code>, <code>numpy</code>, <code>scipy</code>, <code>tqdm</code>, <code>uncertainties</code>, <code>weac</code></li> </ul> <h2>Setup</h2> <ol> <li>Download the zip file or clone this repository to your local machine.</li> <li>Ensure that Python and Jupyter are installed.</li> <li>Install required Python libraries using <code>pip install -r requirements.txt</code>.</li> </ol> <h2>Running the Analysis</h2> <ol> <li>Open the <code>main.ipynb</code> notebook in Jupyter Notebook or JupyterLab.</li> <li>Execute the cells in sequence to reproduce the analysis.</li> </ol> <h2>Data Description</h2> <div>The data included in this repository is encapsulated in two pickled DataFrame files, <code>df_mmft.pkl</code> and <code>df_legacy.pkl</code>, which contain experimental measurements and corresponding parameters. Below are the descriptions for each column in these DataFrames:</div> <h3><code>df_mmft.pkl</code></h3> <div>Includes data such as experiment identifiers, datetime, and physical measurements like slope inclination and critical cut lengths.</div> <ul> <li><code>exp_id</code>: Unique identifier for each experiment.</li> <li><code>datestring</code>: Date of the experiment as a string.</li> <li><code>datetime</code>: Timestamp of the experiment.</li> <li><code>bunker</code>: Field site of the experiment. Bunker IDs 1 and 2 correspond to field sites A and B, respectively.</li> <li><code>slope_incl</code>: Inclination of the slope in degrees.</li> <li><code>h_sledge_top</code>: Distance from sample top surface to the sled in mm.</li> <li><code>h_wl_top</code>: Distance from sample top surface to weak layer in mm.</li> <li><code>h_wl_notch</code>: Distance from the notch root to the weak layer in mm.</li> <li><code>rc_right</code>: Critical cut length in mm, measured on the front side of the sample.</li> <li><code>rc_left</code>: Critical cut length in mm, measured on the back side of the sample.</li> <li><code>rc</code>: Mean of <code>rc_right</code> and <code>rc_left</code>.</li> <li><code>densities</code>: List of density measurements in kg/m^3 for each distinct slab layer of each sample.</li> <li><code>densities_mean</code>: Daily mean of <code>densities</code>.</li> <li><code>layers</code>: 2D array with layer density (kg/m^3) and layer thickness (mm) pairs for each distinct slab layer.</li> <li><code>layers_mean</code>: Daily mean of <code>layers</code>.</li> <li><code>surface_lineload</code>: Surface line load of added surface weights in N/mm.</li> <li><code>wl_thickness</code>: Weak-layer thickness in mm.</li> <li><code>notes</code>: Additional notes regarding the experiment or observations.</li> <li><code>L</code>: Length of the slab–weak-layer assembly in mm.</li> </ul> <h3><code>df_legacy.pkl</code></h3> <div>Contains robustness data such as radii of curvature, slope inclination, and various geometrical measurements.</div> <ul> <li><code>#</code>: Record number.</li> <li><code>rc</code>: Critical cut length in mm.</li> <li><code>slope_incl</code>: Inclination of the slope in degrees.</li> <li><code>h</code>: Slab height in mm.</li> <li><code>density</code>: Mean slab density in kg/m^3.</li> <li><code>L</code>: Lenght of the slab–weak-layer assembly in mm.</li> <li><code>collapse_height</code>: Weak-layer height reduction through collapse.</li> <li><code>layers_mean</code>: 2D array with layer density (kg/m^3) and layer thickness (mm) pairs for each distinct slab layer.</li> <li><code>wl_thickness</code>: Weak-layer thickness in mm.</li> <li><code>surface_lineload</code>: Surface line load from added weights in N/mm.</li> </ul> <p>For more detailed information on the datasets, refer to the paper or the documentation provided within the Jupyter notebook.</p> <h2>License</h2> <div>This work is licensed under a <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</div> <p> </p> <div>You are free to:</div> <ul> <li><strong>Share</strong> — copy and redistribute the material in any medium or format</li> <li><strong>Adapt</strong> — remix, transform, and build upon the material for any purpose, even commercially.</li> </ul> <div>Under the following terms:</div> <div> <ul> <li><strong>Attribution</strong> — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.</li> </ul> </div> <h2>Citation</h2> <div>Please cite the following paper if you use this analysis or the accompanying datasets:</div> <div> <ul> <li>Adam, V., Bergfeld, B., Weißgraeber, P. van Herwijnen, A., Rosendahl, P.L., Fracture toughness of mixed-mode anticracks in highly porous materials. <em>Nature Communincations</em> <strong>15</strong>, 7379 (2024). https://doi.org/10.1038/s41467-024-51491-7</li> </ul> </div>
Fracture Caging in a Porous Lab Fault: Experiment Dataset (sensor coordinates added)
<p>Update: Sensor coordinates and make that were missing from the previous version were added. </p> <p>We conducted shear fracture caging experiments at the Fractured Earth Laboratory to investigate the potential of fracture caging in limiting induced seismicity in the fault. The experiments involved an injection of fluid into a repeatably constructed assembly of two aluminum wedges bonded by plaster, which is critically loaded for shear-slip. Meanwhile, fluid production is enabled using a five-spot pattern of pre-drilled boundary wells (i.e., a fracture cage). Injected fluid was a gear oil with 8.81 cP viscosity at room temperature . Syringe pumps were used to control and measure the injection rate, volume, and pressure. Microseismic activity was recorded using 16 acoustic emission (AE) sensors that were equally split into high-magnitude and high-sensitivity acquisition systems. Over the course of experiments, raw data collected includes AE waveforms from high-magnitude and high-sensitivity systems, post-mortem shear fracture photos, and synchronized timeseries data for 8 tests, which are presented here. </p>
Hydraulic tomography and thermal tracer test data from a fractured-porous field site in Goettingen (Germany)
<p>These data are supplementary material for:</p> <p>Römhild, L., Ringel, L. M., Liu, Q., Hu, L., Ptak, T., & Bayer, P. (2024). Hybrid discrete fracture network inversion of hydraulic tomography data from a fractured-porous field site. Water Resources Research, 60, e2023WR036035. https://doi.org/10.1029/2023WR036035</p> <p>The data set comprises hydraulic tomography and thermal tracer test field data from a porous-fractured site in Göttingen (Germany). The repository contains a schematic figure of the experimental setup and subfolders for the two different experiments. For more information about the data, we refer to the readme files in the repository, and the soon-to-be-published paper (see above).</p>
Dataset: High-fidelity experimental model verification for flow in fractured porous media
<p>The dataset consists of five image series of tracer experiments in fractured porous media. The images have been taken using PET imaging and are available in DICOM format. Overall, three fractured geometries have been considered, called fractip-a, fractip-b, and fractip-e. Different boundary conditions have been used, generating in total four experiments. In addition, images of simple water displacement experiments in an intact core (fractip-j) are provided, useful for extracting macroscopic hydraulic properties of the core material. All experiments are based on water displacement and use 18F-FDG tracer for PET tracking. The available DICOM images are reconstructed using 1 min x 0.4mm space-time voxels. </p> <p>A related data analysis based on the Darcy Scale Image Analysis toolbox DarSIA is available at 10.5281/zenodo.10410227.</p>
Supporting Information: Equivalence of Discrete Fracture Network and Porous Media Models by Hydraulic Tomography
<p>Supporting Information README</p> <p>2018-Jan-17</p> <p>"Equivalence of Discrete Fracture Network and Porous Media Models by Hydraulic Tomography"</p> <p>Yanhui Dong, Yunmei Fu, Tian-Chyi Jim Yeh, Yu-Li Wang, Yuanyuan Zha, Liheng Wang, Yonghong Hao</p> <p>This file contains the supplementary data for this manuscript, including the locations and properties of fracture networks, the locations of observation wells and validation wells, the water head used in inverse model and validation tests, as well as the executive file used in the inverse model.</p>
Supporting data for: Quantitative visualization of two-phase flow in a fractured porous medium, Water Resources Research
<p>This dataset includes experimental images and data processing scripts used to generate the results detailed in the paper titled: Quantitative visualization of two-phase flow in a fractured porous medium and submitted to Water Resources Research.</p>
Clinical Study to Predict the Risk of Bone Fractures With the POROUS Ultrasound Device
ClinicalTrials.gov study NCT06567054. IPD Sharing: NO. Countries: 3. Publications: 1.
Dataset of: "Seismic signatures of fractured porous rocks: The partially-saturated case"
<p>This package contains the data of the paper Solazzi et al. (2020). Further information is given in the README file.</p> <p>The software package for the oscillatory relaxation tests, called Parrot, is available upon request to the authors.</p>
Viscous fingering in fractured porous media
<p>Data from numerical simulations of viscous fingering in fractured porous media.</p> <p>The folder structure of the dataset is given as follows:</p> <p>res_NAME_OF_FRACTURE_GEOMETRY/NAME_OF_VARIABLE/csv/DATA_FILES.</p> <p>The name of the DATA_FILES gives the values of the dimensionless parameters used in the simulation that generated the datafile. The last part of the name (*_task_TASKNUMBER_run_RUNNUMBER.csv) is an identifyer for the simulation and can for most purposes be ignored.</p> <p>The first line of each file is the header with items:</p> <ul> <li>time (dimensionless time of time-step)</li> <li>average_c (the volume averaged concentration in the simulation domain)</li> <li>L05 (the mixing region length in fractures and rock matrix with treshold c<0.05)</li> <li>Lm05 (the mixing region length in rock matrix with treshold c<0.05)</li> <li>Lf05 (the mixing region length in fractures with treshold c<0.05)</li> <li>L01 (the mixing region length in fractures and rock matrix with treshold c<0.01)</li> <li>Lm01 (the mixing region length in rock matrix with treshold c<0.01)</li> <li>Lf01 (the mixing region length in fractures with treshold c<0.01)</li> <li>L001 (the mixing region length in fractures and rock matrix with treshold c<0.001)</li> <li>Lm001 (the mixing region length in rock matrix with treshold c<0.001)</li> <li>Lf001 (the mixing region length in fractures with treshold c<0.001)</li> <li>num_fingers (the number of fingers in the domain)</li> </ul> <p>Each successive line gives the results of a time-step in the simulation.</p> <p> </p>
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