GitHub topics: porespy
PMEAL/porespy
A set of tools for characterizing and analyzing 3D images of porous materials
Language: Python - Size: 1.36 GB - Last synced at: 7 days ago - Pushed at: 7 days ago - Stars: 337 - Forks: 106
petrobras/GeoSlicer
Open source digital rocks software platform for micro-CT, CT, thin sections and borehole image analysis. Includes tools for: annotation, AI, HPC, porous media flow simulation, porosity analysis, permeability analysis and much more.
Language: Python - Size: 104 MB - Last synced at: about 1 month ago - Pushed at: about 1 month ago - Stars: 47 - Forks: 10
tatenda263/3D-porosity-analysis-of-random-samples-
3D Porosity, Tortuosity, and Permeability Assessment in Randomly Generated 3D Savonnières Carbonate Samples
Size: 12.7 KB - Last synced at: 4 months ago - Pushed at: 4 months ago - Stars: 0 - Forks: 0
PorousMedia/star_ccm_flow_simulator
This STAR CCM+ java code takes in two csv files (pore bodies and pore throats), use them to generate pore microstructures, and simulate fluid flow through it. The output of this script is a csv file of flow properties through a set number of iterations.
Language: Java - Size: 57.6 KB - Last synced at: over 1 year ago - Pushed at: almost 2 years ago - Stars: 1 - Forks: 0
PorousMedia/micp_to_eptr
The notebook shows the principle behind the minimum incremental pore volume (MIPV) and estimating the effective pore-throat radius (EPTR) as outlined in steps in the paper above.
Language: Jupyter Notebook - Size: 32.2 KB - Last synced at: almost 2 years ago - Pushed at: almost 2 years ago - Stars: 1 - Forks: 0
PorousMedia/stochastic_pore_microstructural_generator
This code creates CSV files for pore bodies and pore throats of stochastically generated 3D pore microsturtures. The files are intended to make 3D images/ surface files and run simulations in STAR-CCM+.
Language: MATLAB - Size: 42 KB - Last synced at: almost 2 years ago - Pushed at: almost 2 years ago - Stars: 0 - Forks: 0
PorousMedia/xray_to_pore_size_distribution
The notebook demonstrates the workflow for obtaining pore size distribution from binarized micro-CT images. The general principle involves identifying each pore, estimating the volume of each pore, and ultimately determining the radius of a sphere with an equivalent volume of each pore.
Language: Jupyter Notebook - Size: 20.5 KB - Last synced at: almost 2 years ago - Pushed at: almost 2 years ago - Stars: 1 - Forks: 0