Data · dataset · 2024
POREMAPS 1.0.0: Code, Benchmarks, Applications
Listed in DaRUS
Initial release 1.0.0 for POREMAPS, PORous Media Anisotropic Permeability Solver for Stokes flow including benchmarks and applications according to Krach et al. (2024).
Description
POREMAPS is a Finite Difference Method (FDM) -based parallized Stokes flow solver using MPI, specifically designed to process large binarized 3D image datasets of porous media such as X-Ray Computed Tomography (XRCT) images. This repository contains three blocks, namely code, input data, and results for flow simulations in porous materials with respect to evolving anisotropies, all related to the publication Krach et al. (2024): POREMAPS_code.tar.gz Release 1.0.0 for POREMAPS, a FDM-based PORous Media Anisotropic Permeability Solver for Stokes flow.
POREMAPS is a free and open-source simulator for computing permeability tensors of porous materials from binary image data. The compressed file contains the source code, a concise manual, and tools for post-processing. The code is also available via the git repository.
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POREMAPS_benchmarks.tar.gz The benchmarks file includes input geometries and input files for the solver for Poiseuille flow, channel flow, flow through regular sphere packings, in flat, pseudo-3D domains, and around ellipsoids. Also included are the resulting velocity and pressure fields as well as information on neighborhood and domain decomposition. Input files are text files and have .inp file extension.
Voxel-based computer-generated input geometries and results are provided as RAW image files. POREMAPS_applications.tar.gz The applications contain binarized data for the applications discussed in Krach et al. (2024). These include 18 data sets on uniaxially compressed foams and 139 data sets on clogging porous structures.
The former is generated by micro-XRCT and comprises 6 snapshots for different uniaxial compression states. The second data set contains image-based geometries of calcite precipitation in micromodels. The original data set is published in Weinhardt et al. (2022).
All data sets are prepared in such a way that they are in accordance with the boundary conditions specified in the input files. This applies in particular to the applications that have been mirrored to meet periodic boundary conditions. Voxel size, convergence criterion, etc. can be found in the corresponding input files.
Preprocessing also includes the removal of non-interconnected pores so that the specified porosity corresponds to the effective porosity.
Links
Where it is published
- Dataverse dataset page darus.uni-stuttgart.de/dataset.xhtml?persistentId=doi%3A10.18419%2FDARUS-3676 ↗
landing page · from darus uni stuttgart de
- DOI doi.org/10.18419/darus-3676 ↗
DOI / persistent id · from darus uni stuttgart de
Catalogue records · 1
- Dataverse API darus.uni-stuttgart.de/api/datasets/:persistentId/?persistentId=doi%3A10.18419%2FDARU… ↗
metadata API · from darus uni stuttgart de
Topics
- Stated by source
- Computer and Information Science · Earth and Environmental Sciences · Engineering · Mathematical Sciences
- From keywords
- Computer Science & AI · Earth & Environmental Science · Engineering · Mathematics & Statistics
- Inferred from text
- Computed tomography 75% · Fluid mechanics and thermal engineering 72% · Image 75% · Text 75%
Provenance · 1 source records, 18 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| DaRUS | doi:10.18419/DARUS-3676 | 12 d ago | JSON v1 |
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|---|---|---|---|
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| concepts[modality].local:modality:image | enrichment · darus uni stuttgart de | keyword-concept-rules@1.0.0 | title+description (75%) |
| concepts[modality].local:modality:text | enrichment · darus uni stuttgart de | keyword-concept-rules@1.0.0 | title+description (75%) |
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