The multiphasepy package is a Python toolkit for building, running, and maintaining CFD simulation case collections for the software released by the OpenFOAM Foundation (but also others, like Ansys Fluent or Siemens Simcenter Star-CCM). It combines reusable library modules with practical command-line tools to cover the full simulation lifecycle: case templating, batch execution, monitoring, post-processing, validation support, repository comparison, and publication workflows.
At its core, multiphasepy helps teams move from individual case setups to scalable, structured projects containing a large collection of setups, e.g. a validation database. Cases can be provided as templates that are parameterized at runtime, metadata can be managed in a machine-readable way and cases can be conveniently batch-processed with the workflow management tool Snakemake both on workstations and Slurm-based HPC systems. Using a container-based software environment (e.g. Apptainer or Docker) the case collection becomes fully portable. An integrated Copier template allows the creation and maintenance of multiple independent case collections.
Beyond execution, the package supports quality assurance and communication of results: tools are included for automated checks, fuzzy-logic based evaluation, data conversion, visualization, and generation of artifacts suitable for reporting and dissemination. This makes multiphasepy a practical bridge between day-to-day CFD case work and long-term, reproducible research software operations in collaborative environments.
Command-Line ToolsThe multiphasepy package provides a comprehensive suite of command-line tools for CFD workflow management, data processing, and quality assurance. All tools follow the naming convention mpy<function> and provide --help for detailed usage information.
The multiphasepy package requires Python 3.12 or newer.
Install the latest release from PyPI:
pip install multiphasepy For full environment setup and platform-specific notes, see the installation guide in the documentation.
How to cite us?If you find that package useful, please cite as
Schlegel et al. (2026). Multiphase Python Repository by HZDR. Rodare. <https://doi.org/10.14278/rodare.3093>. OpenFOAM® is a registered trade mark of OpenCFD Limited, producer and distributor of the OpenFOAM® software via www.openfoam.com. The Multiphase Python Repository by HZDR is not compatible with the software released by OpenCFD Limited, but is developed for the software released by the OpenFOAM Foundation via www.openfoam.org.
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\n\n"Effect of filling degree on particle segregation in a rotating drum - an experimental and numerical study"
\n\nby Theodoros Nestor Papapetrou, Martina Bieberle, Frank Barthel, Uwe Hampel and Gregory Lecrivain. This updated entry is linked to the manuscript resubmission following revision.
\n\nPRE_2nd_submission.tar.gz (formerly aggregate_data): Scripts for figure production
\ncamera_code: code used to process camera data
\ncamera_data: image camera data
\nDEM_code: code used to process output DEM data
\nDEM_data: LIGGGHTS input data
\nrotating_VTK_data_X.zip: DEM output data related to scenario X, with X=1,2,3,...,7
\nUFXCT_code: code used to process UFXCTdata
\nUFXCT_data: UFXCT data
FINDSLAB: Software for Exfoliation and Cleavage of Crystals
\n\nTom Barnowsky & Rico Friedrich
\nTechnische Universität Dresden & Helmholtz-Zentrum Dresden-Rossendorf, Germany
This program implements the XCP method to identify 2D materials from bulk materials by estimating bonding energies using a two-body potential model [1].
\n\nPotential Models
\n\nThe code supports a range of two-body potential parametrizations \\(V(r)\\), namely:
\n\nParameters are provided as plain text files which are specified via the environment variable `FINDSLAB_POTDATA`. Note that all energies have to be multiplied by a factor two to compare to surface/bonding energies.
\n\nBuild
\n\nRun `make` serially (without `-j n`). A binary will be created in the `bin` directory.
\n\nRequirements: a recent Fortran compiler, BLAS and LAPACK.
\n\nUsage
\n\nFINDSLAB provides some instructions when running `findslab --help`.
\n\nThe code is designed to work with VASP POSCAR files, however, the reader is not fully general and expects the formatting as it is found in the AFLOW database (aflow.org) [2]. To convert general structure files (including those from other codes) to this format, use the AFLOW software to run `aflow --vasp`. The aflow code is available at github.com/aflow-org/aflow.
\n\nHere we provide an example code to determine HKLSEARCH slabs from bulk Ca3N2 retrieved via the AFLOW REST API [3]:
\n\nexport FINDSLAB_POTDATA=<path to file>\nexport OPENBLAS_NUM_THREADS=1\ncurl http://aflowlib.duke.edu/AFLOWDATA/ICSD_WEB/HEX/Ca3N2_ICSD_169727/CONTCAR.relax.vasp |\n aflow --sconv |\n findslab --hklsearchConversion to the conventional unit cell via `aflow --sconv` is optional and is only used here to relate Miller indices to the conventional cell.
\n\nRelease Notes
\n\nAcknowledgements
\n\nThe authors thank Carsten Timm, Steve Schmerler, and Moritz Leucke for fruitful discussions. Parts of this work are based on an implementation for creating Miller planes from the atomic simulation environment (ASE) [4]. Additionally, we implement the criterion of Mounet et al. [5] to identify van der Waals-bound layers in bulk structures.
\n\nLicense
\n\nThis dataset is published under the Apache 4.0 license. We kindly ask works based on this software to cite this entry and/or the associated publication.
","doc_id":"1","doi":"10.14278/rodare.4687","keywords":["2D materials","non-van der Waals compounds","computational materials science"],"language":"eng","license":{"id":"Apache-2.0"},"notes":"FINDSLAB provides the calculation backend for xcp.hzdr.de, which offers an interactive interface to explore the implemented approaches.","pub_id":"42447","publication_date":"2026-06-02","references":["[1] T. Barnowsky, C. Timm, and R. Friedrich, arXiv:2512.16721 (2025)","[2] S. Divilov et al., High Entropy Alloys Mater. 3, 178 (2025).","[3] R. H. Taylor et al., Comp. Mat. Sci. 93, 178 (2014).","[4] A. H. Larsen et al., J. Phys.: Condens. Matter 29, 273002 (2017).","[5] N. Mounet et al., Nat. Nanotechnol. 13, 246 (2018)."],"related_identifiers":[{"identifier":"10.1007/s44210-025-00058-2","relation":"cites","scheme":"doi"},{"identifier":"10.1016/j.commatsci.2014.05.014","relation":"cites","scheme":"doi"},{"identifier":"10.1088/1361-648X/aa680e","relation":"cites","scheme":"doi"},{"identifier":"10.1038/s41565-017-0035-5","relation":"cites","scheme":"doi"},{"identifier":"https://www.hzdr.de/publications/Publ-42447","relation":"isIdenticalTo","scheme":"url"},{"identifier":"10.48550/arXiv.2512.16721","relation":"cites","scheme":"doi"},{"identifier":"10.14278/rodare.4180","relation":"isVersionOf","scheme":"doi"}],"relations":{"version":[{"count":2,"index":1,"is_last":true,"last_child":{"pid_type":"recid","pid_value":"4687"},"parent":{"pid_type":"recid","pid_value":"4180"}}]},"resource_type":{"title":"Software","type":"software"},"title":"FINDSLAB: Software for Exfoliation and Cleavage of Crystals","version":"1.1"},"owners":[475],"revision":6,"stats":{"downloads":4.0,"unique_downloads":4.0,"unique_views":82.0,"version_downloads":17.0,"version_unique_downloads":17.0,"version_unique_views":414.0,"version_views":442.0,"version_volume":1704533.0,"views":86.0,"volume":1037672.0},"updated":"2026-06-09T16:05:27.382940+00:00"},{"conceptdoi":"10.14278/rodare.4684","conceptrecid":"4684","created":"2026-05-28T12:33:12.817214+00:00","doi":"10.14278/rodare.4685","id":4685,"links":{"badge":"https://rodare.hzdr.de/badge/doi/10.14278/rodare.4685.svg","conceptbadge":"https://rodare.hzdr.de/badge/doi/10.14278/rodare.4684.svg","conceptdoi":"https://doi.org/10.14278/rodare.4684","doi":"https://doi.org/10.14278/rodare.4685","html":"https://rodare.hzdr.de/record/4685","latest":"https://rodare.hzdr.de/api/records/4685","latest_html":"https://rodare.hzdr.de/record/4685","self":"https://rodare.hzdr.de/api/records/4685"},"metadata":{"access_conditions":"","access_right":"restricted","access_right_category":"danger","communities":[{"id":"rodare"}],"creators":[{"affiliation":"FWOA","name":"Duckstein, Alexandra","orcid":"0009-0002-5241-8540"},{"affiliation":"FWOA","name":"Pospiech, Solveig","orcid":"0000-0003-2727-2375"},{"affiliation":"FWOA","name":"Brendler, Vinzenz","orcid":"0000-0001-5570-4177"}],"description":"The file was generated using Python and Geochemist's Workbench (GWB).
\nIn Python, the parameters specified in the header are varied within their respective ranges, and GWB is then executed.
\nInput parameters are:
\nSolid/liquid ratio (SLR), pH, concentrations of UO2++, NpO2++, Am+++, Eh, Ca++, HCO3-, mineral composition in wt% (albite, orthoclase, quartz, goethite, phlogopite, illite, clinoclase).
\nThe conversion of the minerals to grams is based on the SLR and a water volume of 1 L.
\nOutput parameters are the Kd values of UO2++, NpO2++, and Am+++ as well as their logarithms to the base 10 (and the imbalance and imbalance error).