Multiphase Python Repository by HZDR

The python package provides several routines and scripts required to operate the code and cases repositories containing additional code and setups for the open-source software released by the OpenFOAM Foundation. This includes, among other things, utilities for pre- and post-processing of simulation cases, utilities to launch virtual environments containing the source code, and utilities to operate the continuous integration and development environment in a self-hosted Gitlab instance

Data Publication: Understanding Energy Flow and Inefficiency of a Thermo-Magnetic Generator by Transient Multi-Physics Modelling

This folder contains the open data used to generate the figures presented in the manuscript entitled Understanding Energy Flow and Inefficiency of a Thermo-Magnetic Generator by Transient Multi-Physics Modelling. The folder includes the following data files: Benchmark_Prototypes_Power_Frequency_PowerDensity.ods Power_Validation.ods Voltage_Validation.ods Sankey_Diagram.ods Average_Component_Temperatures.ods Heat_Propagation_Data.ods Active_Material_Properties.ods Magnetic_Flux_2D_vs_3D.ods FluxYokes_Power_VoltageOC_VoltageLoad.csv Further details are provided in the Readme.txt file.The authors gratefully acknowledge funding from the European Union under grant agreement No 101119852 (EU-ITN Heat4Energy). We gratefully acknowledge compute time and data storage on the vlsmath1 system provided by the Department of Information Services and Computing at HZDR

OPC UA based device backend for the ChimeraTK framework

The OPC UA based device backend implements an OPC UA client, that allows to create an ChimeraTK device that communicates with an OPC UA server. The implementation is based on the open source OPC UA stack open62541

Messdatenarchiv zum Vorhaben DCS-Monitor II

Messdatenarchiv zum Vorhaben DCS-Monitor I

Multiphase Python Repository by HZDR

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 Tools The 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 and provide --help for detailed usage information. Workflow Management mpycopy : Copy and render templated cases mpydocker : Launch customizable Docker development environments mpywatch : Progress display and real-time data visualization mpyworkflow : Create and manage cases projects allowing batch processing Data Processing mpyconv : Convert data files between formats mpypost : Mathematical post-processing operations mpyvisualize : Visualize simulation result files Quality Assurance mpytest : Run CFD code functionality tests mpyfuzzy : Evaluate simulation goodness using fuzzy logic mpyhooks : Run git hooks for code quality checks Utilities mpyidentify : Identify file types and associate tags mpyrpcmp : Compare two repositories (file-based) mpyrpdiff : Compare repositories using git diff mpyshrun : Wrap shell commands with logging Publishing mpypublish : Publish software to Rodare repository Installation The multiphasepy package requires Python 3.12 or newer. Install the latest release from PyPI: pip install -i https://test.pypi.org/simple/ 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. . Acknowledgements 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

Research Data: Recovery of rare earth elements by peptide-induced Ln3+ precipitation

Electronic waste and wastewater from mining, industry, etc. are valuable secondary sources of strategic high-tech metals like rare earth elements (REEs). Due to low concentrations of REEs, their recovery is challenging. Current separation processes have high energy consumption and use large amounts of toxic or expensive reagents, resulting in contaminated water and its costly reprocessing. Biomolecules, as environmentally friendly alternatives, are able to overcome these economic and ecological issues. Metal-binding peptides are convincing not only because of their high selectivity and stability under various conditions. In case of biobased production, they are also “renewable” resources and are neither toxic nor difficult to degrade at the process end. Here, we successfully utilized phage surface display (PSD) to screen for peptides with high affinity for REEs. The selected peptide GC22 (CEPDLWIDRFWC), identified by PSD in combination with next-generation sequencing, revealed the ability to precipitate lanthanide and yttrium ions from aqueous solutions in large quantities (> 60 %). It largely favors all REE ions over other commonly occurring metal ions in wastewater. The amorphous REE-GC22-precipitate is characterized by curled and spherical structures. Nuclear magnetic resonance spectroscopy revealed that in dimethyl sulfoxide Arg9 and Cys12 are most likely involved in metal binding. Reversibility of binding and thus regeneration of the peptide was demonstrated, enabling its potential use for multiple extraction cycles. GC22 thus offers a sustainable, cost-effective, and environmentally friendly alternative for future REE-recovery from low-REE-concentration wastewaters and e-waste leachates

Data for Upscaling mineralogy with hyperspectral data: a benchmark dataset and machine learning framework to enable hyperspectral geometallurgy

Mineral liberation analysis (MLA) dataset accompanying the paper: Upscaling mineralogy with hyperspectral data: a benchmark dataset and machine learning framework to enable hyperspectral geometallurgy. This describes the mineralogy of 204 thick-sections prepared from 49 drillholes sampled across 7 different locations and coregistered with VNIR-SWIR-MWIR-LWIR hyperspectral data. It is intended to help develop, test and benchmark methods for predicting mineralogy from hyperspectral data. The data are stored as hycore (https://github.com/samthiele/hycore) Shed directories for easy loading, although individual MLA sections and corresponding hyperspectral images are all in ENVI format (so can be loaded by any hyperspectral analysis code or software). MLA outputs are also stored in their original (high-resolution) form as indexed bitmaps. The AbundanceMapping.xlsx file can be used to translate these MLA class indices into modal mineral abundances. Finally, jupyter notebooks used to derive the benchmarks presented in the paper are also included, in the Code folder. These illustrate how the data can be loaded and manipulated using hycore and hklearn (https://github.com/samthiele/hklearn), and used to train machine learning models that predict modal mineralogy given hyperspectral data

Techert Dataset: Phage Surface Display Next-Generation Sequencing Data from Biopanning Experiments against Eu3+

Phage Surface Display Next-Generation Sequencing data of screenings for peptides with a high affinity for Europium(III) ions (Eu3+). The experiments involved conducting multiple biopanning experiments with the Ph.D.-12 Phage Display Peptide Library ((Ph.D.™-12 Phage Display Peptide Library Kit, New England Biolabs GmbH, Frankfurt am Main, Germany (NEB)), employing diverse elution methods.This research received funding from the German Federal Ministry of Education and Research (BMBF), Grand number 031B1348A, 3145129048, 031B1506 and 031B0828A; as well as supported by the project FINEST of the Investment and Networking Fund of the Helmholtz Association under grant agreement no. KA2-HSC-10

Modbus device backend for the ChimeraTK framework

The modbus device backend implements a modbus client, that allows to create an ChimeraTK device that communicates with a modbus server. The implementation is based on the open source library libmodbus

Data publication: Short-Pulse High-Power THz Generation Using Optical Klystron FELs: Simulation Results

here are the FEL simulation results via Genesis simulation cod