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Data for: Validating a Numerical Model for Calco-Carbonic Reactive Flow in a Laboratory Scale Fracture Analog
No full textThis dataset contains laboratory‑experiment data and simulation data for the publication
Validating a Numerical Model for Calco‑Carbonic Reactive Flow in a Laboratory Scale Fracture Analog.
For missing details see the attached README.md
To create the graphs in the publication, download fracture_analog.tar.xz.
Make sure that you have the dependencies shown in requirements_paper_2025.txt.
The scripts can recreate the graphs directly with Python.
The structure of the dataset in fracture_analog.tar.xz is as follows:
experimentalDataset – contains the relevant experimental data set.
ftb – contains the simulation data organized in folders for each case.
python – contains Python scripts that allow reproducing the publication graphs. Data created with the scripts are also saved in dedicated folders.
stg – contains the initializers for the simulations in ftb.
Create Figures
To reproduce the plots and figures in the paper, go into the folder containing the Python scripts:
cd fracture_analog/python
Typical usage (run from the python folder):
python plot.py # shows help
python plot.py time --show --save
python plot.py da --save --out-dir ./my_figures
python plot.py sherwood
If neither --save nor --show is supplied, the script defaults to both actions.
Python scripts (folder python)
The main entry point is plot.py. It provides three sub‑commands:
time – concentration vs. time.
da – Damköhler‑number curves.
sherwood – Sherwood‑number curves.
Common options (apply before the sub‑command):
--save – write each figure to a PDF (default folder: fracture_analog/python/figures).
--show – display the interactive Matplotlib window.
--out-dir DIR – destination directory for saved PDFs (used with --save).
Additional plotting scripts:
plot_outlet_contours.py – generates Figure D2.
plot_plug_flow_contours.py – generates Figure D3.
Reproducing the VTK files
The raw simulation outputs have the extension “.dumux” (e.g., t_u_list-00000.dumux).
If you are interested in inspecting the VTK contour plots we recommend the following strategy:
Clone the repository:
https://git.iws.uni-stuttgart.de/dumux-pub/wendel2025a
into a folder that contains only the code.
Enter the cloned folder (wendel2025a) and execute the install script:
./installscript.sh
This will clone, configure, and build the main parts.
Build the VTK reconstruction binary:
cd ~/wendel2025a/build-cmake/appl/forcedtoptobottom/ftb2Dyz/
make Reconstructvtk_forcedflow_toptobottom_2DYZ
Run the binary on a case folder, supplying the full path:
/full/path/to/Reconstructvtk_forcedflow_toptobottom_2DYZ -i params.input -o output.vtk
You can customize the VTK output granularity via the SteppingStride parameter. Edit params.input or pass the option directly, e.g.:
cd fracture_analog/ftb/examplecase
/full/path/to/Reconstructvtk_forcedflow_toptobottom_2DYZ -BinarySolutionLoad.SteppingStride 10
Because DuMux is not installed system‑wide, always use the full path to guarantee the correct binary is executed.
Helper scripts
calculate_Sherwood_theory_numbers.py – computes the data shown in Tables 3 and 4; saves CSV files in fracture_analog/python/sherwood_calculations.
compare_experiment_simulation.py – conducts an error analysis comparing experimental and simulation outcomes; saves
comparison_results_transient.csv (Table C1) and comparison_results.csv (stationary outlet concentrations).
get_preexperimental_inlet_fluid_pH.py – estimates the pH of the process fluid (appears in Table 1, column pH_in); prints to console.
get_surface_retreat.py – estimates the surface retreat expected in the experiments; prints to console.
interpolate_co2_diffusion_coefficients.py – fits CO₂‑diffusion coefficients and prints the parameters.
investigate_recirculation.py – performs various analyses and produces CSV or console output useful for supplementary tables; creates recirculation_table.csv containing Richardson numbers (Table D2).
Folder‑structure summary
Inside the stp folder are the initialization simulation raw data sets; the ftb folders then contain the setups where the pump has been switched on in the experiment.
Both folders use the pattern groupName__caseLabel (e.g., dwIniClo__a6mm_pC1.0_T22).
The caseLabel encodes the main characteristics of the simulation:
aXmm – aperture in mm (X mm).
pCY.YY – CO₂ partial pressure in bar (Y.YY bar).
TZZ – temperature in °C (ZZ °C).
tAAmin – residence time in minutes (AA min).
Note that a stagnant simulation setup does not need a mean residence time, so this attribute is omitted.
For the stagnant‑prior (stg) cases, the recommended binary is:
build-cmake/appl/stagnant/stg2Dyz/ReconstructVtk_stagnant_2DYZ
Detailed explanation: Data structure of the ftb data sets
Each case folder under ftb contains the following files:
params.input – parameter file used to run the simulation in DuMux.
integralQuantities.csv – processing information on the outflowing fluid and total mass/moles inside the model domain.
recirculation.csv – time series of the upper and lower bounds of recirculation patterns; an absent row means no recirculation at that time.
sherwood.csv – processing information for the Sherwood number study (section 3.4.4).
systemcharacteristicnumbers.txt – a‑priori characteristic system information.
</ul
Data for: Thermal behaviour of switchable liquid crystal glazing under real environmental conditions
No full textThis dataset documents a three-year measurement series (January 2022 to December 2024) examining the thermal behavior of switchable liquid crystal glazing (eyrise S350) under real environmental conditions at a test facility in Stuttgart, Germany. Temperature measurements were conducted on double glazing units (DGU) and triple glazing units (TGU) installed in a south-facing facade test building with an 84% window-to-wall ratio.
The study focused on heat distribution within the glazing assembly and the potential risk of thermal stress fractures caused by solar radiation absorption, particularly in clamped and shaded edge zones. A total of 80 Type K thermocouples were strategically positioned across eight glazing units to monitor temperatures in two distinct layers within the glass pane structure. The monitoring campaign captured temperature extremes ranging from -9.9°C to 79.9°C (TGU) and -7.3°C to 77.2°C (DGU), with critical temperature differences between center and edge areas reaching up to 47.9°C
Zentrale Orte und innergemeindliche Zentralitäten in Deutschland für die Anwendung der RIN
No full textGeodaten und Methoden zur Ermittlung innergemeindlicher Zentralitäten für die innergemeindliche Netzgestaltung nach den Richtlinien für integrierte Netzgestaltung (RIN 2008). Wesentlicher Bestandteil dieses Datensatzes ist die Intensitätsschätzung von Standorten der Daseinsvorsorge in fünf Funktionsbereichen (Behörden&Dienstleistungen, Bildung, Einzelhandel&Nahversorgung, Freizeit&Kultur, Gesundheit&Soziales)
Supplemental data for "Accelerating Conjugate Gradient Solvers for Homogenization Problems with Unitary Neural Operators"
No full textThis repository contains supplemental data for the article
Accelerating Conjugate Gradient Solvers for Homogenization Problems with Unitary Neural Operators,
accepted for publication in the International Journal for Numerical Methods in Engineering (IJNME) by Julius Herb and Felix Fritzen [1].
In this publication, we introduce UNO-CG, a hybrid solver that accelerates conjugate gradient (CG) solvers using specially designed machine-learned preconditioners, while ensuring convergence by construction.
As a preconditioner, we propose Unitary Neural Operators (UNOs) as a modification of the established Fourier Neural Operators.
Our method can be interpreted as a data-driven discovery of Green's functions, which are then used much like expert knowledge to accelerate iterative solvers.
The data contained in this DaRUS repository acts as an extension to the GitHub repository that contains a software package for UNO-CG, including a GPU-accelerated implementation of the hybrid solver in PyTorch, an implementation for PETSc, and the training procedures proposed in our article.
All results and figures in the article can be reproduced using the mentioned software package together with the data sets available in this DaRUS repository.
As part of the training data and evaluation data, we consider bi-phasic two-dimensional microstructures with a resolution of 400 × 400 pixels, as published in [2], and three-dimensional microstructures with a resolution of 192 × 192 × 192 voxels, as published in [3].
Further information is available in the `README.md` file of this repository.
[1] Herb, J. and Fritzen, F. (2026), Accelerating Conjugate Gradient Solvers for Homogenization Problems with Unitary Neural Operators. Int J Numer Methods Eng. https://doi.org/10.1002/nme.70277
[2] Lißner, J. (2020). 2d microstructure data (Version V2) [dataset]. DaRUS.
https://doi.org/10.18419/DARUS-1151
[3] Prifling, B., Röding, M., Townsend, P., Neumann, M., and Schmidt, V. (2020). Large-scale statistical learning for mass transport prediction in porous materials using 90,000 artificially generated microstructures [dataset]. Zenodo. https://doi.org/10.5281/zenodo.4047774</a
Atmospheric transmission models for FIFI-LS: altitude 37kft, 39deg O3
No full textThis dataset contains atmospheric transmission models calculated by a modified version of ATRAN ("SDC ATRAN"). They are to be used as the default models for FIFI-LS data reduction with SOFIA Redux
The models are generated from a modified "SDC ATRAN" model based on Steve Lord's ATRAN. The most significant modification is a correction of 17O16O isotopologue abundance coefficients.
The models are stored in FITS binary table format with the columns "wavelength" and "transmission". All models of the same altitude (and same wavelength range) have the same number of wavelength elements.
File naming convention:
atran_sdc_xxK_yydeg_zzpwv_39deg_2nlayer_40-300mum_bt.fits
where xx is the flight altitude in kft, yy is the zenith angle in degree, and zz the
precipitable water vapor value in micron.
This dataset contains models calculated for a 2 layer atmospheric model with an ozone profile identified by "39deg".</p
Atmospheric transmission models for FIFI-LS: altitude 39kft, 39deg O3
No full textThis dataset contains atmospheric transmission models calculated by a modified version of ATRAN ("SDC ATRAN"). They are to be used as the default models for FIFI-LS data reduction with SOFIA Redux
The models are generated from a modified "SDC ATRAN" model based on Steve Lord's ATRAN. The most significant modification is a correction of 17O16O isotopologue abundance coefficients.
The models are stored in FITS binary table format with the columns "wavelength" and "transmission". All models of the same altitude (and same wavelength range) have the same number of wavelength elements.
File naming convention:
atran_sdc_xxK_yydeg_zzpwv_39deg_2nlayer_40-300mum_bt.fits
where xx is the flight altitude in kft, yy is the zenith angle in degree, and zz the
precipitable water vapor value in micron.
This dataset contains models calculated for a 2 layer atmospheric model with an ozone profile identified by "39deg".</p
Atmospheric transmission models for FIFI-LS: altitude 41kft, 39deg O3
No full textThis dataset contains atmospheric transmission models calculated by a modified version of ATRAN ("SDC ATRAN"). They are to be used as the default models for FIFI-LS data reduction with SOFIA Redux
The models are generated from a modified "SDC ATRAN" model based on Steve Lord's ATRAN. The most significant modification is a correction of 17O16O isotopologue abundance coefficients.
The models are stored in FITS binary table format with the columns "wavelength" and "transmission". All models of the same altitude (and same wavelength range) have the same number of wavelength elements.
File naming convention:
atran_sdc_xxK_yydeg_zzpwv_39deg_2nlayer_40-300mum_bt.fits
where xx is the flight altitude in kft, yy is the zenith angle in degree, and zz the
precipitable water vapor value in micron.
This dataset contains models calculated for a 2 layer atmospheric model with an ozone profile identified by "39deg".</p
Atmospheric transmission models for FIFI-LS: altitude 43kft, 39deg O3
No full textThis dataset contains atmospheric transmission models calculated by a modified version of ATRAN ("SDC ATRAN"). They are to be used as the default models for FIFI-LS data reduction with SOFIA Redux
The models are generated from a modified "SDC ATRAN" model based on Steve Lord's ATRAN. The most significant modification is a correction of 17O16O isotopologue abundance coefficients.
The models are stored in FITS binary table format with the columns "wavelength" and "transmission". All models of the same altitude (and same wavelength range) have the same number of wavelength elements.
File naming convention:
atran_sdc_xxK_yydeg_zzpwv_39deg_2nlayer_40-300mum_bt.fits
where xx is the flight altitude in kft, yy is the zenith angle in degree, and zz the
precipitable water vapor value in micron.
This dataset contains models calculated for a 2 layer atmospheric model with an ozone profile identified by "39deg".</p
DEM from LP - National Meteorological Administration of Romania
No full textThis dataset belongs to the EU Interreg project and contains DEM and DSM data from Romanian Pilot Areas (first snapshot); more at https://interreg-danube.eu/projects/transfer-danube</a
Supporting data for 'Mutual Linearity is a Generic Property of Steady-State Markov Networks'
No full textSupporting numerical data. It includes the processed data used to produce the figures in the main text of the accompanying paper