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Replication Data for: Implementation Pitfalls for Carbonate Mineral Dissolution – a Technical Note
No full textThis dataset contains the complete postprocessing workflow for the manuscript "Implementation Pitfalls for Carbonate Mineral Dissolution - a Technical Note". It includes the raw simulation results as CSV files, the Python plotting script for data visualization, and the resulting publication-ready figure.
Contents
Raw simulation results (CSV files)
Python plotting script
Final figure (PDF format)
Requirements
Python libraries:
matplotlib
pandas
numpy
LaTeX
Data Description
The CSV files contain numerical results from calcite dissolution simulations performed using DuMuX and Reaktoro. The Python script processes these data and generates a comparison plot that is used in the manuscript to illustrate the differences between various implementation approaches.
Usage
To generate the plot, simply run:
python3 plotting.py</pre
Supporting data for 'Negative drag force on beating flagellar-shaped bodies in active fluids'
No full textSupporting experimental and simulation data. It includes the processed data used to produce the figures in the main text and supplemental material of the accompanying paper
Data used in and produced during calibration and validation of the model for bioconcrete production
No full textData used in and produced during calibration and validation of the model for bioconcrete production in the paper "A numerical 1D reactive flow and transport model for cementation processes in bio-concrete production".
Experimental data is derived from Smirnova et al. 2023.
The experiment TR1-2 was used to calibrate the developed model for bioconcrete production using PEST. Other experimental setups (TR3-1, TR5-3, TR5-6, and TR7-2) were used for validation of the model. The experimental data used are the injection rates over time, the average sample porosities over time, and the final porosity distribution along the sample length.
Content of this data set:
The subfolder "pestFiles" contains files used by and generated by PEST for the calibration of the bioconcrete production model. The files are:
"Biocement.pst" is the pest control file where all parameters for the calibration are set and the observation data is defined.
"Biocement_inj.ins": a PEST instruction file for the injection data.
"Biocement_poro.ins": a PEST instruction file for the porosity data.
"Biocement.tpl": a template of the bioconcrete production model's input file into which PEST inserts the parameter values it determined to be evaluated in the bioconcrete production model.
"Biocement.rec": the log file of the model calibration by PEST, containing e.g. the updated calibration-parameter estimates, parameter updates, parameter standard deviations and more.
"Biocement.isen": a PEST output file giving the model sensitivities to the calibration parameters for each iteration.
"Biocement.post.cov": a PEST output file giving the calibration parameter's covariances after the calibration.
"coVars.txt": the covariances from "Biocement.post.cov" normalized using the script "normalizeCovariances.py"
"normalizeCovariances.py": the script used to normalize the covariances
"contributionsPorosity.txt": the contributions of the porosity values to the objective function as calculated by PEST
"contributionsInjections.txt": the contributions of the injection values to the objective function as calculated by PEST
"Asw.txt": the values of the fitted parameter Asw (initial surface area) extracted from "Biocement.rec" for each iteration
"Expo.txt": the values of the fitted parameter E (exponent of the porosity-permeability relation) extracted from "Biocement.rec" for each iteration
"kpowder.txt": the values of the fitted parameter kpowder (the maximum reaction rate constant of the ureolysis rate equation at room temperature) extracted from "Biocement.rec" for each iteration
Subfolders in the format "trx-y" for each experimental setup which contains the experimental and model data used in the paper "A numerical 1D reactive flow and transport model for cementation processes in bio-concrete production".
Those are named "tr1-2", "tr3-1", "tr5-3", "tr5-6", and "tr7-2", based on the names for the experimental setups in Smirnova et al. 2023.
Each folder contains:
"avgporoexperiment.txt": the average porosity of the setup determined experimentally for each injection cycle.
"avgporoovertime.txt": the average porosity of the setup based on the model results for every time step of the simulation.
"finalporosities.txt": the final porosity distribution over the length of the sample as predicted by the model.
"injectionovertime.txt": the injection rates over time as predicted by the model.
"refinjectionrates.txt": the experimentally measured injection rates for each injection cycle.
"trx-y.input": the input file used for the model for the given setup.
The folder "tr1-2" additionally contains a subfolder "initialGuess" that contains the model results and input file for the initial-guess values of the calibration parameters, but no experimental data (only "avgporoovertime.txt", "finalporosities.txt", "injectionovertime.txt", and "tr1-2.input").
The folder "tr1-2" additionally contains the data file "finalporosities_experiment.txt" with the final porosity distribution along the column measured in the experiment
the python script "plots.py" that reads in the experimental and model data from the folders and files discussed above and plots them.
"finalAveragePorosities.csv" contains a table of all final average porosities measured in the experiment and predicted by the model as well as the error in the model prediction calculated based on the given values.
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Datasets: 100 Heat Pumps + Real Permeability Fields, Simulation - Raw, 4 + 1 Data Points
No full textThis data set serves as training and testing data for modelling the temperature field emanating from open loop groundwater heat pumps (100, randomly placed). It is simulated with Pflotran and saved in h5 format. It contains 4 + 1 data points, each consisting of one simulation run until a quasi-steady state is reached. Each data point measures 12.8 km x 12.8 km x 5 m with 2560 x 2560 x 1 cells. The 5th data point is intended for scalability test with the same parameters except for the size of 25.6 km x 12.8 km x 5 m with 5120 x 2560 x 1 cells. The varying parameters of the data sets are the positions of the heat pumps and a heterogeneous permeability field (cut from real permeability fields of the region of Munich). Other parameters that define the data sets, such as porosity and hydraulic pressure gradient are chosen to be as close as possible to reality.
Source: "Die hydraulischen Grundwasserverhältnisse des quartären und des oberflächennahen tertiären Grundwasserleiters im Großraum München", Geologica Bavarica Volume 122.</br
Trained Models on Real Permeability Fields, 4+1 Data Points
No full textModels are trained with Heat Plume Prediction on 4 data points (dp).
Steps 1 and 3 of LGCNN (Local Global Convolutional Neural Network) are separate, step 2 is a numerical solver that does not require any trained model.
For inference follow the guidelines of Heat Plume Prediction and applied all 3 steps/models sequentially to your input data.
Based on raw data from https://doi.org/10.18419/darus-5065
Replication Data for: "Show Me Your Best Side: Characteristics of User-Preferred Perspectives for 3D Graph Drawings"
No full textWe provide the supplemental material for our paper 'Show Me Your Best Side: Characteristics of User-Preferred Perspectives for 3D Graph Drawings', allowing additional analysis and replication. The files and collections are described in 'README.txt'
H-Band InGaAs Power Amplifier M260-003: AM/AM AM/PM-Characterization and Behavioral Modeling
No full textThis dataset documents the characterization of an InGaAs power amplifier with a calibratied vectorial network analyzer.
The measurements are characterized with an VNA-Testbench with WR-3.4 extension modules.
The Device under Test (DUT) is an Medium Power Amplifier in an 35-nm InGaAs mHEMT technology by Fraunhofer IAF, packaged in a split-block waveguide housing. The serial number of the MPA is M260-003
Replication Data for: Compensation of the Transmission Errors in Electrically Preloaded Rack-and-Pinion Drives
No full textThis dataset contains all experimental data that is shown within the paper "Compensation of the Transmission Errors in Electrically Preloaded Rack-and-Pinion Drives".
Large machine tools with long feed axes commonly utilize electrically preloaded rack-and-pinion drives, where two redundant drives on one axis are preloaded against each other by a dedicated control loop. Manufacturing- and assembly-related tolerances and elastic deformations lead to transmission errors in the drivetrains, that limit the achievable accuracy. Due to the limited bandwidth of the position control, these cannot be fully suppressed despite the use of linear measuring systems. In this paper, the superposition of the individual errors of the two drivetrains of electrically preloaded systems and the transfer function to the resulting path errors are theoretically derived in a first step. In addition, the negative effect of transmission errors on the preload control is investigated. To address the issue, an error compensation for the position and preload control loops is implemented. The basis for this are models of the state-dependent transmission errors utilizing a two-stage machine learning approach. The achievable reduction of path errors is validated in experimental investigations on a test bench and averages 57.8 % for the measurements conducted.
The data are structured to correspond to the figures in the publication and are available in CSV format:
Fig. 3 Transmission error measurements:
Measured transmission errors of both drivetrains for both directions of transmitted force under varying preload torque.
Fig_3_TE_measurements_D2_positive: Transmission errors of drive 2 for positive transmitted forces
Fig_3_TE_measurements_D2_negative: Transmission errors of drive 2 for negative transmitted forces
Fig_3_Supplement_TE_measurements_D1_positive: Transmission errors of drive 1 for positive transmitted forces (not shown in the publication)
Fig_3_Supplement_TE_measurements_D1_negative: Transmission errors of drive 1 for negative transmitted forces (not shown in the publication)
Fig. 4 Superimposed transmission errors:
Individual transmission errors of both drivetrains and corresponding superimposed overall error for an exemplary motion.
Fig. 6 Path errors:
Comparison of calculated and measured path errors for different velocities with no external load.
Fig. 7 Preload errors:
Control error of the preload control for different velocities with no external load.
Fig. 10 Compensation validation motion:
Evaluation of the compensation of the path and preload errors for an exemplary linear motion with an external load.
Fig. 11 Compensation validation overall:
Evaluation of the improvement of the path accuracy by the compensation for varying external loads and velocities. There is one file for each of the three examined velocities of 50 mm/s, 75 mm/s and 100 mm/s.
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Replication Scripts for: "Collective variables of neural networks: empirical time evolution and scaling laws"
No full textThe scripts provided here are for reproducing the results and plots of "Collective variables of neural networks: empirical time evolution and scaling laws" (https://doi.org/10.1088/2632-2153/adee76
Replication Data for: PLIC-based contact line modeling for simulations of droplet impact onto smooth and structured surfaces
No full textSupplementary material to a submitted manuscript [full citation link follows when it is published] J. Wurst, M. Dreisbach, A. Stroh, J. Kriegseis, K. Schulte, "PLIC-based contact line modeling for simulations of droplet impact onto smooth and structured surfaces", referred to as "related publication" in the following:
A framework for the simulation of droplet impacts onto smooth and structured surfaces is developed within the in-house flow solver "Free Surface 3D" (FS3D). The code solves the incompressible Navier-Stokes equations in a one-field formulation and the Volume of Fluid (VoF) method. The dataset contains the raw simulation data (hdf5-files) of certain timesteps as well as images and text files directly used in the manuscript. The hdf5-data can be viewed with paraview by loading the fs3d.xmf file.
In particular, the data set contains the following cases:
-convergence tests results (textfile)
-sessile drop (textfile)
-square capillary (textfile)
-droplet impact smooth surface (textfile)
-grid study (textfile)
-droplet impact grooves (textfile, hdf5-files, png-files)
The simulation was performed as part of Germany’s Excellence Strategy
under Grant EXC 2075 - 390740016 within the subproject PN1-2(II)