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2037 research outputs found
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DuMuX code for: "Self-pumping transpiration cooling: A joint experimental and numerical study"
No full textThis dataset contains the source code used for the work "Härter, J., Veyskarami, M., Schneider, M. et al. Self-Pumping Transpiration Cooling: A Joint Experimental and Numerical Study. Transp Porous Med 152, 56 (2025). (https://doi.org/10.1007/s11242-025-02198-w)
Supplemental Materials for: Exploring the Sustainability Potential of Lightweight Structures made from Phormium Tenax Fibers by Hybrid Coreless Filament Winding
No full textThis dataset includes geometrical, structural, and physical data on lattice fiber-composite structures made from different fiber materials using hybrid coreless filament winding. In total, 20 samples were manufactured and tested under axial compression, with 5 samples per fiber material. The material-invariant geometrical model of the samples was used for process simulation and is based on a network modeling approach. The dataset consists of three TSV files: coordinates, graph, and samples.
The coordinates file lists the Cartesian coordinates in mm for 16 nodes used in the computational design of the samples. Each node is identified by a unique integer and has x, y, and z coordinates, with the z-axis pointing upward. Nodes 1 to 4 represent the bottom corner points of the sample geometry, nodes 5 to 8 are the top corner points, and nodes 9 to 16 are intermediate crossing points at the outer edges. Additional crossing points within the structure are not included, as no fiber deflection occurs there. While the nominal edge length of the samples is 110 mm, the coordinates require more decimal places due to scaling applied to align the total fiber length with empirically determined values.
The graph file describes the connectivity between nodes, representing the fiber net as an undirected graph. It includes the node identifier as given in the coordinates file and lists the connected nodes as comma-separated integers. Each connection is bidirectional and mentioned twice in the file. Together with the coordinates file, this file allows reconstruction of the spatial configuration of the samples, as shown in Figure 3 of the associated paper.
The samples file contains single-source-of-truth data on the structural performance and physical composition of the 20 samples, labeled by material and sample ID number, with "H" indicating heavy yarn Phormium tenax and "L" indicating light yarn Phormium tenax. It includes the structural stiffness in N/mm, the maximum force in N sustained during destructive axial compression testing before initial structural collapse, and the masses of fiber and resin in g per sample. The composite mass is the sum of fiber and resin masses, and the fiber mass ratio can be calculated by dividing fiber mass by composite mass. The global warming potential (GWP) of each sample can be calculated by adding the GWP of fiber and resin components, determined by multiplying the fiber or resin mass by their material-specific mass-specific GWP values given in Section 3 of the associated paper.</p
Experimental Data for: Characterization of splashing and regime thresholds for oblique droplet impact on thin wall films
No full textSupplementary material to Jonathan Lukas Stober, Maurizio Santini, Kathrin Schulte, "Characterization of splashing and regime thresholds for oblique droplet impact on thin wall films" (https://doi.org/10.1016/j.expthermflusci.2025.111493), referred to as "related publication" in the following:
- Shadowgraphy high-speed recordings from two perspectives of an oblique droplet impact onto a thin liquid film (13 experiments as shown and described in the related publication)
- Full impact parameters (impact angle, Weber number, film thickness, Ohnesorge number) and splashing type classification of over 600 experiments. Data to reproduce Figure 4 in the related publication
Replication Data for: mmcDropletSprayFoam v2406
No full textValidation Data for mmcDropletSprayFoam
This data set contains the DNS and LES cases for the double shear layer setup used to validate the mmcDropletSprayFoam solver of the mmcFoam v2406 version.
Requirements
To run the simulations you need to download and compile the mmcFoam software based on OpenFOAM v2312. This software is open-source upon registration. If you wish to use mmcFoam please contact:
Prof. Andreas Kronenburg: [email protected]
Prof. Matthew Cleary: [email protected]
Additional Libraries
This data set also includes following libraries:
ofReader: Python tools to post-process and read OpenFOAM files
sigmaTurbulenceModel: Implementation of the sigma turbulence model for OpenFOAM v2312 and OpenFOAM v2412, which is compatible with GCC 11.3 compiler.
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Interview Transcripts on Conditions For Study Success (WiSe 2023/24)
No full textThis dataset contains raw interview data (transcripts) from a seminar on study success requirements ("Lernerfolgsbedingungen im Hochschulstudium", Universität Stuttgart) in winter 2023/24. Students are narrating their experiences with a wide range of topics regarding their learning and study conditions, and their impact on study success.The interviews are best characterized as problem-centered interviews with strong narrative elements. The interviews were conducted online via the WebEx platform
Damped pendulum for nonlinear system identification - inputs are sampled from a multivariate-normal distribution - synthetically generated
No full textOverview
This dataset contains input-output data of a damped nonlinear pendulum that is actuated at the mounting point. The data was generated with statesim [1], a python package for simulating linear and nonlinear ODEs, for the system actuated pendulum. The configuration .json files for the corresponding datasets (in-distribution and out-of-distribution) can be found in the respective folders. After creating the dataset, the files are stored in the raw folder. Then, they are split into subsets for training, testing, and validation and can be found in the processed folder; details about the splitting are found in the config.json file.
The dataset can be used to test system identification algorithms and methods that aim to identify nonlinear dynamics from input-output measurements. The training dataset is used to optimize the model parameters, the validation set for hyperparameter optimization, and the test set only for the final evaluation.
In [2], the authors used the same underlying dynamics to create their dataset but without damping terms.
Input generation
Input trajectories are sampled from a multivariate-normal distribution.
Noise
Gaussian white noise of approximately 30dB is added at the output.
Statistics
The input and output size is one.
In-distribution data: 2 100 000 data points
Training: 10 000 trajectories of length 150
Validation: 2 000 trajectories of length 150
Test: 2 000 trajectories of length 150
Out-of-distribution data: 7 times 100 000 data points
7 different datasets were only used for testing. Each dataset contains 200 trajectories of length 500.
References
Frank, D. statesim [Computer software]. https://github.com/Dany-L/statesim
Lu, L., Jin, P., Pang, G., Zhang, Z., & Karniadakis, G. E. (2021). Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators. Nature machine intelligence, 3(3), 218-229.
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Data Sets for a Fractional Moisture-Dependent Viscoelasticity Model for Thermoplastic Polymers
No full textThis dataset contains Dynamical Mechanical Thermal Analysis (DMTA) results, including the determination of the complex Young’s modulus and complex shear modulus from torsion and tension measurements. The dataset is associated with the publication Fauser et al. (2025) and complements the dataset “A Modular Framework for Non-Linear Optimization of Linear Viscoelastic Model Parameters Using a Second-Order Fractional Approach”.
Frequency-dependent measurements were performed using a rheometer (MCR 702, linear motor, Anton Paar, Graz, Austria) over a frequency range of 0.1 s⁻¹ to 10 s⁻¹. The applied strain amplitudes were ε = 0.01 % (axial) and γ = 0.01 % (shear). Measurements were conducted at constant temperatures within the glass transition range of the respective polymers.
Master curves were generated from frequency sweeps of the complex Young’s and shear moduli for cylindrical samples (BASF, Ludwigshafen, Germany) of:
Poly-l-actic acid (PLA)
Poly-ethylene terephthalate (PET)
Poly-amid (PA)
and for rectangular samples of:
Thermoplastic poly-urethane (TPU, SMP Technologies Inc., Tokyo, Japan)
TPU samples were analyzed at varying moisture contents (in 10 % RH increments). The corresponding state-dependent shift factors used for master curve generation are also provided in the dataset.</p
Experimental data for solute redistribution below evaporating surfaces
No full textThis dataset contains all the relevant data pertaining to the non-invasive imaging of solute redistribution below evaporating surfaces using Na-MRI. The main folders include:
Sand properties of the two sands used i.e. F36 and W3
Calibration experiments performed to generate calibrations curves for Na concentration in F36 and W3
Na-imaging of evaporation experiment performed on F36 using NaCl solution along with the mass balance data and the data processing file
Na-imaging of evaporation experiment performed on W3 using NaCl solution along with mass balance data and the data processing file
Each folder contains a readme file giving description to the data contained in that folder, information on relevant file names and other context.</p
Replication Data for: High Gain Operational Amplifier Using Enhancement and Depletion Mode a-IGZO TFTs
No full textThis dataset holds all measurement data, as well as the evaluation and plotting scripts to replicate all figures of the paper.
The data is structered in folders of the figures. In the most cases a folder inlcudes the figure (.pdf), a subfolder named Plot and a subfolder named Raw data and evaluation script. The Plot folder contains the plotting script (.py) and the plotted data (.txt). The Raw data and evaluation script folder contains the recorded (unprocessed) data (.csv, .xlxs) and a python script (.py), which generates the plotted data from the raw data
Replication Data for: Properties of COOH-Functionalized Silica: A Particularly Weak Solid Acid
No full textAll primary data files of measurements and processed data of the journal article mentioned under related publications from Estes group can be found here. The data is structured according to the respective analytical methods in the research article and contains the following data types (format): (IR, potentiometric titration (.csv), NMR (bruker, .mnova). Also python scripts and jupyter notebooks were added as used to create plots and calculate data (.py, .ipynb). I recommend to view the data as "tree"