561 to 570 of 3,519 Results
Mar 25, 2026SFB 1333 "Molecular heterogeneous catalysis in confined geometries"
In this dataverse you can find all research data related to publications of the group generated within the context of CRC 1333. |
Mar 25, 2026SFB 1333 "Molecular heterogeneous catalysis in confined geometries"
In this dataverse you can find all research data related to publications of the group generated within the context of CRC 1333. |
Mar 25, 2026SFB 1333 "Molecular heterogeneous catalysis in confined geometries"
In this dataverse you can find all research data related to publications of the group generated within the context of CRC 1333. |
Mar 24, 2026 - Institute of Aerospace Thermodynamics
Molina Vogelsang, Pablo, 2026, "Replication Data for: Defocused Shadowgraphy μPTV for Analyzing the Spreading Mechanism of an Impacting Droplet onto a Thin Liquid Film", https://doi.org/10.18419/DARUS-5798, DaRUS, V1, UNF:6:5eUhuBlw71HogHgfbgwTDA== [fileUNF]
Related Publication Supplementary material to: P. Molina Vogelsang, T. Fuchs, S. Schubert & G. Lamanna “Defocused Shadowgraphy μPTV for Analyzing the Spreading Mechanism of an Impacting Droplet onto a Thin Liquid Film” DOI: 10.1615/AtomizSpr.2026064053 Dataset Description The data were obtained using shadowgraphy-based Defocusing Micro Particle Tra... |
Mar 24, 2026 - Timber Constructions
Klemm, Julian; Grönquist, Philippe; Hein, Helen Luisa; Nowak, Jana, 2026, "Python Source Code zu "Material-, Ressourcen- und Emissionseffizienz verschiedener Vollholzwerkstoffe für Stützen im mehrgeschossigen Holzbau"", https://doi.org/10.18419/DARUS-5524, DaRUS, V1
Python Source Code zu "Material-, Ressourcen- und Emissionseffizienz verschiedener Vollholzwerkstoffe für Stützen im mehrgeschossigen Holzbau" |
Mar 24, 2026 - Publication: Rigorous vertical averaging for pseudo-3D modeling of precipitation obstructed fluid flow
Krach, David; Weinhardt, Felix; Class, Holger; Steeb, Holger; Schneider, Martin, 2026, "Code and benchmarks for a vertically averaged drag term for pseudo-3D Stokes simulations", https://doi.org/10.18419/DARUS-5624, DaRUS, V1
Content: This data set includes snapshots of the code used to compute the benchmarks and applications in Krach et al (2026). The included software tools allow users to conduct pseudo-3D Stokes simulations with a geometry-informed drag term as well as analytical drag terms using DuMux. Users can determine permeability, volumetric flux, and local pre... |
Mar 24, 2026 - Publication: Rigorous vertical averaging for pseudo-3D modeling of precipitation obstructed fluid flow
Krach, David; Weinhardt, Felix; Class, Holger; Steeb, Holger; Schneider, Martin, 2026, "Results for pseudo-3D Stokes simulations using a vertically averaged drag term", https://doi.org/10.18419/DARUS-5626, DaRUS, V1
Content: This data set includes all the necessary input data to fully replicate the benchmarks and applications in Krach et al.(2026). In addition, the results for all solvers are provided as raw simulation output as well as in condense form. Complete datasets: 01_SINGLE_PRECIPITATE, 02_SEGMENT1, 03_SEGMENT2, 04_SEGMENT3, 05_WHOLE_DOMAIN Each data... |
Mar 20, 2026 - Institute of Engineering and Computational Mechanics
Krauspenhaar, Jasmin; Rosenfelder, Mario; Krendelsberger, Emilia; Eberhard, Peter, 2026, "Differential-Drive Robot Data Sets and Reference Trajectories for EDMD", https://doi.org/10.18419/DARUS-5799, DaRUS, V1
This repository provides real-world measurement data for data-driven modeling of a differential drive robot. The data sets are tailored for research on Extended Dynamic Mode Decomposition (EDMD) for control-affine systems. The repository contains data collected using two different sampling strategies. In the first strategy, arbitrary control inputs... |
Mar 20, 2026 - EXC IntCDC Associated Project 42 'Universal Timber Slab'
Zorn, Max Benjamin; Wortmann, Thomas, 2026, "Universal Timber Slab: Disciplinary Surrogate Models", https://doi.org/10.18419/DARUS-5801, DaRUS, V1
This dataset contains 9 trained surrogate models across all four disciplines predicting the performance of UTS bay elements and a demo Python script. Model Artifacts Each surrogate is saved as a .joblib file which stores: { 'model': , # Trained model 'scaler': , # Feature scaler 'feature_names': List[str], # 31 feature names 'model_name': str, # e.... |
