21 to 30 of 101 Results
Jun 27, 2023 - Scientific Computing
Pollinger, Theresa, 2023, "Replication Data for: Leveraging the compute power of two HPC systems for higher-dimensional grid-based simulations with the widely-distributed sparse grid combination technique", https://doi.org/10.18419/darus-3393, DaRUS, V1
We ran different fractions of the combination technique scenario described in the publication, also widely-distributed between the two machines SuperMUC-NG (file suffix `_ng`) and Hawk (file suffix `_hawk`). The dataset contains input files to generate the scenarios on the respec... |
May 31, 2023
|
May 4, 2023 - Publication: Particulate systems
Ruf, Matthias; Taghizadeh, Kianoosh; Steeb, Holger, 2023, "micro-XRCT data sets and in situ measured ultrasonic wave propagation of pre-stressed monodisperse rubber and glass particle mixtures with 10%, 20%, and 30% volume rubber content: samples 2 and 3", https://doi.org/10.18419/darus-3437, DaRUS, V1, UNF:6:LyvQPm+lMHvJ9Z4neyxihA== [fileUNF]
This dataset contains 12 micro X-ray Computed Tomography (micro-XRCT) data sets from scans of cylindrical particulate mixture samples (diameter 80 mm; unloaded height 80 mm) under different uniaxial compression loads. The samples consist of monodisperse stiff (glass) and soft (ru... |
May 4, 2023 - Publication: Particulate systems
Ruf, Matthias; Taghizadeh, Kianoosh; Steeb, Holger, 2023, "micro-XRCT data sets and in situ measured ultrasonic wave propagation of pre-stressed monodisperse rubber and glass particle mixtures with 10%, 20%, 40%, and 60% volume rubber content: sample 1", https://doi.org/10.18419/darus-3436, DaRUS, V1, UNF:6:B6SNTx6Co9kvBdKrEWyCwA== [fileUNF]
This dataset contains 8 micro X-ray Computed Tomography (micro-XRCT) data sets from scans of cylindrical particulate mixture samples (diameter 80 mm; unloaded height 80 mm) under different uniaxial compression loads. The samples consist of monodisperse stiff (glass) and soft (rub... |
Apr 20, 2023 - Publications
Pollinger, Theresa, 2022, "Replication Data for: A mass-conserving sparse grid combination technique with biorthogonal hierarchical basis functions for kinetic simulations", https://doi.org/10.18419/darus-2790, DaRUS, V2
Replication data for advection, Landau damping, and two-stream instability experiments with mass-conserving basis functions (vs hat functions) in the combination technique. The simulations are based on the DisCoTec and SeLaLib codes. If you want to re-generate the numerical data,... |
Mar 9, 2023 - NMR investigation of water confined by salt interface
Gravelle, Simon; Holm, Christian; Schlaich, Alexander, 2023, "Molecular simulation scripts for slit nanopores", https://doi.org/10.18419/darus-3180, DaRUS, V1
GROMACS molecular simulation input files for slit nanopores made of NaCl and Na2SO4 solid walls, and filled with respectively NaCl and Na2SO4 solutions. Initial configuration with a given salt concentration can be generated using the Python script ConfigurationGenerator.py, and s... |
Mar 8, 2023 - NMR investigation of water confined by salt interface
Gravelle, Simon; Holm, Christian; Schlaich, Alexander, 2023, "Molecular simulation scripts for bulk solutions", https://doi.org/10.18419/darus-3179, DaRUS, V1
GROMACS molecular simulation input files for bulk solutions of NaCl and Na2SO4. Initial configuration with different salt concentration can be generated using the Python script ConfigurationGenerator.py, and successive GROMACS runs can be performed by running the runall.sh Bash s... |
Mar 3, 2023C01: A Lattice-Boltzmann investigation of two-phase electrolyte flow in porous structures with morphology alterations and tunable interfacial wetting behaviour
Molecular dynamics input scripts. |
Feb 20, 2023 - PN 6
Zaverkin, Viktor; Holzmüller, David; Bonfirraro, Luca; Kästner, Johannes, 2023, "Pre-trained and fine-tuned ANI models for: Transfer learning for chemically accurate interatomic neural network potentials", https://doi.org/10.18419/darus-3299, DaRUS, V1
Pre-trained and fine-tuned ANI models using the Gaussian Moments Neural Network (GM-NN) approach. Code for GM-NN implemented within the Tensorflow framework, including the respective documentation and tutorials, can be found on GitLab. The data represents TensorFlow v2 checkpoint... |
Jan 17, 2023 - PN 7-6
Rodegast, Philipp; Maier, Steffen; Kneifl, Jonas; Fehr, Jörg, 2023, "Simulation Data from Motorcycle Sensors in Operational and Crash Scenarios", https://doi.org/10.18419/darus-3301, DaRUS, V1, UNF:6:rnJlYpzgwi2nYAtNG7jBtA== [fileUNF]
This dataset provides time-dependent simulation results from high-fidelity motorcycle body crash scenarios. The set contains the angular as well as linear positions, velocities, and accelerations of different parts of the motorcycle. In addition, force and contact sensor signals... |