241 to 250 of 374 Results
Feb 13, 2023 - C04: Pore-scale and REV-scale approaches to biological and chemical pore-space alteration in porous media
Keim, Leon; Class, Holger; Schirmer, Larissa; Wendel, Kai; Strauch, Bettina; Zimmer, Martin, 2023, "Data for: Measurement Campaign of Gaseous CO2 Concentrations in a Karst Cave with Aqueous Concentrations in a Stagnant Water Column 2021-2022.", https://doi.org/10.18419/DARUS-3271, DaRUS, V1
This dataset contains data generated during the measurement campaign inside the karst cave. The CO2 sensors in the cave air will continue to measure (as of Feb. 2023). For details on the site etc. see https://doi.org/10.3390/geosciences13020051 To create the graphs in the Class et al. 2023 Download cave-data.tar.xz, make sure you have the dependenc... |
Jan 30, 2023 - PN 7-6
Kneifl, Jonas; Rosin, David; Avci, Okan; Röhrle, Oliver; Fehr, Jörg, 2023, "Continuum-mechanical Forward Simulation Results of a Human Upper-limb Model Under Varying Muscle Activations", https://doi.org/10.18419/DARUS-3302, DaRUS, V1
This dataset provides simulation results from a high-fidelity human upper-arm finite element model under varying muscle activations and an example script to load the data. The upper arm model consists of the bones of the radius and ulna for the forearm and the humerus for the upper arm. The elbow joint that connects them is modeled as a simple hing... |
Jan 26, 2023 - DuMux
Kelm, Mathis; Ackermann, Sina; Buntic, Ivan; Coltman, Edward; Flemisch, Bernd; Gläser, Dennis; Grüninger, Christoph; Heck, Katharina; Hommel, Johannes; Keim, Leon; Kiemle, Stefanie; Koch, Timo; Lipp, Melanie; Schneider, Martin; Schollenberger, Theresa; Stadler, Leopold; Utz, Martin; Veyskarami, Maziar; Wang, Yue; Wendel, Kai; Werner, David; Wu, Hanchuan, 2023, "DuMux 3.6.0", https://doi.org/10.18419/DARUS-3247, DaRUS, V1
Release 3.6.0 of DuMux, DUNE for Multi-{Phase, Component, Scale, Physics, ...} flow and transport in porous media. DuMux is a free and open-source simulator for flow and transport processes in and around porous media. It is based on the Distributed and Unified Numerics Environment DUNE. |
Jan 25, 2023 - PN 4-4
Rosenfelder, Mario; Ebel, Henrik; Eberhard, Peter, 2023, "Experiment Videos of the Force-Based Non-Prehensile Cooperative Transportation of Objects with Mobile Robots", https://doi.org/10.18419/DARUS-3331, DaRUS, V1
The videos provided show two experimental results of cooperative object transportation using lightweight omnidirectional mobile robots. In particular, the mobile robots shall transport two different polygonal, but non-convex, objects along predefined paths. No central decision entity is employed; the tasks are accomplished in a purely distributed m... |
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 are also part of the dataset. The driving scenarios include critical,... |
Jan 11, 2023 - Materials Design
Jung, Jong Hyun; Srinivasan, Prashanth; Forslund, Axel; Grabowski, Blazej, 2023, "Data for: High-accuracy thermodynamic properties to the melting point from ab initio calculations aided by machine-learning potentials", https://doi.org/10.18419/DARUS-3239, DaRUS, V1
Data for the publication High-accuracy thermodynamic properties to the melting point from ab initio calculations aided by machine-learning potentials, npj Comput. Mater., DOI: 10.1038/s41524-022-00956-8 (2023) This data set contains - the training sets (VASP files), - the low moment-tensor-potentials (MTPs) and high-MTPs, - the effective quasiharmo... |
Dec 16, 2022 - demoa
Wochner, Isabell; Schmitt, Syn, 2022, "MPC/OC Code for: Learning with Muscles: Benefits for Data-Efficiency and Robustness in Anthropomorphic Tasks", https://doi.org/10.18419/DARUS-3268, DaRUS, V1
This code allows you reproduce the optimal control and model predictive control results of the paper: "Learning with Muscles: Benefits for Data-Efficiency and Robustness in Anthropomorphic Tasks" by Isabell Wochner, Pierre Schumacher, Georg Martius, Dieter Büchler, Syn Schmitt and Daniel F.B. Haeufle. Always cite the paper together with this datase... |
Nov 2, 2022 - PN 5-6
Praditia, Timothy; Karlbauer, Matthias; Otte, Sebastian; Oladyshkin, Sergey; Butz, Martin V.; Nowak, Wolfgang, 2022, "Replication Data for: Learning Groundwater Contaminant Diffusion-Sorption Processes with a Finite Volume Neural Network", https://doi.org/10.18419/DARUS-3249, DaRUS, V1
This dataset contains diffusion-sorption data, generated with numerical simulation based on three different sorption isotherms, namely the linear, Freundlich, and Langmuir isotherms. This dataset is used to train, validate, and test all the deep learning models that are used in the publication "Learning Groundwater Contaminant Diffusion-Sorption Pr... |
Oct 28, 2022 - FAIR Fluids
Gültig, Matthias; Range, Jan Peter; Schmitz, Benjamin; Pleiss, Jürgen, 2022, "Self-diffusion coefficients of simulated aqueous glycerol mixtures", https://doi.org/10.18419/DARUS-3115, DaRUS, V1
In order to make thermophysical properties of complex liquid mixtures available to a comprehensive analysis, we developed a data management and analysis platform based on the standard data exchange format ThermoML. The practicability of integrating thermophysical data from experiment and simulation was demonstrated for two binary mixtures, methanol... |
Oct 28, 2022 - FAIR Fluids
Gültig, Matthias; Range, Jan Peter; Schmitz, Benjamin; Pleiss, Jürgen, 2022, "Densities of simulated aqueous methanol mixtures", https://doi.org/10.18419/DARUS-3112, DaRUS, V1
In order to make thermophysical properties of complex liquid mixtures available to a comprehensive analysis, we developed a data management and analysis platform based on the standard data exchange format ThermoML. The practicability of integrating thermophysical data from experiment and simulation was demonstrated for two binary mixtures, methanol... |
