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501 to 510 of 679 Results
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 26, 2023 - DuMux 3.6.0
Gzip Archive - 28.8 MB - MD5: 17b79c3cb624c650799303cf15a0ae37
Dec 7, 2022 - Cyclorotor
Schließus, Julian; Gagnon, Louis, 2022, "Data for: Create a Fluid-Structure Simulation Framework for Cycloidal Rotors", https://doi.org/10.18419/DARUS-2232, DaRUS, V2
OpenFOAM simulation case files and results accompanying the article having the same title are included. The pressure and velocity fields could be visualized by using Paraview. The cases are described in the Bachelor Thesis referenced under Related Publication. For details how to use and access the data and code, please see the instructions in the f...
Gzip Archive - 4.8 MB - MD5: c94fb54e67ee8ea3539fca2adc2ee57b
Code
Base case setup
Oct 28, 2022 - B03: Heterogeneous multi-scale methods for two-phase flow in dynamically fracturing porous media
Burbulla, Samuel; Hörl, Maximilian; Rohde, Christian, 2022, "Source Code for: Flow in Porous Media with Fractures of Varying Aperture", https://doi.org/10.18419/DARUS-3012, DaRUS, V1
Python source code to replicate the results in [S. Burbulla, M. Hörl, and C. Rohde (2022). "Flow in Porous Media with Fractures of Varying Aperture." Submitted for publication, https://doi.org/10.48550/arXiv.2207.09301]. The contained Python package "mmdgpy" is an implementation of interior penalty discontinuous Galerkin (dG) schemes in 2D and 3D f...
Shell Script - 245 B - MD5: 0bbed9e11ddfc8a5b237a990906f8d9a
installation script
Gzip Archive - 34.2 KB - MD5: da128f7d2b751d962699684242e15c5a
source code
Oct 26, 2022 - Projects without PN Affiliation
Davis, Kyle; Schulte, Miriam, 2022, "Replication Data for: Geothermal-ML - predicting thermal plume from groundwater heat pumps", https://doi.org/10.18419/DARUS-3184, DaRUS, V1
This dataset provides all python3 code necessary to train convolutional neural networks built with pyTorch, as well as the data to train the networks. A README file is provided with instructions for accessing the training data, viewing the alread computed models, and all software dependency versions.
Python Source Code - 8.2 KB - MD5: 5420869a7ca34ac2b7a71749dcf2145c
Gzip Archive - 406.8 MB - MD5: 546150f2eb93e82729ac0279a15326c1
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