341 to 350 of 2,757 Results
Mar 15, 2025 -
DuMuX code for dual network for: "Local Thermal Non-Equilibrium Models in Porous Media: A Comparative Study of Conduction effects"
XZ Archive - 5.8 KB -
MD5: 266936cbce51808dcf4d1f975bb8fe32
All files related to installation of code via Docker. |
Mar 15, 2025 -
DuMuX code for dual network for: "Local Thermal Non-Equilibrium Models in Porous Media: A Comparative Study of Conduction effects"
Python Source Code - 4.9 KB -
MD5: cec0d7ef54445a987cd6f19bdf1b1723
Installation script for dumux module `Kostelecky2025a` and all related dumux and dune modules. |
Mar 15, 2025 -
DuMuX code for dual network for: "Local Thermal Non-Equilibrium Models in Porous Media: A Comparative Study of Conduction effects"
Markdown Text - 6.7 KB -
MD5: 458753947a320db45be4fc735f0e6346
Instructions to install and run simulations. |
Mar 4, 2025 - D03: Development and realisation of validation benchmarks
Kohlhaas, Rebecca; Morales Oreamuno, Maria Fernanda; Lacheim, Alina, 2025, "BayesValidRox 2.0.0", https://doi.org/10.18419/DARUS-4752, DaRUS, V1
Release 2.0.0 of BayesValidRox. BayesValidRox is an open-source python package that provides methods for surrogate modeling, Bayesian inference and model comparison. (2025-02-05) |
Mar 4, 2025 -
BayesValidRox 2.0.0
Gzip Archive - 142.8 KB -
MD5: b8a1b0a6df6184dd1cc2d58e8171026e
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Jan 30, 2025 - SFB1313: Project Area B01
Khurshid, Hamza; Polukhov, Elten; Keip, Marc-André, 2025, "Mixed variational formulation and finite-element implementation of second-order poro-elasticity: Datasets", https://doi.org/10.18419/DARUS-4485, DaRUS, V1
The datasets provided here are associated with our publication "Mixed variational formulation and finite-element implementation of second-order poro-elasticity". The main idea of the work is to develop a working model for second gradient poro-elasticity. This includes a mixed-variational formulation and finite-element formulation. The results are g... |
Jan 30, 2025 -
Mixed variational formulation and finite-element implementation of second-order poro-elasticity: Datasets
Hierarchical Data Format - 441.4 KB -
MD5: 0a28f536531bda27639a2f1aa0d235d5
Results for Terzaghi's quasi one-dimensional consolidation problem using the first-order formulation: The values of vertical strain, fluid mass content and pressure are given along the vertical central axis of the body at several values of the non-dimensional time during the consolidation process. The data corresponds to Fig. 4a,c,e in the paper. |
Jan 30, 2025 -
Mixed variational formulation and finite-element implementation of second-order poro-elasticity: Datasets
Hierarchical Data Format - 592.7 KB -
MD5: c53c179fd6195bcfb38f825a740650d1
Results for Terzaghi's quasi one-dimensional consolidation problem using the first-order formulation for a layered body: The values of vertical strain, fluid mass content and pressure are given along the vertical central axis of the body at several values of the non-dimensional time during the consolidation process. The data corresponds to Fig. 8a... |
Jan 30, 2025 -
Mixed variational formulation and finite-element implementation of second-order poro-elasticity: Datasets
Hierarchical Data Format - 346.5 KB -
MD5: a3f3aa54d544d45f2247ef614d608f9c
Results for the two-dimensional Mandel's problem using the first-order formulation for a layered body: The values of horizontal strain, fluid mass content and pressure are given along the horizontal central axis of the body at several values of the non-dimensional time during the consolidation process. The data corresponds to the Fig. 9a in the pap... |
Jan 30, 2025 -
Mixed variational formulation and finite-element implementation of second-order poro-elasticity: Datasets
Hierarchical Data Format - 459.9 KB -
MD5: 39d92f546c1e3a59ec843c576f69af4e
Results for the two-dimensional Mandel's problem using the first-order formulation: The values of horizontal strain, fluid mass content and pressure are given along the horizontal central axis of the body at several values of the non-dimensional time during the consolidation process. The data corresponds to Fig. 6a,c,e in the paper. |