Stuttgart Center for Simulation Science (SC SimTech)

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20,991 to 21,000 of 21,173 Results

master_curve_SMP_redry.tab Jun 22, 2021 - Humidity and thermal triggered Shape Memory Effect - Rheology-based numerical modelling - Dynamic Mechanical Thermal Humidity Analysis Tabular Data - 21.8 KB - 11 Variables, 280 Observations - UNF:6:5mNcDmwEWTJUJ+8sSAOpJA==
master_curve_SMP_wet.tab Jun 22, 2021 - Humidity and thermal triggered Shape Memory Effect - Rheology-based numerical modelling - Dynamic Mechanical Thermal Humidity Analysis Tabular Data - 14.9 KB - 11 Variables, 203 Observations - UNF:6:eh1ZdiSyYujnc8jXlFIhUQ==
Humidity and thermal triggered Shape Memory Effect - Rheology-based numerical modelling - Diffusion measurements Jun 22, 2021 - Publication: Humidity and thermal triggered Shape Memory Effect - Rheology-based numerical modelling Fauser, Dominik; Kuhn, Moritz; Steeb, Holger, 2021, "Humidity and thermal triggered Shape Memory Effect - Rheology-based numerical modelling - Diffusion measurements", https://doi.org/10.18419/DARUS-2024, DaRUS, V1, UNF:6:ig+5CuLNRHaoCQtncwbvWA== [fileUNF] This data contains diffusion measurements of Shape Memory Polymers (SMP) immersed in demineralized water. The SMP is a polyurethane-based Polymer, which is produced from SMP Technologies Inc. The SMP filament were processed with a 3D printer (Ultimaker 3, Ultimaker, Geldermarsen, Netherlands). The samples were dried in a drying oven for 10 days pri...
absorption_smp_30C.tab Jun 22, 2021 - Humidity and thermal triggered Shape Memory Effect - Rheology-based numerical modelling - Diffusion measurements Tabular Data - 1.9 KB - 5 Variables, 45 Observations - UNF:6:GdI0GAXdOE+GHTVkYz1r3w==
absorption_smp_60C.tab Jun 22, 2021 - Humidity and thermal triggered Shape Memory Effect - Rheology-based numerical modelling - Diffusion measurements Tabular Data - 607 B - 4 Variables, 19 Observations - UNF:6:B+ueKT6L7QY4CQCKG5vr6w==
desorption_smp_30C.tab Jun 22, 2021 - Humidity and thermal triggered Shape Memory Effect - Rheology-based numerical modelling - Diffusion measurements Tabular Data - 1.4 KB - 5 Variables, 35 Observations - UNF:6:xooN2iGcUxkVTZXN21Uz+Q==
CAMPOS Project P8: Conceptual Model Uncertainty(Universität Stuttgart) Jun 21, 2021Stochastic Simulation and Safety Research for Hydrosystems (LS3)
Replication Data for: Overcoming the model-data-fit problem in porous media: A quantitative method to compare invasion-percolation models to high-resolution data Jun 18, 2021 - Modeling Strategies for Gas migration in Subsurface Banerjee, Ishani, 2021, "Replication Data for: Overcoming the model-data-fit problem in porous media: A quantitative method to compare invasion-percolation models to high-resolution data", https://doi.org/10.18419/DARUS-1776, DaRUS, V1 This dataset contains modeling data used to obtain the results and figures in the manuscript: "Overcoming the model-data-fit problem in porous media: A quantitative method to compare invasion-percolation models to high-resolution data." In particular, the model realization data and post-processing codes for the manuscript can be found here.
CompMethod_readme.txt Jun 18, 2021 - Replication Data for: Overcoming the model-data-fit problem in porous media: A quantitative method to compare invasion-percolation models to high-resolution data Plain Text - 2.3 KB - MD5: 1b1729d1013e7f62d388169fd0c2eaae Documentation text file with details of the codes and data provided in this dataset
Gasmat.rar Jun 18, 2021 - Replication Data for: Overcoming the model-data-fit problem in porous media: A quantitative method to compare invasion-percolation models to high-resolution data RAR Archive - 532.3 MB - MD5: 2f480b77c9db7c0b3fce17eeddc4530c Data 3d matrices containing the gas path development in time along 3rd-dimension

master_curve_SMP_redry.tab

Jun 22, 2021 - Humidity and thermal triggered Shape Memory Effect - Rheology-based numerical modelling - Dynamic Mechanical Thermal Humidity Analysis

Tabular Data - 21.8 KB - 11 Variables, 280 Observations -

master_curve_SMP_wet.tab

Jun 22, 2021 - Humidity and thermal triggered Shape Memory Effect - Rheology-based numerical modelling - Dynamic Mechanical Thermal Humidity Analysis

Tabular Data - 14.9 KB - 11 Variables, 203 Observations -

Humidity and thermal triggered Shape Memory Effect - Rheology-based numerical modelling - Diffusion measurements

Jun 22, 2021 - Publication: Humidity and thermal triggered Shape Memory Effect - Rheology-based numerical modelling

Fauser, Dominik; Kuhn, Moritz; Steeb, Holger, 2021, "Humidity and thermal triggered Shape Memory Effect - Rheology-based numerical modelling - Diffusion measurements", https://doi.org/10.18419/DARUS-2024, DaRUS, V1, UNF:6:ig+5CuLNRHaoCQtncwbvWA== [fileUNF]

This data contains diffusion measurements of Shape Memory Polymers (SMP) immersed in demineralized water. The SMP is a polyurethane-based Polymer, which is produced from SMP Technologies Inc. The SMP filament were processed with a 3D printer (Ultimaker 3, Ultimaker, Geldermarsen, Netherlands). The samples were dried in a drying oven for 10 days pri...

absorption_smp_30C.tab

Jun 22, 2021 - Humidity and thermal triggered Shape Memory Effect - Rheology-based numerical modelling - Diffusion measurements

Tabular Data - 1.9 KB - 5 Variables, 45 Observations -

absorption_smp_60C.tab

Jun 22, 2021 - Humidity and thermal triggered Shape Memory Effect - Rheology-based numerical modelling - Diffusion measurements

Tabular Data - 607 B - 4 Variables, 19 Observations -

desorption_smp_30C.tab

Jun 22, 2021 - Humidity and thermal triggered Shape Memory Effect - Rheology-based numerical modelling - Diffusion measurements

Tabular Data - 1.4 KB - 5 Variables, 35 Observations -

CAMPOS Project P8: Conceptual Model Uncertainty(Universität Stuttgart)

Jun 21, 2021Stochastic Simulation and Safety Research for Hydrosystems (LS3)

Replication Data for: Overcoming the model-data-fit problem in porous media: A quantitative method to compare invasion-percolation models to high-resolution data

Jun 18, 2021 - Modeling Strategies for Gas migration in Subsurface

Banerjee, Ishani, 2021, "Replication Data for: Overcoming the model-data-fit problem in porous media: A quantitative method to compare invasion-percolation models to high-resolution data", https://doi.org/10.18419/DARUS-1776, DaRUS, V1

This dataset contains modeling data used to obtain the results and figures in the manuscript: "Overcoming the model-data-fit problem in porous media: A quantitative method to compare invasion-percolation models to high-resolution data." In particular, the model realization data and post-processing codes for the manuscript can be found here.

CompMethod_readme.txt

Jun 18, 2021 - Replication Data for: Overcoming the model-data-fit problem in porous media: A quantitative method to compare invasion-percolation models to high-resolution data

Plain Text - 2.3 KB -

Documentation

text file with details of the codes and data provided in this dataset

Gasmat.rar

Jun 18, 2021 - Replication Data for: Overcoming the model-data-fit problem in porous media: A quantitative method to compare invasion-percolation models to high-resolution data

RAR Archive - 532.3 MB -

Data

3d matrices containing the gas path development in time along 3rd-dimension

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