Project Z02 (Porous Media Lab) provides experimental infrastructure and expertise for studying flow, transport, and deformation in porous media. It supports CRC 1313 with microfluidics and in-situ XRCT, enabling high-resolution, real-time imaging. Key advances include custom micromodels, novel imaging setups, and 4D data tools. Research addressed fracture mechanics, salt precipitation, and carbonate dissolution. Upcoming goals focus on dynamic fatigue tests, spectral XRCT, and tracer-based microfluidic analysis. The lab drives benchmarking and validation efforts across the CRC. Strong collaborations amplify its scientific and technical impact.
Featured Dataverses

In order to use this feature you must have at least one published or linked dataverse.

Publish Dataverse

Are you sure you want to publish your dataverse? Once you do so it must remain published.

Publish Dataverse

This dataverse cannot be published because the dataverse it is in has not been published.

Delete Dataverse

Are you sure you want to delete your dataverse? You cannot undelete this dataverse.

Advanced Search

141 to 150 of 241 Results
Gzip Archive - 20.6 GB - MD5: c9efd054e36cd9845021af23dbdde86c
Reconstructed micro-XRCT data set in 16 bit *.tif file format of overview scan "6o" ("6o_projections.tar.gz"). 2940x2940x2141 voxels with a uniform voxel size of 7.5 µm. (Internal ID: 20220818_02/reconstructed)
Tabular Data - 92.0 MB - 4 Variables, 2710609 Observations - UNF:6:85GK4HNLvPYSdYWASl8Dpw==
Data of the universal testing machine used to apply the axial load sigma_a.
Tabular Data - 93.7 MB - 4 Variables, 2710606 Observations - UNF:6:gRZCCO845YrgEc9udZcVlg==
Data of the syringe pump used to control the pore fluid pressure p.
Tabular Data - 81.2 MB - 3 Variables, 2710602 Observations - UNF:6:rdCQ2hRLGvFlsnGmMO4duw==
Data of the syringe pump used to control the confining fluid pressure sigma_r.
7Z Archive - 417.9 MB - MD5: ab58b9cb6a76de1e09c5baa790eada71
Data for training and examples for pre/post-processing
Plain Text - 155 B - MD5: 770701e71b665603a9e61292c7824135
An example of training log file (all the adopted parameters used during training are saved in this log)
Python Source Code - 5.7 KB - MD5: ee7d79e5e2f70aabaae7bae129627301
Post-processing code to create enhanced images
Python Source Code - 2.4 KB - MD5: 9b63a89131ad52136b92531a6b7f5e71
Pre-processing code to change the images into available shape and format
Python Source Code - 7.0 KB - MD5: 10e1540fe5afda49692b2437eb245903
Training code in order to train the model
Python Source Code - 17.5 KB - MD5: 3a13bef31e567313fc8ef766bc00d377
Customized image library in order to save and load the image data
Add Data

Log in to create a dataverse or add a dataset.

Share Dataverse

Share this dataverse on your favorite social media networks.

Link Dataverse
Reset Modifications

Are you sure you want to reset the selected metadata fields? If you do this, any customizations (hidden, required, optional) you have done will no longer appear.