1 to 4 of 4 Results
Oct 21, 2022 - C-X2
Hommel, Johannes; Gehring, Luca, 2022, "Enzymatically induced carbonate precipitation and its effect on capillary pressure-saturation relations of porous media - column samples", https://doi.org/10.18419/darus-1713, DaRUS, V1, UNF:6:vBpJde3Meqz5+FeRU6TReA== [fileUNF]
This data set includes processed image data from three experiments on enzymatically induced carbonate precipitation ( further named low-, medium-, and high-mineralization or column 10, column 3, and column 4, respectively). Related data sets with the CT data this data set is base... |
Jun 29, 2022 - Publication: Microfluidic experiments
Vahid Dastjerdi, Samaneh; Karadimitriou, Nikolaos; Steeb, Holger, 2022, "Data for: Experimental Evaluation of Connectivity in Two-phase Flow in Porous Media during Drainage", https://doi.org/10.18419/darus-2250, DaRUS, V1, UNF:6:LEbgZwpIspjSG+AWpLIGmw== [fileUNF]
With the use of optical microscopy, microfluidic experiments take place in quasi-2D artificial porous media for a variety of cyclic displacement processes and boundary conditions, three of which are shared here. This dataset contains the data presented in the related publication... |
Mar 30, 2022 - Publication: Spatio-temporal distribution of precipitates and mineral phase transition during biomineralization affect porosity-permeability relationships
Weinhardt, Felix; Deng, Jingxuan; Steeb, Holger; Class, Holger, 2022, "Optical Microscopy and log data of Enzymatically Induced Calcite Precipitation (EICP) in microfluidic cells (Quasi-2D-structure)", https://doi.org/10.18419/darus-1799, DaRUS, V1
Content: This dataset includes raw as well as processed data from three experiments (Quasi-2D-1, Quasi-2D-2 and Quasi-2D-3). Each dataset consists of the readouts from the pressure sensor(s), as logged with the use of QmixElements ([Name of Experiment]_logFiles_QMIX), raw images... |
Jan 2, 2022 - ParkCast
Würth, Ines; Wigger, Maayen; Berlinger, Philipp, 2022, "Scanning wind lidar dataset from the alpha ventus wind farm", https://doi.org/10.18419/darus-2298, DaRUS, V1
In the nationally funded project ParkCast new methods for short-term power prediction for offshore wind farms will be developed, optimized and evaluated. The power forecasts focus on the time range up to 60 minutes with high temporal resolution. The aim is to significantly improv... |