1 to 10 of 64 Results
Feb 16, 2024 - PN3-5
Sriram, Siddharth, 2024, "Data-driven analysis of structural instabilities in electroactive polymer bilayers based on a variational saddle-point principle: Datasets and ML codes", https://doi.org/10.18419/darus-3881, DaRUS, V1
The datasets and codes provided here are associated with our article entitled "Data-driven analysis of structural instabilities in electroactive polymer bilayers based on a variational saddle-point principle". The main idea of the work is to develop surrogate models using the con... |
Nov 20, 2023 - SPP2311: Ultrasound Neuromodulation
Werneck, Linda; Yildiz, Erdost; Han, Mertcan; Keip, Marc-Andre; Sitti, Metin; Ortiz, Michael, 2023, "Ion Flow Through Neural Ion Membrane: scripts and data", https://doi.org/10.18419/darus-3575, DaRUS, V1
The scripts and data are related to the numerical implementation of a quantitative model for ion flow through neural ion channels and a validation of the underlying single ion channel flow model for gramicidin A channels. The model is based on the Poisson-Nernst-Planck (PNP) equa... |
Nov 10, 2023Chair of Material Theory
Ultrasonic neuromodulation (UNM) is among the most significant new technologies being developed for human neuroscience because it can provide non-invasive control of neural activity in deep-brain regions with millimeter spatial precision and has elicited a surge of recent interes... |
Nov 10, 2023
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Sep 12, 2023 - Publication: Microfluidic experiments
Karadimitriou, Nikolaos; Steeb, Holger; Valavanides, Marios, 2022, "Pressure and volumetric flux measurements intended to scale relative permeability under steady state, co-flow conditions, in a PDMS micromodel", https://doi.org/10.18419/darus-2816, DaRUS, V2
The current repository contains raw data collected during a systematic laboratory study, examining the flow rate dependency of steady-state, co-injection of two-immiscible fluids within a microfluidic pore network model. The study is presented in the paper by Karadimitriou et al.... |
Sep 6, 2023 - Publication: Development of stochastically reconstructed 3D porous media micromodels using additive manufacturing: numerical and experimental validation
Lee, Dongwon; Ruf, Matthias; Yiotis, Andreas; Steeb, Holger, 2023, "Numerical investigation results of 3D porous structures using stochastic reconstruction algorithm", https://doi.org/10.18419/darus-3244, DaRUS, V1
This dataset contains the outcomes of conducted numerical simulations, rooted in designs generated using a stochastic algorithm devised by Quiblie (1984), Adler et al. (1990), and Hyman et al. (2014). Moreover, the investigation employed Lattice Boltzmann simulation, as used in p... |
Sep 6, 2023 - Publication: Development of stochastically reconstructed 3D porous media micromodels using additive manufacturing: numerical and experimental validation
Ruf, Matthias; Lee, Dongwon; Yiotis, Andreas; Steeb, Holger, 2023, "micro-XRCT datasets of stochastically reconstructed 3D porous media micromodels manufactured by additive manufacturing", https://doi.org/10.18419/darus-3243, DaRUS, V1
This dataset contains micro X-ray Computed Tomography (micro-XRCT) scan data sets (projection, reconstructed, and binarized images) of 3D porous media micromodels manufactured by additive manufacturing using the Material Jetting (MJ) method. The micromodel geometries were designe... |
Sep 4, 2023Publications
This dataverse includes the underlying measurment and simulation data of the publication: Lee, D., Ruf, M., Karadimitriou, N., Steeb, H., Manousidaki, M., Varouchakis, E.A., Tzortzakis, S., & Yiotis, A. (2023). Development of stochastically reconstructed 3D porous media micromode... |
Aug 4, 2023 - Publication: Microfluidic experiments
Karadimitriou, Nikolaos; Lee, Dongwon; Steeb, Holger, 2023, "Visual characterization of displacement processes in porous media", https://doi.org/10.18419/darus-3615, DaRUS, V1
This dataset correlates to the submitted article to IEEE VIS 2023, entitled “Visual Analysis of Displacement Processes in Porous Media using Spatio-Temporal Flow Graphs”, by Straub et al. 2023. More specifically, this data set is the one used to create the graphs shown in all Fig... |