DaRUS (University of Stuttgart)
Not a member yet
2037 research outputs found
Sort by
Code and benchmarks for geometry-informed drag term computation for pseudo-3D Stokes simulations with varying apertures
No full textContent:
This data set includes snapshots of the code used to compute the benchmarks and applications in Krach et al.(2024). All software tools provided enable the user to perform pseudo-3D Stokes simulations with a geometry-informed drag term using DuMux and to determine permeability, volumetric flux as well as local pressure and velocity fields for both, the domains in the publication as well as user defined geometries.
Pseudo3D_Stokes: DuMuxsubmodule
Pseudo-3D-Stokes Module for Varying Apertures is a DuMux module developed at research institutions. DuMux is a simulation framework focusing on Finite Volume discretization methods, model coupling for multi-physics applications, and flow and transport applications in porous media.
This module aims to assist researchers in planning, improving, or interpreting microfluidic experiments through numerical simulations based on the Stokes equations in an easy and intuitive way. It uses .pgm files as input to create numerical grids. These .pgm files should include 8 bit grayscale values referring to the relative height of a microfluidic cell, which can be created from microscopy images of a microfluidic experiment using suitable image processing procedures.
Based on the .pgm files, the python module localdrag (see below) should be used to create the suitable drag prefactor fields lambda1 and lambda2. For further details, refer to our publication (see below). Please check out the README for information on requirements, building DuMux including its submodule and examples.
localdrag: python preprocessing module
localdrag is a python module to create geometry informed pre-factor maps for pseudo-3D Stokes simulations with DuMux based on local pore morphology. localdrag is used as a preprocessing tool for the DuMux module pseud3D_stokes and is delivered directly when the pseudo3D_stokes is cloned with the recurse-submodules option (recommended, see README).
Related datasets and repositories:
POREMAPS, 3D Stokes solver used to create reference solutions: git repository, DaRUS dataset
DuMux:
Website, git repository
Pseudo3D_Stokes:
git repository
Image dataset of micromodel with precipitation: DaRUS dataset, research paper
localdrag python module for preprocessing and data handling: git repository
Input data and results for all domains in Krach et al. (2024): DaRUS dataset
</ul
CSI Dataset espargos-0002: Larger combined antenna array, indoor lab room with metal wall, LoS and NLoS areas
No full textDataset containing WiFi channel state information (CSI) alongside ground truth data (position tags, timestamps) measured with ESPARGOS antenna arrays. Measurement parameters and machine-readable file format descriptions are provided in a JSON file (spec.json).
Four ESPARGOS arrays are combined into one large array with 8 by 4 antennas. A metal wall blocks the LoS path in parts of the measurement area. The wall is later removed
dolfinx_eqlb v1.1.0
No full textdolfinx_eqlb is an open source library, extending FEniCSx by local flux equilibration strategies. The resulting H(div) conforming fluxes can be used for the construction of adaptive finite element solvers for the Poisson problem [1], elasticity [2][3][4] or poro-elasticity [5][6].
The flux equilibration relies on so called patches, groups of all cells, connected with one node of the mesh. On each patch a constrained minimisation problem is solved [7]. In order to improve computational efficiency, a so called semi-explicit strategy [8][9] is also implemented. The solution procedure is thereby split into two steps: An explicit determination of an H(div) function, fulfilling the minimisation constraints, followed by an unconstrained minimisation on a reduced, patch-wise ansatz space. If equilibration is applied to elasticity - stress tensors have distinct symmetry properties - an additional constrained minimisation step, after the row wise reconstruction of the tensor [3][4] is implemented.
[1] Braess, D. and Schöberl, J.: Equilibrated Residual Error Estimator for Edge Elements (2008).
[2] Prager, W. and Synge, J. L.: Approximations in elasticity based on the concept of function space (1947).
[3] Bertrand et al.: Weakly symmetric stress equilibration and a posteriori error estimation for linear elasticity (2021).
[4] Bertrand et al.: Weakly symmetric stress equilibration for hyperelastic material models (2020).
[5] Riedlbeck et al.: Stress and flux reconstruction in Biot’s poro-elasticity problem with application to a posteriori error analysis (2017).
[6] Bertrand, F. and Starke, G.: A posteriori error estimates by weakly symmetric stress reconstruction for the Biot problem (2021).
[7] Ern, A. and Vohralik, M.: Polynomial-Degree-Robust A Posteriori Estimates in a Unified Setting for Conforming, Nonconforming, Discontinuous Galerkin, and Mixed Discretizations (2015).
[8] Cai, Z. and Zhang, S.: Robust equilibrated residual error estimator for diffusion problems: conforming elements (2012).<br
DP-FR-1924-MODLTW Polar Data Re15E5
No full textPolar measurements of the DP-FR-1924-MODLWT airfoil. Data recorded at the Laminar Wind Tunnel, University of Stuttgart. For details on the measurements, visit https://www.iag.uni-stuttgart.de/en/institute/test-facilities/laminar-wind-tunnel/</a
Assessing nucleotide sugar donors inside the Golgi apparatus as a prerequisite for unravelling culture impacts on glycoforms of antibodies
No full textThe dataset presented here supplements the publication titled “Assessing Nucleotide Sugar Donors Inside the Golgi Apparatus as a Prerequisite for Unraveling Culture Impacts on Glycoforms of Antibodies.” The study emphasizes the importance of glycosylation in biopharmaceuticals and unravels a novel, integrative workflow for absolute quantification of nucleotide sugar donors (NSDs) within subcellular compartments of CHO DP-12 cells. Through a combination of state-of-the-art methodologies such as subcellular fractionation, exhaustive sample extraction, and metabolomics measurements, the researchers quantified NSD concentrations, establishing a clear correlation between these metabolites and the glycan profiles observed on antibodies, especially under conditions of nutrient pulse stimulation.
The dataset enables a comprehensive and compartment-specific empirical analysis of NSD dynamics, which are essential for understanding and optimizing N-glycosylation regulation within the Golgi apparatus. This approach provides new insights into how glycosylation can be controlled and enhanced in production cell lines, with the ultimate aim of improving the efficiency and consistency of glycosylation in biotherapeutic production.
The data consists of absolute and relative quantifications, including mean values and standard deviations/errors from wet lab measurements. These encompass concentrations of fractionated proteins and metabolites, along with cultivation data such as cell counts, substrate/product/byproduct concentrations, and cell viability metrics over time. Additionally, the dataset provides quantitative data on antibody production and the relative abundance ratios of glycoforms under various nutrient conditions. Graphic representations of western blot analyses and other experimental results are included to support the quantitative findings. This dataset serves as a valuable resource for researchers aiming to optimize glycosylation processes in biopharmaceutical production systems, especially by embodying a new generation of metabolic data with subcellular precision for modelling purposes.</p
Dynamics of Head Pointing Using Static and Dynamic Gains
No full textData was acquired from experiments of the master's thesis "Exploring the dynamics of the head pointing." There were two studies: preliminary and the main study. The task of both was a two-dimensional pointing, as described in ISO 9241-41. Pointing was performed by tracking the head rotation using Tobii 4C Eyetracker, capable of detecting head rotation and position. Goal of the preliminary study is to compare different gains and find two most highly-ranked gains for the main study. Goal of the main study is to explore the effects of different gain methods on over and udershooting. For more details, check README.m
exaFOAM Microbenchmark MB16 - Simplified HMC aeroacoustics vehicle
No full textThis case has been developed as is part of the exaFOAM project that aims to enable the open-source CFD software OpenFOAM to exploit massively parallel HPC architectures and overcome performance scaling bottlenecks. The geometry files represent a simplified vehicle at nominally 1/3rd scale, with non-complex geometry: stub wheels, smooth underbody, generically inclined vehicle front, and with flat roof, sides and rear. The only geometric complexity comprises the side-view mirror and rounded A-Pillar, which are taken from production vehicles in order to measure the realistic aerodynamic and acoustic perturbations in the wake of these components (nominally the vehicle side-glass). Detailed information and case setup can be found in the README PDF file or the README.md file contained in the case setup file
Replication Data for: "Olefin Metathesis in Confined Geometries: A Biomimetic Approach toward Selective Macrocyclization"
No full textThis paper addresses the synthesis of macrocyclic molecules by a spatial confinement effect in SBA-15. Various aspects such as concentration dependence, substrate size and temperature dependence have been discussed.
Article PDF, submitted and accepted version, Figures and ChemDraw files with structures as published. OPEN ACCESS Article- Request access to files if needed
Dataset: Two Heat Pumps Simulation - Raw, 1000 Data Points
No full textThis data set serves as training data for modelling the temperature field emanating from two groundwater heat pumps (one fixed, one randomly placed). It is simulated with Pflotran and saved in h5 format. It contains 1000 data points, each consisting of one simulation run until a near steady state is reached. Each datapoint measures 1250 m x 2560 m with 250 x 512 cells. The varying parameters of the data set are pressure and permeability. Both are constant within a data point, but vary across the data set. Other parameters that define the data set, such as porosity, are chosen to be as close as possible to reality.
Source: "Die hydraulischen Grundwasserverhältnisse des quartären und des oberflächennahen tertiären Grundwasserleiters im Großraum München", Geologica Bavarica Volume 122.
Generated with scripts from Dataset generation with Pflotran (commit 94daf52) with arguments given in inputs/args.yaml
Object Detection on Depth Map with YOLO
No full textA neural network, based on the ‘You Only Look Once’ (YOLO) network, has been trained to detect objects, using conventional RGB images. Taking advantage of the pixel relationship between the RGB image and the depth map, the positions of the detected objects will be projected onto a depth map. After some statistic analysis, the pixels pertaining to one object will be extracted. Finally, the 3D position of the object in the surroundings will be calculated