OPUS Online Publikationen der Universität Stuttgart
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Quantifying salt crystallization impact on evaporation dynamics from porous surfaces
No full textWe investigated the effects of salt crystallization on the dynamics of saline water evaporation in porous media. Water mass loss rates from sand columns supplied with NaCl solutions at three concentrations were monitored under controlled ambient conditions. The formation and evolution of salt crystals over sand surfaces were synchronously imaged optically and thermally. Despite identical experimental and ambient conditions, we observed distinct crystallization dynamics that affected evaporative mass loss rates for similar salt concentrations, highlighting high variability of crystallization and its impact on evaporation. We observed the enhancement of maximum evaporation rates by factors of 3 to 14 under our experimental conditions and attributed this enhancement to the formation and evolution of porous crystalized salts at the surface. Additionally, visible intermittent temperature fluctuations of the salt crust were quantified using thermal imagery attributed to the dynamic processes of crystallization, dissolution and evaporation occurring simultaneously at the surface.Deutsche Forschungsgemeinschaf
Digital twins for 3D confocal microscopy : near-field, far-field, and comparison with experiments
No full textTo push the boundaries of confocal microscopy beyond its current limitations by predicting sensor responses for complex surface geometries, we build digital twins using three rigorous models, the finite element method (FEM), Fourier modal method (FMM), and boundary element method (BEM) to model light-surface interactions. Fourier optics are then used to calculate the sensor signals at the back focal plane and at the detector. A 3D illumination model is applied to 2D periodic structures for FEM and FMM modelings and to 3D aperiodic structures for BEM modeling. The lateral and vertical scanning processes of the confocal microscope are achieved through focal-point shifts of the objective, using plane-wave illuminations with varying incident and azimuthal angles. This approach reduces the need for repeated, time-intensive rigorous simulations of the scattering process when a fine scanning is desired. Furthermore, we give an in-depth description of a novel confocal microscopy method using FMM. For rectangular grating surfaces, the three models yield identical, highly accurate results, as validated by measured results. Simulations of the instrument transfer function, tilted gratings, and gratings with edge rounding offer insights into some experimentally observed effects. This research therefore provides a promising approach for correcting systematic errors in confocal microscopy.European Metrology Program for Innovation and Research (EMPIR) projectDanish Agency for InstitutionsDanish Innovation FundEMPIR programEuropean Union’s Horizon 2020 research and innovation programGerman Research Foundatio
Enhanced vectorial measurement uncertainty model
No full textQuantitative determination of the uncertainty of a measurement result is the key to assessing the quality and reliability of a measurement process and its result. The comparability of measurement results is ensured by the method for evaluating and expressing uncertainty defined by the Joint Committee for Guides in Metrology, where the model of the measurement process-which expresses the causal relationship of the measurand and the input quantities-is fundamental for the uncertainty evaluation. Setting up this model is very specific to the particular measurement setup and process, as well as the required level of detail. In this contribution, a vectorial method is presented which has been developed to assist users in modelling complex relationships, based on basic physical effects and their combination. Using a hierarchical approach, the method aims to be flexible, extensible and adaptable to a wide range of applications.German Research Foundation (Deutsche Forschungsgemeinschaft, DFG
Kommentierte Formelsammlung der deskriptiven und induktiven Statistik für Wirtschaftswissenschaftler
No full textDie kommentierte Formelsammlung ist als Begleitmaterial einer Einführungsveranstaltung in die Statistik für Wirtschaftswissenschaftler konzipiert. Sie soll nicht nur durch begleitende Aufgaben zum Selbststudium motivieren, sondern vor allem über die Kommentare zu den Maßzahlen und Verfahren bei einer sachbezogenen Interpretation statistischer Ergebnisse helfen
Accelerated non‐linear stability analysis based on predictions from data‐based surrogate models
No full textIn many applications in computer‐aided engineering, like parametric studies, structural optimization, or virtual material design, a large number of almost similar models must be simulated. Although the individual scenarios may differ only marginally in both space and time, the same amount of effort is invested in each new simulation, without taking into account the experience and knowledge gained in previous simulations. Therefore, we have developed a method that combines data‐based Model Order Reduction (MOR) and reanalysis, exploiting knowledge from previous simulation runs to accelerate computations in multi‐query contexts. While MOR allows reducing model fidelity in space and time without significantly deteriorating accuracy, reanalysis uses results from previous computations as a predictor or preconditioner. In particular, this method enables acceleration of the exact computation of critical points, such as limit and bifurcation points, by the method of extended systems for systems that depend on a set of design parameters, such as material or geometric properties. Such critical points are of utmost engineering significance due to the special characteristics of the structural behavior in their vicinity. Conventional reanalysis methods, like the fold line analysis, can be used to accelerate the computation of critical points of almost similar systems but are limited in their applicability. For the fold line analysis, only small parameter variations are possible as the algorithm may not converge to the correct solution or fail to converge elsewise. Moreover, this method is only suited to finding the first critical points of limit point problems. In contrast to that, our developed data‐based “reduced model reanalysis” method overcomes these problems. Thus, a larger parameter space can be covered. The efficiency of this method is demonstrated for a couple of numerical examples, including standard and isogeometric finite element models.Deutsche ForschungsgemeinschaftStuttgart Center for Simulation Science (SimTech
Uncertainty-aware PCA for nonnormally distributed data
No full textDimensionality reduction techniques are essential in modern data analysis to enable interpretable representations of high-dimensional data. Principal component analysis (PCA), a widespread approach, identifies directions of maximal variance and provides linear projections, but does not account for uncertainty in the data. The recently proposed uncertainty-aware PCA (UAPCA) extends PCA by modeling each data point not as a fixed vector but as a probability distribution, focusing on multivariate normal distributed data. However, many real-world datasets exhibit non-normal characteristics, rendering the Gaussian assumption insufficient. This thesis introduces a generalization termed non-normal uncertainty-aware PCA (NNUAPCA), that projects arbitrary probability density functions, such as Gaussian mixture models or histograms, into lower-dimensional spaces while preserving the uncertainty structure introduced by UAPCA. By analytically propagating non-Gaussian uncertainty through the DR pipeline, NNUAPCA overcomes the limitations of UAPCA and makes our approach more applicable and accurate. The method is evaluated on synthetic and real-world datasets using qualitative visualizations and quantitative measures. Empirical results demonstrate that NNUAPCA produces low-dimensional embeddings that more faithfully preserve the uncertain structure of non-normally distributed data, while maintaining lower computational cost compared to other approaches.Techniken zur Dimensionsreduktion sind in der modernen Datenanalyse essenziell, um interpretierbare Darstellungen hochdimensionaler Daten zu ermöglichen. Principal Component Analysis (PCA), eine weit gestrueute Methode, identifiziert Richtungen maximaler Varianz und liefert lineare Projektionen, berücksichtigt jedoch keine Unsicherheit in den Daten. Die kürzlich vorgeschlagene Uncertainty-aware PCA (UAPCA) erweitert PCA, indem sie jeden Datenpunkt nicht als festen Vektor, sondern als Wahrscheinlichkeitsverteilung modelliert und sich dabei auf multivariat normalverteilte Daten konzentriert. Viele reale Datensätze weisen jedoch nicht-normalverteilte Eigenschaften auf, wodurch die Gaußsche Annahme unzureichend wird. Diese Arbeit stellt eine Verallgemeinerung namens Non-normal Uncertainty-aware PCA (NNUAPCA) vor, die beliebige Wahrscheinlichkeitsdichten wie Gaussian Mixture Models oder Histogramme in niedrigdimensionale Räume projiziert und dabei die von UAPCA eingeführte Unsicherheitsstruktur beibehält. Durch die analytische Propagation nicht-gaußscher Unsicherheit entlang der DR-Pipeline überwindet NNUAPCA die Einschränkungen von UAPCA und macht unseren Ansatz anwendbarer und genauer. Die Methode wird anhand synthetischer und realer Datensätze mittels qualitativer Visualisierungen und quantitativer Metriken evaluiert. Die empirischen Ergebnisse zeigen, dass NNUAPCA niedrigdimensionale Einbettungen erzeugt, die die unsichere Struktur nicht-normalverteilter Daten originalgetreuer bewahren und gleichzeitig geringere Rechenkosten im Vergleich zu anderen Ansätzen aufweisen
Crystal structure of (3aS,4R,5S,6R,6aS)-4,5,6-trihydroxy-5,6-O-isopropylidene-3,3a,4,5,6,6a-hexahydro-1H-cyclopent[c]isoxazole, C9H15NO4
No full textC9H15NO4, orthorhombic, P21212 (No. 18), a = 8.8078(6) Å, b = 21.209(1) Å, c = 5.3420(4) Å, V = 997.9 Å3, Z = 4, Rgt(F) = 0.045, wRref(F2) = 0.123, T = 293 K.Fonds der Chemischen Industri
Spatially resolved current- and photodetection based on amorphous indium gallium zinc oxide
No full textIn the scope of this thesis amorphous indium gallium zinc oxide based thin-film devices and circuits are utilized to create current- and photodetection arrays. These arrays enable the spatial resolution of the charge carrier creation, the electric field distribution and the fluorescence light inside a nitric oxide sensing cell. The experimental setup including the gas cell is part of the quantum nitric oxide sensing experiment, whose goal is to realize a trace gas sensor for the detection of nitric oxide. The sensor principle is based on the thermal ionization of resonant excited electronic states. Thin-film technology can provide an integrated readout of the ionization current. Therefore the development, simulation and characterization of the required current-to-voltage conversion circuits (resistive transimpedance amplifiers) takes up a considerable part of this thesis. Especially the creation of an on-glass unity gain stable operational amplifier design is treated extensively. This includes a theoretical overview on different current-to-voltage converters, operational amplifiers, thin-film devices and technology. A variation of thin-film based photosensing devices is manufactured and characterized. The characterization includes the investigation of the detection efficiency, the sensitivity and the bandwidth. Furthermore the influence of sensor dimensions and operating parameters on the sensor characteristics is determined. Additional the compatibility of photosensors and transistors or readout circuits are elaborated. The functionality of the photosensor arrays is validated by measuring the beam size of an ultraviolet laser at 227 nm. In general the created thin-film devices and circuit have applications in the display field, especially in the subfield of sensor integration into displays. Transimpedance amplifiers can be essential parts in the readout circuits of these sensors. The utilized operational amplifier also has an application in column and row driver circuits of displays
Proton exchange membrane-like alkaline water electrolysis using flow-engineered three-dimensional electrodes
No full textFor high rate water electrolysers, minimising Ohmic losses through efficient gas bubble evacuation away from the active electrode is as important as minimising activation losses by improving the electrode’s electrocatalytic properties. In this work, by a combined experimental and computational fluid dynamics (CFD) approach, we identify the topological parameters of flow-engineered 3-D electrodes that direct their performance towards enhanced bubble evacuation. In particular, we show that integrating Ni-based foam electrodes into a laterally-graded bi-layer zero-gap cell configuration allows for alkaline water electrolysis to become Proton Exchange Membrane (PEM)-like, even when keeping a state-of-the-art Zirfon diaphragm. Detailed CFD simulations, explicitly taking into account the entire 3-D electrode and cell topology, show that under a forced uniform upstream electrolyte flow, such a graded structure induces a high lateral velocity component in the direction normal to and away from the diaphragm. This work is therefore an invitation to start considering PEM-like cell designs for alkaline water electrolysis as well, in particular the use of square or rectangular electrodes in flow-through type electrochemical cells.European Commissio
Software-defined value stream process systems
No full textThe increasing volatility of the markets and the growing demand for customized products are challenges for future production to ensure maximum flexibility and adaptability. This paper introduces software-defined value stream process systems (SVPSs), a novel approach that extends the concept of software-defined manufacturing into autonomous, reconfigurable production systems. SVPSs establish a cyber-physical chain that links product features to requirements, enabling their fulfillment through modular machine and process hardware. A modular construction kit of individually combinable hardware and associated software modules is presented. These modules are coordinated via a digital process chain that enables holistic simulations, optimizations, and planning based on a Digital Twin. This system is based on software-defined manufacturing but extends it into autonomous reconfigurable machines. By enabling virtual planning and commissioning of entire production lines, the SVPS concept provides an efficient and adaptable solution to meet the demands of volatile markets.The Ministry of Science, Research and Arts of the Federal State of Baden-WürttembergInnovationsCampus Mobilität der Zukunft (ICM