Universität Stuttgart
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Item Open Access Low-field chip-based Overhauser dynamic nuclear polarization platforms(2026) Yang, Qing; Anders, Jens (Prof. Dr.)Item Open Access Methodology to qualify batteries for safety-critical vehicle applications(2025) Conradt, Rafael; Birke, Kai Peter (Prof. Dr.-Ing.)Item Open Access Modeling, testing and application of tuned liquid multi-column dampers for floating offshore wind turbines(2024) Yu, Wei; Cheng, Po Wen (Prof. Dr.)Item Open Access Coupling mechanisms of magnetic nanoparticles in polymeric environments(2026) Kreissl, Patrick; Holm, Christian (Prof. Dr.)Item Open Access Scalable traffic engineering heuristics for time-triggered communication in real-time networks(2026) Geppert, Heiko; Rothermel, Kurt (Prof. Dr. rer. nat. Dr. h. c.)Distributed safety-critical cyber-physical systems require real-time behavior. This means they must respond not just quickly, but in time, to new situations considering both, the task processing and network communication time. From a networking perspective, meticulous, time-driven traffic planning performed at the frame level is necessary to guarantee low end-to-end delay bounds and low latency. This involves carefully planning transmission operations along each time-critical frame's network path are carefully planned, including precise timing, to limit or even eliminate interference from cross-traffic and ensure timely delivery. Since modern real-time systems can consist of hundreds or thousands of devices - for example, large manufacturing plants or continental-sized power grids - the traffic planning must be highly scalable. Although there are many traffic planning approaches in the literature, there is a lack of very fast heuristics that can handle very large stream sets and networks quickly. This thesis investigates traffic planning heuristics and optimization techniques, focusing on different aspects of the traffic planning domain. The traffic planning consists of novel methods for conflict-graph-based scheduling and new heuristics for very large instances of traffic planning problem. The optimizations include multicast partitioning, which combines the benefits of multicast and unicast traffic plans, and load-balanced stream placement, which generates traffic plans that can accommodate additional streams joining the system later. We created prototype implementations and analyzed their performance in solving the traffic planning problem. Our traffic plans yielded a higher accumulated network throughput or admitted more streams while maintaining computation times ranging from sub-seconds to minutes, even for extremely large-scale problem instances. The traffic planning methods and optimization techniques presented in this thesis can be applied to modern real-time networking technologies, such as Time-Sensitive Networking and TTEthernet.Item Open Access Potentials and limitations of the a priori data-augmentation of turbulence closure models(2026) Mandler, Hannes; Weigand, Bernhard (Prof. Dr.-Ing. habil.)Turbulent flows occur in numerous technical applications. In some applications, turbulence is deliberately exploited to increase their efficiency. In others, the efficiency can be increased by suppressing turbulence to the greatest extent possible. The ability to accurately predict turbulent flows is, therefore, of immense importance. Nowadays, mainly numerical simulation methods are used for this purpose. As solving the Navier-Stokes equations would be far too costly for most applications of practical interest, the Reynolds-averaged Navier-Stokes equations are typically considered instead. However, their solution requires closure models to describe the influence of the turbulence on the mean flow. As a result of structural and parametric deficiencies of existing models, especially the popular eddy viscosity models, the accuracy of the predicted flow fields often no longer meets the current quality requirements. One way to address these deficiencies is to replace the empirical but often constant model coefficients by functions of the local mean flow field. Unlike the classical modeling approach, which seeks to derive such functional dependencies from theory and physical reasoning, leveraging machine learning instead allows for extracting the desired coefficient functions from publicly available DNS data. The models could, therefore, be calibrated for applications that are still simple but exceed the complexity of the traditional calibration cases, e.g., applications governed flow separation and reattachment. This thesis investigates the merits of this approach with respect to the accuracy of the flow field predictions and the possibility of developing more universal closure models. To this end, an a priori augmentation method for existing closure models was developed. A two-stage procedure was proposed to find appropriate functions for the closure coefficients. First, using the DNS data, the extended closure model is inverted to obtain the spatial distribution of the optimal coefficients for a particular training case. These allow the optimal structure of the constitutive equation to be determined in order to prevent any structural deficiencies. By subsequently solving a regression problem, functions represented by neural networks can be inferred that predict those optimal values of the coefficients as a function of the local mean flow state. Based on three examples, namely the flows through a plane channel, a plane channel with periodic hills, and a square duct, the data-augmented development of such model corrections was demonstrated. The errors in the prediction of the velocity field for the respective training cases could be reduced by up to 65%. The accuracy achieved with this method is typically unmatched even for significantly more complex existing closure models. In addition, it was proven that the extended models provide at least equivalent, but often more accurate predictions than the baseline model for a wide range of Reynolds numbers. The same applies to applications that differ geometrically, but not phenomenologically from the training case. However, if the test case was characterized by different flow phenomena than the training case, a sometimes considerable decrease in the predictive accuracy compared to the baseline model was observed. The obvious strategy of dealing with this loss of universality, i.e., deriving the coefficient functions from a more diverse training data set, proved to be ineffective. This is considered to be due to the complexity of the structure of the coefficient functions, which is limited for stability reasons and, hence, usually not sufficient to actually reflect the diversity of the training data. The method developed in this work for the data-driven augmentation of existing closure models is representative of a number of similar approaches that seek to improve flow field predictions via a more accurate description of the Reynolds stress tensor. In summary, such methods are suitable for developing highly specialized models that achieve the desired accuracy gains for a class of not too complex and phenomenologically similar flows. These two limitations could probably be remedied by CFD-integrated training and well-designed combinations of many such expert models.Item Open Access Entwicklung und Analyse einer selbstkühlenden und substratunabhängigen Beschichtung für technische Textilien unter Nutzung der energiefreien Strahlungskühlung(2024) Zimmermann, Lea; Gresser, Götz T. (Prof. Dr.-Ing.)Aufgrund des Klimawandels, des Bevölkerungswachstums und des städtischen Wärmeinseleffekts (UHI) ist der Bedarf an Kühlenergie insbesondere in städtischen Gebieten gestiegen und wird voraussichtlich auch in Zukunft weiter zunehmen. Bisherige konventionelle Kühlsysteme für Gebäude wie Klimaanlagen basieren auf thermodynamischen Kreisläufen, die einen großen Teil des Strombedarfs ausmachen und gleichzeitig Abwärme und Kohlendioxid (CO2) an die Umwelt abgeben. Technologien wie die Strahlungskühlung bieten eine nachhaltige und energiefreie Lösung, indem sie die Wellenlängenbereiche der Atmosphäre, die für elektromagnetische Strahlung transparent sind, das so genannte atmosphärische Fenster (8-13 µm), nutzen, um Wärmestrahlung in den kälteren (3 K) Weltraum abzugeben. Durch die Entwicklung von Beschichtungen, die selektiv Wärme durch die Atmosphäre abstrahlen und weniger Sonnenwärme absorbieren, ist eine Abkühlung unter die Umgebungstemperatur auch tagsüber möglich. Während sich bisherige Veröffentlichungen im Bereich der textilen Gebäudekühlung auf spezifische Faserstrukturen und textile Trägermaterialien sowie komplexe Mehrschichtaufbauten konzentrierten, was den Einsatz für hochskalierte Außenanwendungen einschränkt, zielt diese Arbeit auf die Entwicklung einer neuartigen, substratunabhängigen Beschichtung mit spektral selektiven Strahlungseigenschaften hin. Durch die detaillierte Abstimmung von Beschichtungsparametern wie der Partikelkonzentration, verteilung und -größe in Kombination mit niedrig emittierenden und solarreflektierenden Partikeln sowie einem stark im mittleren Infrarot emittierenden Matrixmaterial, wird eine substratunabhängige Kühlung unter die Umgebungstemperatur erreicht, gezeigt am Beispiel von drei für den Membran- und Zeltbau typischen Gewebetypen. Darüber hinaus ist die Beschichtung so konzipiert, dass sie einfach auf verschiedene textile Materialien appliziert werden kann und gleichzeitig eine geringe Dicke aufweist, um hohe Flexibilität und Skalierbarkeit zu gewährleisten. Um die Funktionsweise des entwickelten Beschichtungssystems weiter zu validieren, wurden Tests im Freien mit einem konzipierten Messaufbau durchgeführt, um Temperaturunterschiede und Kühlleistungen unter realen Wetterbedingungen zu messen. Die Ergebnisse zeigen, dass die Temperatur der Beschichtung (zwischen 7-19 Uhr) an einem heißen Sommertag um durchschnittlich 2 °C unter der Umgebungstemperatur liegt. Darüber hinaus wird ein thermisches Modell an textile Materialien angepasst und validiert, um die Kühlleistung für verschiedene Wetterszenarien zu simulieren und zu berechnen. Damit leistet diese Arbeit einen Beitrag zur Weiterentwicklung nachhaltiger textilbasierter Kühltechnologien und bietet eine vielversprechende Lösung für den wachsenden Bedarf an energieeffizienter Kühlung in städtischen Umgebungen.Item Open Access Zwei Dichter und ihre Katastrophensucht - Lukas Bärfuss und Heinrich von Kleist(2025) Reiß, Birgitt; Bühler-Dietrich, Annette (Prof. Dr.)Ausgehend von der Kleistrede, die Lukas Bärfuss 2011 in Thun gehalten hat, wird literaturwissenschaftlich untersucht, welche Bezugspunkte es in seinem Werk zu demjenigen von Heinrich von Kleist gibt.Item Open Access Über die Lösung der Navier-Stokes-Gleichungen mit Hilfe der Moore-Penrose-Inversen des Laplace-Operators im Vektorraum der Polynomkoeffizienten(2024) Große-Wöhrmann, Bärbel; Resch, Michael (Prof. Dr.-Ing.)Die bekannten numerischen Standard-Verfahren zur Lösung partieller Differentialgleichungen basieren auf einer räumlichen Diskretisierung des Berechnungsgebiets. Ihre Performance und Skalierbarkeit auf modernen massiv-parallelen Höchstleistungsrechnern ist von der Verfügbarkeit effizienter numerischer Verfahren zur Lösung linearer Gleichungssysteme abhängig. Angesichts grundlegender Herausforderungen erscheint die Entwicklung neuer Lösungsansätze sinnvoll. Ich stelle in dieser Arbeit einen Polynomansatz zur Lösung partieller Differentialgleichungen vor, der nicht auf einer räumlichen Diskretisierung beruht und mit Hilfe der Moore-Penrose-Inversen des Laplace-Operators die Entkopplung der Navier-Stokes-Gleichungen ermöglicht. Dabei ist der Grad der Polynome nicht grundsätzlich beschränkt, so dass eine hohe räumliche Auflösung erreicht werden kann.Item Open Access Digital laboratory: X-ray computed tomography towards direct numerical simulations(Stuttgart : Institute of Applied Mechanics, 2025) Uribe, David; Steeb, Holger (Prof. Dr.-Ing.)A digital porous media laboratory comprises a set of techniques to analyse and characterize samples using computer simulations and digital measurements to predict or determine material properties. This thesis has implemented the complete workflow of this technique from sample preparation until reporting the gained information pertaining said sample. A complete design of an in house built X-ray computed tomographic system is described. All components of the system are listed and the way they are electrically and mechanically coupled is explained. Software architecture to control the system and the advantages it has to maintain the system is described at a cost of system complexity. The in-house tomographic system was used to characterize engineering, geologic and biological samples.