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Aluminum powder as recyclable energy carrier : population balance modelling of oxide smoke formation
Full text linkAgainst the background of our society’s endeavour towards a sustainable energy economy,
in the past decade, a variety of different technologies has emerged to tackle the challenge of
global warming. While particular concepts have already been developed to maturity, metal
powders have recently been hypothesized as recyclable and carbon-free energy carriers as
part of an on-demand oxidation-reduction cycle, on the consumer side of which the metal
powder is burned while releasing heat and condensed oxides as main reaction products.
This thesis focuses on aluminum which not only qualifies as a potential metal fuel due to
its high energy density, availability and handling safety, but has also been investigated in
detail for its reaction kinetics. At high temperatures, aluminum particles can be burned
exothermally in oxidizing atmospheres in a similar way to carbon-based particulate fuels.
The produced aluminum oxide is solid under ambient conditions and may form through two
distinct chemical pathways. On the one hand, vaporized aluminum initiates homogeneous
gas phase combustion, leading to the condensation of aluminum oxide into very fine smoke
droplets. On the other hand, heterogeneous reactions at the particle surface cause a direct
conversion of aluminum into aluminum oxide, rendering the fuel particle biphasic. At
present, the formation of oxide smoke fines poses major challenges to the oxide recovery
from dust flames and the closure of the metal fuel cycle. The smoke’s size distribution
influences natural deposition and emission mechanisms and, consequently, plays a decisive
role in the design of gas-particle separation devices. With the objective of elucidating the
oxide smoke dynamics and identifying operating conditions promoting the formation of
larger smoke droplets, we propose a comprehensive modelling approach that permits a
prediction of the smoke size distribution alongside gas and particle surface compositions.
Physically, on the spatially localized level, the oxide droplet size distribution is influenced
by the ambient gas phase composition and shaped by the mutual competition of nucleation,
condensational surface growth, evaporation/dissociation and coagulation. The heat release
and dispersion temperature, on the other hand, are affected by chemical reactions, phase
transition and radiation. In this thesis, the oxide smoke droplets are described in a Eulerian
fashion by harnessing a population balance description that is informed by a complete set
of droplet formation and interaction kinetics and allows for analyzing the interaction,
competition and mutual reinforcement of the relevant physical processes. In a first step,
the population balance framework is applied in a perfectly stirred reactor and a partially
stirred reactor. These simplified model formulations are representative of the dynamics
in a single grid cell of a spatially inhomogeneous laminar reactive flow solver or a one-
point, one-time probability density function description. As key novelty in this context,
the partially stirred reactor model is extended to account for the presence of a reactive
surface with small-scale variability in terms of surface composition. Detailed gas phase
and heterogeneous surface kinetics, including NOx formation, are taken into account.
In a next step, we present a fully Eulerian framework for modelling the combustion of a
single spatially resolved aluminum particle. In order to describe the reacting gas-droplet
dispersion, we combine the population balance equation governing the smoke size distri-
bution with tailored balance laws for gas phase species as well as the dispersion mass, mo-
mentum and enthalpy, while the detailed kinetic framework is augmented by the transport
parameters governing species differential diffusion, droplet diffusion and thermophoresis.
The major novelties of our physical model lie with the prediction of the smoke size distri-
bution at every location in the flow domain, the accommodation of size-sensitive kinetics
and transport processes as well as the prediction of possible NOx pollutants in a spatially
resolved fashion. Based on a comparison of our model predictions with available experi-
mental measurements, we calibrate the droplet formation kinetics and, finally, validate the
model while attesting a very good agreement. Ultimately, the spatio-temporally resolved
single particle model is instrumented to estimate the emissions of a burning aluminum
particle over the course of its conversion. Here, a particular feature is the incorporation
of a time-varying particle morphology, including an oxide lobe. Our predictions of the
particle burning times and residue sizes for different initial particle diameters are found to
agree well with available experimental data. In order to demonstrate the controllability
of the combustion products’ sizes, an analysis of the effect of a varying pressure on our
predictions is performed for all three configurations.
Lastly, we present the fundamentals of a modelling framework for turbulent metal dust
flames encountered in practically relevant metal dust burners. Within the scope of a
partially stirred reactor, the model is shown to allow for the individual assessment of
polydispersity and turbulence influencing the particle-laden flow.Literaturverzeichnis: Seite 179-19
Krebs in Subsahara-Afrika mit Fokus auf das kolorektale Karzinom und infektiöse Krebsursachen : Epidemiologie, Überleben und leitliniengerechte Therapie : eine Analyse multizentrischer populationsbasierter Krebsregisterdaten
Full text linkDiese kumulative Arbeit analysiert populationsbasierte Daten von 653 Patient:innen mit kolorektalem Karzinom aus zehn Ländern Subsahara-Afrikas, um Versorgungssituation und Überleben zu bewerten. Nur 3,1 % der Patient:innen mit nicht-metastasierter Erkrankung erhielten eine leitliniengerechte Therapie; das Sterberisiko war für unzureichend versorgte Patient:innen 3,49-fach erhöht. Besonders benachteiligt waren Patient:innen aus Ländern mit niedrigem Human Development Index. Weitere eingeschlossene Studien zeigen, dass Infektionen in SSA eine zentrale Rolle bei der Krebsentstehung spielen: 28,7 % aller Krebsfälle sind Infektionen, vor allem viralen Erregern, zuzuschreiben. Die Dissertation unterstreicht die Notwendigkeit von Prävention, Impfprogrammen, Aufklärung und verbessertem Zugang zu leitliniengerechter Behandlung, um die wachsende Krebsbelastung in Subsahara-Afrika wirksam zu adressieren
Local nutrient addition drives plant diversity losses but not biotic homogenization in global grasslands
Full text linkNutrient enrichment typically causes local plant diversity declines. A common but untested expectation is that nutrient enrichment also reduces variation in nutrient conditions among localities and selects for a smaller pool of species, causing greater diversity declines at larger than local scales and thus biotic homogenization. Here we apply a framework that links changes in species richness across scales to changes in the numbers of spatially restricted and widespread species for a standardized nutrient addition experiment across 72 grasslands on six continents. Overall, we find proportionally similar species loss at local and larger scales, suggesting similar declines of spatially restricted and widespread species, and no biotic homogenization after 4 years and up to 14 years of treatment. These patterns of diversity changes are generally consistent across species groups. Thus, nutrient enrichment poses threats to plant diversity, including for widespread species that are often critical for ecosystem functions
Apolipoprotein E4 facilitates transfection of human monocyte-derived dendritic cells by lipid nanoparticles
Full text linkThe use of mRNA as a therapeutic drug class is a safe and fast alternative to viral vector or plasmid DNA therapies. Nevertheless, free mRNA will be rapidly degraded after administration to the body and only reach the cytosol of desired cells with difficulty. Lipid nanoparticles (LNP) safely deliver mRNA to cells of interest and can be used in the treatment of different diseases. Dendritic cells are the primary antigen-presenting cells and important for mRNA vaccine delivery. Efforts to increase LNP transfection of these cells are necessary and can be achieved by different approaches. Here, we present apolipoprotein E4 addition to LNP administration as one mean of increasing LNP-mediated eGFP mRNA delivery to human monocyte-derived dendritic cells. We also show some steps in the preparation method for LNP optimization using MS2 RNA as a novel model nucleic acid
Die Studienganglandschaft in den Bereichen Gesundheitswissenschaften, Public Health und Gesundheitsförderung in Deutschland : Entwicklung der Bachelor- und Masterstudiengänge
Full text linkDie aktuellen Herausforderungen im Gesundheitssystem in Deutschland bedingen neue Aufgabenbereiche, Handlungsfelder und Tätigkeitsprofile sowie angepasste fachliche Kompetenzen der in der Systemgestaltung Tätigen. Studienangebote im Bereich von Gesundheitswissenschaften/Public Health leisten hierfür seit den 1990er-Jahren einen Beitrag. Seither hat sich die Studienganglandschaft kontinuierlich weiterentwickelt. Die vorliegende Bestandsaufnahme zeigt den aktuellen Entwicklungsstand der Studiengänge.DEAL SpringerNatur
Bildbasierte Situationsanalyse zur intuitiven Mensch-Roboter-Interaktion in dynamischen Umgebungen
No full textMobile, intelligente Roboter helfen, die Produktivität, Präzision und Effizienz in der Industrie zu
steigern, Arbeitsunfälle und Kosten zu reduzieren und tragen damit gleichzeitig zu einer umwelt-
freundlichen Ressourcenschonung bei. Zusätzlich birgt ihr Einsatz in medizinischen und sozialen
Bereichen erhebliche Potenziale. Sie können die Zusammenarbeit von Hilfsbedürftigen und Helfen-
den unterstützen und so zur Steigerung der Lebensqualität beitragen. Für die Realisierung dieser
Potenziale muss jedoch die intelligente Erfassung des semantischen Aktionsraums und der darin
befindlichen menschlichen Interaktionspartner verbessert werden, um eine kontextbezogene und
intuitive Mensch-Roboter-Interaktionen zu ermöglichen.
Die vorliegende Arbeit befasst sich mit der Entwicklung, Implementierung und Evaluierung bild-
basierter Deep Learning-Methoden, die die soziale Autonomie mobiler Roboter verbessern und
den Informationsgehalt zur Bestimmung adäquater Verhaltensstrategien erhöhen. Sie ist in mehrere
wissenschaftliche Beiträge unterteilt, die sich auf die räumlich-semantische Umgebungsanalyse und
die Analyse menschlicher Interaktionspartner konzentrieren.
Der erste wissenschaftliche Beitrag befasst sich mit der Orientierung mobiler Roboter in komplexen,
dynamischen Umgebungen. Hierfür wird visueller SLAM (Simultaneous Localization and Mapping)
mittels eines Deep Learning-basierten Szenen-Flows erweitert, wodurch eine pixelgenaue Erfassung
dynamischer Bildelemente erzielt und eine signifikante Reduzierung des Trajektoriefehlers erreicht
werden kann. Als Nächstes wird eine neue Methode zur semantischen Kartierung vorgestellt, bei der
rein geometrische Umgebungskarten durch semantische Objekte erweitert werden. Dies verbessert
das kontextuelle Verständnis der Umgebung und ermöglicht das Greifen und Transportieren von Ob-
jekten, während die kartierten Objekte gleichzeitig für die Optimierung der Trajektoriebestimmung
einbezogen werden können.
Zur Analyse von Interaktionspartnern wird eine neue Methode zur Kopfposeschätzung vorgestellt,
welche den gesamten Rotationsbereich abschätzen kann und in Robustheit und Genauigkeit den Stand
der Technik übertrifft. Diese Methode wird im Anschluss mittels eines Multi-Task-Ansatzes mit
einer Blickrichtungsschätzung kombiniert, um Synergien beider Aufgaben auszuschöpfen, welche
zu einer Verbesserung der Generalisierungsfähigkeit des Modells, insbesondere für die Blickrich-
tungsschätzung, führt. Mithilfe eines zusätzlichen Modells wird sich der Detektion von Blickkontakt
aus der Egoperspektive angenommen. Für diesen noch weitgehend unerforschten Bereich wird
eine umfangreiche Datenbank erzeugt, mit deren Hilfe akkurate und robuste Prädiktionsmodelle
erzeugt werden können, welche neben Kopfpose und Blickrichtung nonverbale Interaktionen mit
menschlichen Kooperationspartnern verbessern.
Insgesamt trägt diese Arbeit zur Verbesserung der mobilen Mensch-Roboter-Interaktion bei, indem
sie Lokalisierungsfehler in dynamischen Umgebungen reduziert, semantische Informationen in
die Umgebungserfassung einbettet und Methoden zur Erfassung und Verarbeitung menschlicher
Interaktionspartner entwickelt. Jede der vorgestellten Methoden ist dabei modular gestaltet, sodass
sie sowohl isoliert als auch in anderen Applikationsbereichen eingesetzt werden können.Mobile, intelligent robots can enhance productivity and efficiency in industry, reduce workplace
accidents and costs, and thereby contribute to environmentally friendly resource conservation.
Additionally, their use in medical and social fields holds significant potential to support collaboration
between those in need and caregivers, thus contributing to an improved quality of life.
This work focuses on the development, implementation, and evaluation of image-based deep learning
methods aimed at improving the social autonomy of mobile robots and enhancing their information
content for determining appropriate behavioral strategies. It is divided into several scientific con-
tributions that concentrate on spatial-semantic environment perception and the analysis of human
interaction partners.
The first contribution addresses the orientation of mobile robots in dynamic environments by ex-
tending visual SLAM (Simultaneous Localization and Mapping) with deep learning-generated
optical flow into a scene flow. This enables fine, pixel-based capture of dynamic image elements
and significantly reduces trajectory error. Next, a new method for semantic mapping is presented,
where purely geometric environment maps are augmented with semantic objects. This enhances the
understanding of the environment and enables the grasping and transporting of objects.
For the analysis of interaction partners, a new method for head pose estimation is introduced,
which can analyze the entire range of rotation and surpasses the state of the art in robustness and
accuracy. This method is subsequently combined with gaze estimation using a multi-task approach
to exploit synergies between both tasks, leading to an improvement in the model’s generalization
ability, especially for gaze estimation. An additional model addresses gaze contact detection from
an ego perspective. For this largely unexplored area, an extensive database is created, enabling the
development of accurate and robust prediction models that improve non-verbal interactions with
human cooperation partners by incorporating head pose and gaze direction.
Overall, this work contributes to the enhancement of human-robot interaction (HRI) by reducing
localization errors in dynamic environments, embedding semantic information into environment
perception, and developing methods for capturing and processing human interaction partners. Each
of the presented methods is modular in design, allowing them to be used both in isolation and in
other application areas.
Cobots (collaborative robots) are robots capable of interacting directly and safely with humans.
Unlike conventional industrial robots, which often work in enclosed areas, cobots can be used in
close proximity to humans. They are increasingly used in industry to automate physically demanding
or monotonous tasks, thus increasing productivity, and also offer the possibility for use in other areas
such as healthcare and even private use as personal assistants.
To fully exploit the potential of cobots, their abilities for autonomous navigation and interaction must
be further improved. Special challenges lie in environment sensing and in the registration of nonverbal
communication signals to enable efficient human-robot interactions without misunderstandings. This
dissertations presents a series of new methods that optimize human-robot interaction (HRI) through
image-based techniques. These include algorithms for reducing localization errors of mobile cobots in dynamic environments, embedding semantic information into their environment sensing, and
various methods for sensing and processing human interaction partners to enable more efficient and
intuitive collaborations.Literaturverzeichnis: Seite 133-16
Local coordinates on Lie groups for half-explicit time integration of Cosserat-rod models with constraints
Full text linkExplicit Runge-Kutta methods are the gold standard of time-integration methods for nonstiff problems in system dynamics since they combine a small numerical effort per time step with high accuracy, error control, and straightforward implementation. For the analysis of beam dynamics, we couple them with a local coordinates approach in a Lie group setting to address large rotations. Stiff shear forces and inextensibility conditions are enforced by internal constraints in a coarse-grid discretization of a geometrically exact beam model. The resulting nonstiff constrained systems are handled by a half-explicit approach that relies on the constraints at velocity level and avoids all kinds of Newton-Raphson iteration. We construct half-explicit Runge-Kutta Lie group methods of order up to five that are equipped with an adaptive step-size strategy using embedded Runge-Kutta pairs for error estimation. The methods are tested successfully for a roll-up maneuver of a flexible beam and for the classical flying-spaghetti benchmark
A network model for human playfulness during war
No full textThis novel study investigates adult playfulness during recent wartime in the Middle East using the OLIW model of playfulness and the concept of fantastic reality ability to utilize imagination in response to stress and trauma. Through a network analysis approach, we explore the relationships between playfulness, resilience, and clinical symptoms among N = 1511 Israeli participants. Our findings highlight the nuanced dynamics of playfulness amidst adversity. Notably, playfulness—particularly lighthearted playfulness—emerges as closely linked to resilience, suggesting its role as a coping mechanism during war. Additionally, the centrality of dissociation and transcendence within the network underscores their importance as potential targets for therapeutic interventions. Furthermore, our analysis highlights the potential roles of playful imagination and control, advocating for the testing of tailored interventions to enhance coping strategies and mental health outcomes in war-affected populations. This study offers valuable insights into responses to adversity, with implications for promoting resilience and mitigating the impact of trauma
Towards transformative change for biodiversity : what can we learn from case studies in Germany?
No full textCurrent human activities have led to fundamental changes in ecosystems, including the loss of biodiversity, which increasingly leads to irreversible negative impacts on society. Although called for in many policy documents, the debate on how to initiate, promote and specifically support socio-ecological transformations for the conservation and restoration of biodiversity is still in its early stages. So far, efforts to protect biodiversity were only partially successful. Therefore, there is a need for approaches to promote societal change for the benefit of biodiversity. We analysed 22 case studies of biodiversity-enhancing societal processes and projects in Germany to understand barriers and success factors and to identify features that support transformative change towards sustainability and biodiversity mainstreaming. Following Wittmer et al. (2021), the following topics were analysed: a) orientation towards a shared and compelling vision that enables biodiversity conservation or enhancement (transformative vision), b) the role of (different types of) knowledge about how to change the system (transformative knowledge), c) navigating the dynamics inherent in changing development pathways (transformational dynamics), d) enabling emancipated action and opening spaces for creative participation of different social groups (emancipation and agency), and e) targeted interventions that aim to enable governance for transformation. This article discusses lessons learned from examples in Germany to support future transformative processes for biodiversity conservation, restoration and biodiversity mainstreaming. It identifies 16 features, enabling transformative change for biodiversity, many of which may be applicable in other countries with similar governance contexts. These characteristics suggest that a structured and well-informed approach, based on a broad range of communication, engagement, negotiation, and stakeholder involvement efforts throughout the process, is well-suited for developing and implementing proposals. While in some small cases indirect drivers were addressed, achieving this on a broader scale is the largest remaining challenge
Antimycobacterial characterisation of Nα-aroyl-N-aryl phenylalanine amides with focus on Mycobacterium abscessus
No full textThe work carried out as part of the dissertation included the establishment of different methods for testing substances on relevant non-tuberculous mycobacteria, such as M. abscessus. In particular, the possibility of using different methods (synergy testing, macrophage-infection-assay, MBC determination) ensured a differentiated characterisation of the substances. This contributed to the optimisation and further development of the potential drug candidate MMV688845, an RNA polymerase inhibitor from the Nα-aroyl-N-aryl-phenylalaninamide substance class. As part of the work, an assay based on fluorescence microscopic evaluation was also developed to simplify the determination of minimal bactericidal concentrations