In der vorliegenden Arbeit wurde das Verhalten dünner Flüssigkeitsfilme an gekrümmten Substratoberflächen durch experimentelle Beschichtungsversuche basierend auf der non-invasiven laserinduzierten Fluoreszenzmesstechnik und durch numerische Filmsimulationen mit Hilfe des Volume-of-Fluid Mehrphasenmodells untersucht. Besonderes Interesse galt dabei dem Finden optimaler Einflussgrößenkombinationen zur Reduzierung des Fettkanten-Effekts. In der hierfür durchgeführten Parameterstudie wurden sowohl Applikationsparameter wie der Kantenrundungsradius und die Applikationsschichtdicke als auch Stoffparameter der untersuchten Flüssigkeit wie die Viskosität und die Oberflächenspannung variiert. Neben qualitativen Beschreibungen der entstandenen Fettkantengestalten sind als Resultate auch Größen zur Quantifizierung der Fettkanten festgelegt worden und systematisch dargestellt. Es konnte nachgewiesen werden, dass ungünstige und geeignete Parameterkonfigurationen existieren, welche prägnante bzw. kaum auffällige Fettkanten erzeugen, insbesondere im Experiment. Über die dabei eingreifenden Mechanismen der zugrundeliegenden Strömungen wurden konkrete Hypothesen aufgestellt, auch um die resultierenden Proportionalitäten der Fettkantengrößen bezüglich der Einflussgrößen zu plausibilisieren. Weiterhin konnte eine Aussage über die Signifikanz der untersuchten Einflussgrößen getroffen werden. Abschließend wurde eine geeignete dimensionslose Kenngröße generiert, um den Fettkanten-Effekt parameterübergreifend beschreiben zu können, wodurch mittels der Ähnlichkeitstheorie auch eine gewisse Abschätzung des Fettkanten-Effekts ermöglicht wird. / In this study the behaviour of a thin liquid layer at a curved solid edge was examined by experimental coating investigations based on the laser-induced fluorescence technique and by numerical film simulations based on the Volume-of-Fluid multiphase flow model, respectively. The main motivation was to find optimal combinations of influencing quantities to reduce the fat-edge effect. Therefore a study of these quantities was performed, in which application parameters like edge radii of curvature and application layer thicknesses as well as determining liquid properties like viscosity and surface tension have been varied. Results are described qualitatively at corresponding fat-edge shapes and quantified by suitable fat-edge parameters, which had to be identified and selected. It could be shown that adverse and appropriate influencing parameter combinations exist, which generate conspicuous and less distinctive fat-edges, respectively - especially in laboratory experiments. The experimental findings and proportionalities regarding fat-edge shapes and dimensions are found to be physically plausible. Furthermore an order of significance of the influencing quantities established. Eventually, a dimensionless quantity was derived by dimensional analysis, which describes the fat-edge effect. Thus, the fat-edge effect has also been described by the application of similarity theory and the corresponding dimenionless number, respectively.

This thesis presents work around 3 themes: dark energy, gravitational waves and Bayesian inference. Both dark energy and gravitational wave physics are not yet well constrained. They present interesting challenges for Bayesian inference, which attempts to quantify our knowledge of the universe given our astrophysical data. A dark energy equation of state reconstruction analysis finds that the data favours the vacuum dark energy equation of state $w {=} -1$ model. Deviations from vacuum dark energy are shown to favour the super-negative ‘phantom’ dark energy regime of $w {< } -1$, but at low statistical significance. The constraining power of various datasets is quantified, finding that data constraints peak around redshift $z = 0.2$ due to baryonic acoustic oscillation and supernovae data constraints, whilst cosmic microwave background radiation and Lyman-$\alpha$ forest constraints are less significant. Specific models with a conformal time symmetry in the Friedmann equation and with an additional dark energy component are tested and shown to be competitive to the vacuum dark energy model by Bayesian model selection analysis: that they are not ruled out is believed to be largely due to poor data quality for deciding between existing models. Recent detections of gravitational waves by the LIGO collaboration enable the first gravitational wave tests of general relativity. An existing test in the literature is used and sped up significantly by a novel method developed in this thesis. The test computes posterior odds ratios, and the new method is shown to compute these accurately and efficiently. Compared to computing evidences, the method presented provides an approximate 100 times reduction in the number of likelihood calculations required to compute evidences at a given accuracy. Further testing may identify a significant advance in Bayesian model selection using nested sampling, as the method is completely general and straightforward to implement. We note that efficiency gains are not guaranteed and may be problem specific: further research is needed.