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The dripping behaviour of polymers is often observed experimentally through the UL94 flammability standard test. In this work, polymeric dripping under fire is investigated numerically using particle finite element method. A parametric analysis was carried out to observe the influence of a single property on overall dripping behaviour via a UL94 vertical test model. Surrogates and property ranges were defined for variation of the following parameters: glass transition temperature (Tg), melting temperature (Tm), decomposition temperature (Td), density (ρ), specific heat capacity (Cp), apparent effective heat of combustion of the volatiles, char yield (μ), thermal conductivity (k), and viscosity (η). Polyamide, poly(ether ether ketone), poly(methyl methacrylate), and polysulfone were used as benchmarks. Simulated results showed that specific heat capacity, thermal conductivity, and char yield allied with viscosity were the properties that most influenced dripping behaviour (starting time and occurrence).
Der Fachbereich 7.6 untersucht seit einiger Zeit an Auslagerungsständen auf dem Testgelände in Horstwalde Korrosion im maritimen Bereich. Im Moment läuft das System mit künstlichem Meerwasser und simuliert die Bewitterung von Metallproben unter angenäherten klimatischen Bedingungen ähnlich zum natürlichem Habitat. Im Laufe der Zeit konnte nicht intendierter mikrobieller bewuchs beobachtet werden. Es stellte sich die Frage, welche Mikroorganismen vorlagen und ob diese einen Einfluss auf die Korrosionsuntersuchungen haben könnte. Aus diesem Grund wurden 16S-rRNA Untersuchungen durchgeführt welche alle drei Domänen des Lebens widerspiegeln Bakterien, Archaea und Eukaryoten. Es konnte gezeigt werden, dass über 95% der vorliegenden Biomasse Grünalgen waren, die durch Licht Eintrag in den Container Photosynthese betrieben und dadurch an Biomasse zunahmen. Des weiteren konnten Bakterien detektiert werden, welche in der Regel halophilen und aeroben Habitaten zu finden sind. Allerdings wurden auch Sulfat reduzierende Bakterien (MIC) detektiert, wenn auch in einem geringen prozentualen Anteil. Es muss allerdings Berücksichtigt werden, dass die mikrobielle Zusammensetzung sich im Laufe der Zeit weiter ändern kann. Als Ursprung der Biomasse wird, das künstliche Sediment vermutet. Außerdem wäre es für zukünftige Experiment denkbar, das System mit echtem Sediment aus dem marinen Habitat anzuimpfen.
Combination of model reduction and adaptive subset simulation for structural reliability problems
(2019)
A safe and robust design is a key criterion when building a structure or a component. Ensuring this criterion can either be performed by fullfilling prescribed safety margins, or by using a full probabilistic approach with a computation of the failure probability. The latter approach is particularly well suited for complex Problems with an interaction of different physical penomena that can be described in a numerical model. The bottleneck in this approach is the computational effort. Sampling methods such as Markov chain Monte Carlo methods are often used to evaluate the system reliability. Due to small failure probabilities (e.g. 10^6) and complex physical models with already and extensive computational effort for a single set of parameters, these methods a prohibitively expensive. The focus of this contribution is to demonstrate the advantages of combining model reduction techniques within the concept a variance reducing adaptive sampling procedures. In the developed method, a modification of the adaptive subset simulation based on Papaioannou et al. 2015 is used and coupled with a limit state function based on Proper Generalized Decomposition (PGD) (Chinesta et al. 2011). In the subset simulation the failure probability is expressed as a product of larger conditional failure probabilities. The intermediate failure events are chosen as a decreasing sequence. Instead of solving each conditional probability with a Markov chain approach, an importance sampling approach is used. It is be shown that the accuracy of the estimation depends mainly on the number of samples in the last sub-problem. For model reduction, the PGD approach is used to solve the structural problem a priori for a given Parameter space (physical space plus all random parameters). The PGD approach results in an approximation of the problem output within a prescribed range of all input Parameters (load factor, material properties, ..). The approximation of the solution by a separated form allows an evaluation of the limit state function within the sampling algorithm with almost no cost. This coupled PGD – adaptive subset Simulation approach is used to estimate the failure probability of examples with different complexity. The convergence, the error propagation as well as the reduction in computational time is discussed.