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In selective laser melting (SLM), a powdered material is locally melted by a laser and, after cooling, forms a coherent solid structure that enables the production of complex geometries with various materials. The process involves extreme heating and cooling rates and, thus, large temperature gradients, which lead to anisotropic material properties on the macroscopic scale and, in the worst case, reduced mechanical properties. In order to reliably predict the final mechanical component properties, simulations can be performed at different time and length scales. Enormous computational resources are often required to perform such simulations. In order to transform these simulations into suitable surrogate models, the generated data must be compressed and evaluated in a suitable way. This paper shows first preliminary work and a possible new data description of such simulations.
Tracking waterborne microplastic (MP) in urban areas is a challenging task because of the various sources and transport pathways involved. Since MP occurs in low concentrations in most wastewater and stormwater streams, large sample volumes need to be captured, prepared, and carefully analyzed. The recent research in urban areas focused mainly on MP emissions at wastewater treatment plants (WWTPs), as obvious entry points into receiving waters. However, important transport pathways under wet-weather conditions are yet not been investigated thoroughly. In addition, the lack of comprehensive and comparable sampling strategies complicated the attempts for a deeper understanding of occurrence and sources. The goal of this paper is to (i) introduce and describe sampling strategies for MP at different locations in a municipal catchment area under dry and wet-weather conditions, (ii) quantify MP emissions from the entire catchment and two other smaller ones within the bigger catchment, and (iii) compare the emissions under dry and wet-weather conditions. WWTP has a high removal rate of MP (>96%), with an estimated emission rate of 189 kg/a or 0.94 g/[population equivalents (PEQ · a)], and polyethylene (PE) as the most abundant MP. The specific dry-weather emissions at a subcatchment were ≈30 g/(PEQ · a) higher than in the influent of WWTP with 23 g/(PEQ · a). Specific wet-weather emissions from large sub-catchment with higher traffic and population densities were 1952 g/(ha · a) higher than the emissions from smaller catchment (796 g/[ha · a]) with less population and traffic. The results suggest that wet-weather transport pathways are likely responsible for 2–4 times more MP emissions into receiving waters compared to dry-weather ones due to tire abrasion entered from streets through gullies. However, more investigations of wet-weather MP need to be carried out considering additional catchment attributes and storm event characteristics.
Model-based prediction is becoming increasingly important to meet the ever-increasing demands on manufacturing. In grinding, the prediction of the process forces and the generated surface by physical models are particularly important.Since cooling lubricants are almost always used on an industrial scale, the grinding model, developed at our institut, must be extended to include this component. Therefore, in order to implement cooling lubricants into the FEM-based model, it is first necessary to investigate the behaviors and effects of cooling lubricants in real experiments. Various influencing factors such as the scratching speed of individual abrasive grains in interaction with cooling lubricants need to be investigated. However, the existing physical grinding model is not limited exclusively to the prediction of the resulting forces. It is also supposed to be able to qualitatively predict the expected resulting surface of the workpiece. Hence, this paper will focus on the topographic characteristics that can occur in the scratch test due to different cooling lubricants and scratching speeds.Based on real experiments on a test rig for such scratch tests, it has been shown that different scratch speeds have a negligible influence on the topographical nature and expression of a scratch. In contrast, however, there is a direct influence of cooling lubricants on the topographic properties. This effect is additionally influenced by the viscosity of the cooling lubricant used.
In gravity separators, also known as settlers, two immiscible liquid phases separate due to differences in density. In extraction mixer-settler units, a dispersion needs to be separated within the separator unit. In order to overcome the hitherto purely experimental design, a knitted mesh adapted model as well as an automated test facility were developed in this work, which easily enable a scale-up to industrial units. An automation allows for a controlled investigation of knitted meshes as coalescing aids in settlers, and this was achieved via photo-optical probes with an optimized image analysis technique. It overcomes the limitations of neuronal network training based on manually annotating images using computer-generated image data. Therefore, the new methodology and setup are explained in detail, and the derivation and application of a new model to design separators with knitted meshes as coalescing aid is presented and compared to experimental results using meshes of different structures and materials. Finally, case studies and scale-up are discussed.
Within a biorefinery platform several conversion steps such as pretreatment, saccharification, fermentation and downstream processing are necessary to obtain the final bio-based product(s) from lignocellulosic biomass. The structural composition of the biomass, especially the lignin content, determines the necessary pretreatment steps. To obtain sugar monomers, the hydrolysis of lignocellulosic biomass is an essential step. This work examines the impact of different pretreatments on the sugar release during biocatalysis. Even without prior pretreatment the biocatalysis of low lignin biomass achieves glucose yields of up to 93 %, while the biocatalysis of high lignin biomass requires an upstream hydrothermal procedure to achieve a glucose yield of 74
Distributed message-passing systems have become ubiquitous and essential for our daily lives. Hence, designing and implementing them correctly is of utmost importance. This is, however, very challenging at the same time. In fact, it is well-known that verifying such systems is algorithmically undecidable in general due to the interplay of asynchronous communication (messages are buffered) and concurrency. When designing communication in a system, it is natural to start with a global protocol specification of the desired communication behaviour. In such a top-down approach, the implementability problem asks, given such a global protocol, if the specified behaviour can be implemented in a distributed setting without additional synchronisation. This problem has been studied from two perspectives in the literature. On the one hand, there are Multiparty Session Types (MSTs) from process algebra, with global types to specify protocols. Key to the MST approach is a so-called projection operator, which takes a global type and tries to project it onto every participant: if successful, the local specifications are safe to use. This approach is efficient but brittle. On the other hand, High-level Message Sequence Charts (HMSCs) study the implementability problem from an automata-theoretic perspective. They employ very few restrictions on protocol specifications, making the implementability problem for HMSCs undecidable in general. The work in this thesis is the first to formally build a bridge between the world of MSTs and HMSCs. To start, we present a generalised projection operator for sender-driven choice. This allows a sender to send to different receivers when branching, which is crucial to handle common communication patterns from distributed computing. Despite this first step, we also show that the classical MST projection approach is inherently incomplete. We present the first formal encoding from global types to HMSCs. With this, we prove decidability of the implementability problem for global types with sender-driven choice. Furthermore, we develop the first direct and complete projection operator for global types with sender-driven choice, using automata-theoretic techniques, and show its effectiveness with a prototype implementation. We are the first to provide an upper bound for the implementability problem for global types with sender-driven (or directed) choice and show it to be in PSPACE. We also provide a session type system that uses the results from our projection operator. Last, we introduce protocol state machines (PSMs) – an automata-based protocol specification formalism – that subsume both global types from MSTs and HMSCs with regard to expressivity. We use transformations on PSMs to show that many of the syntactic restrictions of global types are not restrictive in terms of protocol expressivity. We prove that the implementability problem for PSMs with mixed choice, which requires no dedicated sender for a branch but solely all labels to be distinct, is undecidable in general. With our results on expressivity, this answers an open question: the implementability problem for mixed-choice global types is undecidable in general.
Sound localization involves information analysis in the lateral superior olive (LSO), a conspicuous nucleus in the mammalian auditory brainstem. LSO neurons weigh interaural level differences (ILDs) through precise integration of glutamatergic excitation from the cochlear nucleus (CN) and glycinergic inhibition from the medial nucleus of the trapezoid body (MNTB). Sound sources can be localized even during sustained perception, an accomplishment that requires robust neurotransmission. Virtually nothing is known about the sustained performance and the temporal precision of MNTB–LSO inputs after postnatal day (P)12 (time of hearing onset) and whether acoustic experience guides development. Here we performed whole-cell patch-clamp recordings to investigate neurotransmission of single MNTB-LSO fibres upon sustained electrical stimulation (1–200 Hz/60 s) at P11 and P38 in wild-type (WT) and deaf otoferlin (Otof) knock-out (KO) mice. At P11, WT and KO inputs performed remarkably similarly. In WTs, the performance increased drastically between P11 and P38, e.g. manifested by an 8 to 11-fold higher replenishment rate (RR) of synaptic vesicles and action potential robustness. Together, these changes resulted in reliable and highly precise neurotransmission at frequencies ≤100 Hz. In contrast, KO inputs performed similarly at both ages, implying impaired synaptic maturation. Computational modelling confirmed the empirical observations and established a reduced RR per release site for P38 KOs. In conclusion, acoustic experience appears to contribute massively to the development of reliable neurotransmission, thereby forming the basis for effective ILD detection. Collectively, our results provide novel insights into experience-dependent maturation of inhibitory neurotransmission and auditory circuits at the synaptic level.
Development of a simple substitute model to describe the normal force of fluids in narrow gaps
(2023)
Fluids in narrow gaps are employed frequently in many applications. The motivation for their use is diverse and ranges from hydrodynamic lubrication in plain bearings to the transport of hard particles into the working gap for the purpose of machining workpiece surfaces in lapping processes. Depending on the focus of the analysis, it may be useful to investigate the entire pressure field or to calculate only individual quantities. For example, in sophisticated simulations it may be of interest to know the resulting force of a fluid as a function of the external system state in order to describe its damping characteristics. Especially for the simulation of flows in narrow gaps, the Reynolds equation is a convenient choice, which, in contrast to the more general Navier-Stokes equations, can lead to considerable savings in computational time because no three-dimensional discretization is required, but only a two-dimensional discretization. However, if not a highly detailed pressure field is of interest, but only simple relations such as the resulting force as a function of distance and velocity, and if this relation to be evaluated many times for different parameter combinations over a wide range of values, the use of a robust substitute model is a good choice. This article deals with the creation of such a substitute model based on the Reynolds equation taking cavitation into account.
Machine learning algorithms are widely applied to create powerful prediction models. With increasingly complex models, humans' ability to understand the decision function (that maps from a high-dimensional input space) is quickly exceeded. To explain a model's decisions, black-box methods have been proposed that provide either non-linear maps of the global topology of the decision boundary, or samples that allow approximating it locally. The former loses information about distances in input space, while the latter only provides statements about given samples, but lacks a focus on the underlying model for precise ‘What-If'-reasoning. In this paper, we integrate both approaches and propose an interactive exploration method using local linear maps of the decision space. We create the maps on high-dimensional hyperplanes—2D-slices of the high-dimensional parameter space—based on statistical and personal feature mutability and guided by feature importance. We complement the proposed workflow with established model inspection techniques to provide orientation and guidance. We demonstrate our approach on real-world datasets and illustrate that it allows identification of instance-based decision boundary structures and can answer multi-dimensional ‘What-If'-questions, thereby identifying counterfactual scenarios visually.
In the last decades, the phase field method has drawn much attention for its application in fracture mechanics because it offers a simple unified framework for crack propagation. The core idea of phase field models for fracture is to introduce a continuous scalar field representing the discontinuous crack. Recently, a phase field model for fatigue has been proposed along this path. The fatigue failure differs from the other fracture scenarios since cracks only occur after a considerable number of load cycles. As fracturing happens, changes of the material microstructure are involved, which causes the evolution of the structural configuration. Thus, a new mathematical description not based on traditional spatial coordinates but the material manifold is desired, which will serve as an elegant analysis tool to understand the energetic forces for crack propagation. Configurational forces are a suitable choice for this purpose, as they describe the energetic driving forces associated with phenomena changing the material itself. In this work, we present a phase field model for fatigue. Furthermore, the phase field fatigue model is analyzed within the concept of configurational forces, which provides a straightforward way to understand the phase field simulations of fatigue fracture.