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Mit modernen nicht invasiven bildgebenden Verfahren lassen sich anhand der Fundusfotografie bzw. der optischen Verfilmung Aspekte der funktionellen und strukturellen retinalen Gefäßveränderungen objektiv untersuchen. Der Zustand und das Verhalten retinaler Gefäße beeinflussen im prä-, post- und kapillaren Bereich den Blutfluss und strömungsbedingte Stoffwechselverhältnisse passiv und aktiv über den Gefäßdurchmesser. Retinale Gefäße gleichen von Aufbau und Funktion den zerebralen Gefäßen und spiegeln den Zustand der Mikrozirkulation wider. Mithilfe von aus den Gefäßweiten berechneten Biomarkern soll eine Aussage über die Prognose von systemischen vaskulär bedingten Erkrankungen getroffen werden. Die statische retinale Gefäßanalyse befasst sich mit der Untersuchung des Zustandes der prä- und postkapillaren Gefäßdurchmesser der retinalen Mikrozirkulation anhand einer optischen Fundusaufnahme. Bei der dynamischen retinalen Gefäßanalyse wird der Längsschnitt eines retinalen Gefäßes nicht invasiv funktionell und strukturell über einen Zeitraum vor, während und nach einer spezifischen vaskulären Stimulation untersucht. Die genaue Methodologie der Auswertung und die Bezeichnung der Parameter variieren bei unterschiedlichen Ansätzen. Mittels retinaler Gefäßanalyse wurden bislang mehrere klinische Querschnitts- und Interventionsstudien in der Augenheilkunde und anderen Fachgebieten, inkl. Kardiologie, Neurologie, Neurochirurgie, Nephrologie, Gynäkologie, Sportmedizin, Diabetologie, Hypertensiologie usw. durchgeführt. Mit der statischen retinalen Gefäßanalyse steht eine kostengünstige, reproduzierbare, nicht invasive Screeningtechnik zur Verfügung, um eine prognostische Aussage über die Gefäßgesundheit eines individuellen Patienten zu treffen. Die dynamische retinale Gefäßanalyse besitzt ein weiteres diagnostisches Anwendungsspektrum als die statische, da sie das Verhalten retinaler Gefäße zeitkontinuierlich untersucht. Die Evaluation vaskulärer Erkrankungen sowie zerebro- bzw. kardiovaskulärer Morbidität und Mortalität mittels mehrerer methodologischer Modalitäten retinaler Gefäßanalyse mit ihren jeweiligen quantitativen Biomarkern bietet eine zukunftsträchtige diagnostische Perspektive. Die interdisziplinäre klinische Anwendung dieser vaskulären Biomarker gewinnt zunehmend an Bedeutung, sowohl in der Augenheilkunde als auch in anderen Fachgebieten.
Bereits 2015 wurde in der Zeitschrift „Qualität in der Wissenschaft“ (QiW) über das Kooperationsprojekt „Guter Studienstart im Ingenieurbereich“, das Orientierungssemester von RWTH und FH Aachen, berichtet. In diesem Artikel legen wir den Schwerpunkt auf die Entwicklung des Projekts in den Jahren 2015 bis 2018 und geben eine Rückschau sowie einen Ausblick auf die Entwicklungsperspektiven nach dem offiziellen Projektende.
Slot die coating is applied to deposit thin and homogenous films in roll-to-roll and sheet-to-sheet applications. The critical step in operation is to choose suitable process parameters within the process window. In this work, we investigate an upper limit for stripe coatings. This maximum film thickness is characterized by stripe merging which needs to be avoided in a stable process. It is shown that the upper limit reduces the process window for stripe coatings to a major extent. As a result, stripe coatings at large coating gaps and low viscosities are only possible for relatively thick films. Explaining the upper limit, a theory of balancing the side pressure in the gap region in the cross-web direction has been developed.
Die Batterie ist eine der absolut zentralen Komponenten des Elektrofahrzeugs. Die serielle Entwicklung und Produktion dieser Batterien und die Verbesserung der Leistungen wird entscheidend für den Erfolg der Elektromobilität sein. Die Batterie ist jedoch nicht das einzige elektrofahrzeugspezifische System, das neu entwickelt, umkonzipiert oder verbessert werden muss. So sind ebenso die Entwicklung der neuen Fahrzeugstruktur sowie des elektrifizierten Antriebsstranges Teil dieses Kapitels. Weiterhin wird ein Blick auf das bedeutende Thema des Thermomanagements geworfen.
In energy economy forecasts of different time series are rudimentary. In this study, a prediction for the German day-ahead spot market is created with Apache Spark and R. It is just an example for many different applications in virtual power plant environments. Other examples of use as intraday price processes, load processes of machines or electric vehicles, real time energy loads of photovoltaic systems and many more time series need to be analysed and predicted.
This work gives a short introduction into the project where this study is settled. It describes the time series methods that are used in energy industry for forecasts shortly. As programming technique Apache Spark, which is a strong cluster computing technology, is utilised. Today, single time series can be predicted. The focus of this work is on developing a method to parallel forecasting, to process multiple time series simultaneously with R and Apache Spark.
Around 60% of the paper worldwide is made from recovered paper. Especially adhesive contaminants, so called stickies, reduce paper quality. To remove stickies but at the same time keep as many valuable fibers as possible, multi-stage screening systems with several interconnected pressure screens are used. When planning such systems, suitable screens have to be selected and their interconnection as well as operational parameters have to be defined considering multiple conflicting objectives. In this contribution, we present a Mixed-Integer Nonlinear Program to optimize system layout, component selection and operation to find a suitable trade-off between output quality and yield.
Given industrial applications, the costs for the operation and maintenance of a pump system typically far exceed its purchase price. For finding an optimal pump configuration which minimizes not only investment, but life-cycle costs, methods like Technical Operations Research which is based on Mixed-Integer Programming can be applied. However, during the planning phase, the designer is often faced with uncertain input data, e.g. future load demands can only be estimated. In this work, we deal with this uncertainty by developing a chance-constrained two-stage (CCTS) stochastic program. The design and operation of a booster station working under uncertain load demand are optimized to minimize total cost including purchase price, operation cost incurred by energy consumption and penalty cost resulting from water shortage. We find optimized system layouts using a sample average approximation (SAA) algorithm, and analyze the results for different risk levels of water shortage. By adjusting the risk level, the costs and performance range of the system can be balanced, and thus the
system’s resilience can be engineered
To increase pressure to supply all floors of high buildings with water, booster stations, normally consisting of several parallel pumps in the basement, are used. In this work, we demonstrate the potential of a decentralized pump topology regarding energy savings in water supply systems of skyscrapers. We present an approach, based on Mixed-Integer Nonlinear Programming, that allows to choose an optimal network topology and optimal pumps from a predefined construction kit comprising different pump types. Using domain-specific scaling laws and Latin Hypercube Sampling, we generate different input sets of pump types and compare their impact on the efficiency and cost of the total system design. As a realistic application example, we consider a hotel building with 325 rooms, 12 floors and up to four pressure zones.
Highly competitive markets paired with tremendous production volumes demand particularly cost efficient products. The usage of common parts and modules across product families can potentially reduce production costs. Yet, increasing commonality typically results in overdesign of individual products. Multi domain virtual prototyping enables designers to evaluate costs and technical feasibility of different single product designs at reasonable computational effort in early design phases. However, savings by platform commonality are hard to quantify and require detailed knowledge of e.g. the production process and the supply chain. Therefore, we present and evaluate a multi-objective metamodel-based optimization algorithm which enables designers to explore the trade-off between high commonality and cost optimal design of single products.
Resilience as a concept has found its way into different disciplines to describe the ability of an individual or system to withstand and adapt to changes in its environment. In this paper, we provide an overview of the concept in different communities and extend it to the area of mechanical engineering. Furthermore, we present metrics to measure resilience in technical systems and illustrate them by applying them to load-carrying structures. By giving application examples from the Collaborative Research Centre (CRC) 805, we show how the concept of resilience can be used to control uncertainty during different stages of product life.
The overall energy efficiency of ventilation systems can be improved by considering not only single components, but by considering as well the interplay between every part of the system. With the help of the method "TOR" ("Technical Operations Research"), which was developed at the Chair of Fluid Systems at TU Darmstadt, it is possible to improve the energy efficiency of the whole system by considering all possible design choices programmatically. We show the ability of this systematic design approach with a ventilation system for buildings as a use case example.
Based on a Mixed-Integer Nonlinear Program (MINLP) we model the ventilation system. We use binary variables to model the selection of different pipe diameters. Multiple fans are model with the help of scaling laws. The whole system is represented by a graph, where the edges represent the pipes and fans and the nodes represents the source of air for cooling and the sinks, that have to be cooled. At the beginning, the human designer chooses a construction kit of different suitable fans and pipes of different diameters and different load cases. These boundary conditions define a variety of different possible system topologies. It is not possible to consider all topologies by hand. With the help of state of the art solvers, on the other side, it is possible to solve this MINLP.
Next to this, we also consider the effects of malfunctions in different components. Therefore, we show a first approach to measure the resilience of the shown example use case. Further, we compare the conventional approach with designs that are more resilient. These more resilient designs are derived by extending the before mentioned model with further constraints, that consider explicitly the resilience of the overall system. We show that it is possible to design resilient systems with this method already in the early design stage and compare the energy efficiency and resilience of these different system designs.
The energy-efficiency of technical systems can be improved by a systematic design approach. Technical Operations Research (TOR) employs methods known from Operations Research to find a global optimal layout and operation strategy of technical systems. We show the practical usage of this approach by the systematic design of a decentralized water supply system for skyscrapers. All possible network options and operation strategies are modeled by a Mixed-Integer Nonlinear Program. We present the optimal system found by our approach and highlight the energy savings compared to a conventional system design.
The UN sets the goal to ensure access to water and sanitation for all people by 2030. To address this goal, we present a multidisciplinary approach for designing water supply networks for slums in large cities by applying mathematical optimization. The problem is modeled as a mixed-integer linear problem (MILP) aiming to find a network describing the optimal supply infrastructure. To illustrate the approach, we apply it on a small slum cluster in Dhaka, Bangladesh.