Entwicklung einer intelligenten Struktur : eine Kombination globaler und lokaler Verfahren zur Schadensdiagnose

Während in der Vergangenheit oftmals die technische Umsetzbarkeit einer Produktidee im Vordergrund stand, entscheidet gegenwärtig und zukünftig wohl in zunehmenden Maße die Höhe der Betriebskosten über den wirtschaftlichen Erfolg eines Produktes. Die Lösung dieses Problems stellen u.a. die Prinzipien und Konzepte, die unter dem Oberbegriff Leichtbau bekannt geworden sind, dar. Vornehmlich beschäftigt sich der Leichtbau damit, das Strukturgewicht und die damit eng verknüpften Betriebskosten zu reduzieren ohne dabei die technologischen Eigenschaften zu verschlechtern. Möglich wird dies jedoch erst durch eine schadenstolerante Auslegung, d.h. das bewusste in Kauf nehmen eines möglichen Schadens noch während der Produktlebensdauer. Die Überwachung von Strukturen ist Gegenstand des sogenannten Structural Health Monitoring (SHM), das alle Verfahren, Entwicklungen und Forschungsaktivitäten auf diesem Gebiet umfasst. <br /> In dieser Arbeit wird zunächst ein Überblick über die derzeitig auf dem Gebiet des Structural Health Monitoring stattfindende Forschung gegeben. Der Fokus richtet sich dabei insbesondere auf die <i>globalen</i> und <i>lokalen</i> Verfahren, die im weiteren Verlauf der Arbeit zur Entwicklung einer Intelligenten Struktur genutzt werden sollen. Unter Intelligenten Strukturen versteht der Verfasser Strukturen, die, mit Aktoren, Sensoren und Auswertealgorithmen ausgestattet, in der Lage sind, selbstständig über ihren Zustand Auskunft zu geben. <br /> Im Grundlagenteil der Arbeit werden die theoretischen Voraussetzungen und die physikalischen Wirkprinzipien, auf denen die eingesetzten und entwickelten Verfahren basieren, zusammengefasst. Im Anschluss daran wird die im Rahmen dieser Arbeit entwickelte Intelligente Struktur vorgestellt. Die letzten beiden Kapitel zeigen schließlich die Leistungsfähigkeit dieser Struktur und geben einen Ausblick auf die noch zu leistende Forschungsarbeit.To realise a product idea in the best technical way has been the dominating thought of the past, whereas today it becomes more and more likely that the operating costs will decide about the economical success of a product. The solution of this problem could be found in the principals and concepts developed in the context of the idea of a lightweight construction. The mayor concern of such a construction is to reduce structural weight - and at the same time related operational costs - without decreasing technological properties. This goal can only be achieved by taking into account that a damage may already occur within economic life-time. Such a concept may then be called a “damage tolerant design”. The monitoring of structures describes the topic of the so-called “structural health monitoring” (SHM), which includes all methods, developments and research activities in this field. <br /> The paper starts with an overview of today’s research activities in the field of structural health monitoring. The focus is directed to <i>global</i> and <i>local</i> methods that will later be used for the development of a smart structure. The author defines a smart structure as a structure that is equipped with sensors and actuators and that includes all algorithms to perform a self diagnosis. <br /> The basics of this paper describe the theory and the physical mechanisms the used and developed methods are based on. After that the developed smart structure will be introduced. The last two chapters finally show the performance of the smart structure and give an outlook on the research activities that still have to be done

Engineering Local Electricity Markets for Residential Communities

In line with the progressing decentralization of electricity generation, local electricity markets (LEMs) support electricity end customers in becoming active market participants instead of passive price takers. They provide a market platform for trading locally generated (renewable) electricity between residential agents (consumers, prosumers, and producers) within their community. Based on a structured literature review, a market engineering framework for LEMs is developed. The work focuses on two of the framework\u27s eight components, namely the agent behavior and the (micro) market structure. Residential agent behavior is evaluated in two steps. Firstly, two empirical studies, a structural equation model-based survey with 195 respondents and an adaptive choice-based conjoint study with 656 respondents, are developed, conducted and evaluated. Secondly, a discount price LEM is designed following the surveys\u27 results. Theoretical solutions of the LEM bi-level optimization problem with complete information and heuristic reinforcement learning with incomplete information are investigated in a multi-agent simulation to find the profit-maximizing market allocations. The (micro) market structure is investigated with regards to LEM business models, information systems and real-world application projects. Potential business models and their characteristics are combined in a taxonomy based on the results of 14 expert interviews. Then, the Smart Grid Architecture Model is utilized to derive the organizational, informational, and technical requirements for centralized and distributed information systems in LEMs. After providing an overview on current LEM implementations projects in Germany, the Landau Microgrid Project is used as an example to test the derived requirements. In conclusion, the work recommends current LEM projects to focus on overall discount electricity trading. Premium priced local electricity should be offered to subgroups of households with individual higher valuations for local generation. Automated self-learning algorithms are needed to mitigate the trading effort for residential LEM agents in order to ensure participation. The utilization of regulatory niches is suggested until specific regulations for LEMs are established. Further, the development of specific business models for LEMs should become a prospective (research) focus

Data Driven Creation of Sentiment Dictionaries for Corporate Credit Risk Analysis

It has been shown, that German-language user generated content can improve corporate credit risk assessment, when sentiment analysis is applied. However, the approaches have only been conducted by human coders. In order to automate the analysis, we construct 20 domain-dependent sentiment dictionaries based on parts of a manually classified corpus from Twitter. Then, we apply the dictionaries to the remaining part of the corpus and rank the dictionaries based on their accuracy. Results from McNemar’s tests indicate, that the three best dictionaries do not differ significantly, but significant difference can be assured regarding the first and the fourth dictionary in the ranking. In addition to that, a general German-language dictionary is inferior compared to the constructed dictionaries. The results emphasize the importance of domain-dependent dictionaries in German-language sentiment analysis for future research. Furthermore, practitioners can utilize the dictionaries in order to create an additional indicator for corporate credit risk assessment

Forward and inverse calculation methods for Lorentz force evaluation applied to laminated composites

The classification of material deficiencies is a key feature in quality assurance. In this framework, laminated composite materials are of special interest, because they increasingly replace monolithic materials. The Lorentz force evaluation (LFE) is an evaluation technique to reconstruct the geometry of flaws in electrically conducting composites using inverse calculations. These calculations are based on perturbations that occur in the measured Lorentz force signals and are caused by the flaws. The force signals are obtained using the nondestructive testing method Lorentz force eddy current testing (LET). In this electromagnetic technique, a permanent magnet and the material under investigation move relative to each other. As a consequence eddy currents are induced in the conductor. The eddy currents in turn interact with the magnetic field and cause a Lorentz force. Inverse calculations in LFE require a forward solution of the measured force signals, which incorporates a model of the LET setup. The objective of this thesis is the development and evaluation of forward and inverse calculation methods for LFE. The proposed methods are assessed using Lorentz force data obtained from laminated composites. In order to model the permanent magnet in the forward solution for LFE the magnetic dipoles model (MDM) is introduced. In the MDM, a permanent magnet is represented by an assembly of magnetic dipoles. An optimization procedure is used to determine optimal dipole positions. Contrary to analytic models the MDM can be applied to permanent magnets of arbitrary geometry, and forward calculations can be performed with analytic mathematics. For defect reconstruction three inverse methods are introduced in this thesis. In the first method, conductivity reconstructions are performed using a stochastic optimization algorithm, the Differential Evolution (DE). Prior to inverse calculations, the intrinsic control parameters of the DE are determined based on parameter studies. As the second inverse strategy, current density reconstructions (CDR) calculated with minimum norm estimates (MNE) are employed. This approach is based on interpreting a defect in the forward solution for LFE as a distributed current source. In the third method, a goal function scan is performed to reconstruct the geometry parameters of the defect. All three inverse methods are suitable for reconstructing defects, whereas the first and third method provide more accurate results than the second. Further, measured Lorentz force signals obtained from glass laminate aluminum reinforced epoxy (GLARE) composite are investigated. GLARE is widely used in the aircraft industry. The flaw detectability of LET and LFE for GLARE is proved.Die Klassifizierung von Materialdefekten ist ein wesentliches Merkmal der Qualitätssicherung. Dabei sind geschichtete Verbundwerkstoffe von besonderem Interesse, weil sie zunehmend monolithische Werkstoffe ersetzen. Lorentz force evaluation (LFE) ist eine Methode zur Rekonstruktion der Geometrie von Fehlstellen in elektrisch leitfähigen Verbundwerkstoffen mittels inverser Berechnungen. Die Grundlage der inversen Berechnungen sind Störungen, die aufgrund der Fehlstellen in den gemessenen Lorentzkraft-Signalen auftreten. Die Signale werden mittels der zerstörungsfreien Prüfmethode, der Lorentzkraft-Wirbelstromprüfung (LET) gemessen. Bei diesem elektromagnetischen Testverfahren bewegen sich ein Permanentmagnet und das zu untersuchende Material relativ zu einander. Dadurch werden Wirbelströme im Material induziert. Die Interaktion dieser mit dem Magnetfeld hat eine Lorentzkraft zur Folge. Für inverse Verfahren ist eine Vorwärtslösung zur Berechnung der Lorentzkraft notwendig, der ein Modell des LET-Aufbaus zugrunde liegt. Das Ziel der vorliegenden Dissertation ist die Entwicklung und Evaluierung von Vor-wärtslösungen und inversen Berechnungsmethoden für LFE. Zur Bewertung der Methoden werden Lorentzkraft Signale verwendet, die aus Messungen von geschichteten Verbundmaterialien stammen. Zur Modellierung des Permanentmagneten in der Vorwärtslösung für LFE wird das Magnetische-Dipole-Modell (MDM) entwickelt. In diesem Modell wird ein Permanentmagnet durch eine Verteilung magnetischer Dipole repräsentiert. Die Positionen der magnetischen Dipole werden optimiert. Im Vergleich zu analytischen Modellen kann das MDM zur Modellierung beliebig geformter Permanentmagneten verwendet werden. Die Lorentzkraft-Signale können analytisch berechnet werden. In dieser Dissertation werden drei inverse Berechnungsmethoden für LFE erarbeitet. In der ersten Methode wird ein stochastischer Optimierungsalgorithmus, der Differential Evolution, zur Rekonstruktion von Leitfähigkeiten im Material verwendet. Die intrinsischen Kontrollparameter des Differential Evolution (DE) werden anhand von Parameterstudien festgelegt. Als zweite inverse Methode werden Stromdichterekonstruktionen mittels Minimum-Norm-Schätzungen durchgeführt. Grundlegend für diesen Ansatz ist die Interpretation eines Defektes in der Vorwärtslösung als verteilte Stromquelle. Als dritte inverse Methode wird eine Abtastung der Zielfunktion zur Rekonstruktion der Defektparameter vorgenommen. Alle inversen Verfahren sind zur Defektrekonstruktion geeignet, wobei sich die Ergebnisse der ersten und dritten Methode genauer darstellen als die der zweiten. Des Weiteren werden Messdaten eines aus glasfaserverstärktem Aluminium (GLARE) bestehenden Prüfkörpers ausgewertet. GLARE wird insbesondere im Flugzeugbau eingesetzt. Es wird gezeigt, dass mit LET und LFE Materialfehler in GLARE nachgewiesen werden können

Korrelation der tumorassoziierten Lymphozyten und Makrophagen mit der Überlebensrate von Ovarialkarzinom-Patienten

Epitheliales Ovarialkarzinom zählt zu den gefährlichsten, da meist letal verlaufenden Krebsarten bei Frauen in westlichen Ländern. Das Ziel der Studie ist es einen Zusammenhang zwischen tumorinfiltrierenden Lymphozyten (TIL), tumorassoziierten Makrophagen (TAM)und der Überlebensrate bei an Ovarialkarzinom erkrankten Patientinnen nachzuweisen. Klinische und pathologische Informationen stammen von Patientinnen des UKSH. Insgesamt werden 100 Patientenfälle ausgewertet. Für das immunhistochemische Verfahren werden Schnitte aus Paraffinblöcken angefertigt und mit folgenden Antikörpern behandelt: CD3+, CD8+, CD25+, FOXP3+, CD68+, und CD163+. Zu erwarten ist ein möglicher Zusammenhang zwischen dem gehäuften Auftreten von TIL und der verbesserten Überlebensrate der erkrankten Personen. Ein Nachweis wäre für weiterführende Therapiemaßnahmen von Bedeutung, da so dem Immunsystem eine wichtige regulatorische Rolle bei der Behandlung von Ovarialkarzinomen nachgewiesen werden könnte. Im Gegensatz zu dem positiven Einfluss der tumorinfiltrierenden Lymphozyten, vermutet man, dass die tumorassoziierten Makrophagen einen negativen Einfluss auf den Krankheitsverlauf haben, da man bei ihnen eine immunsupprimierende Rolle vermutet. Ziele der Studie sind es die Präsens von TIL (CD3+,CD8+, CD25+, FOXP3+) und TAM (CD68+, CD168+) nachzuweisen, die Interaktion zwischen TIL und TAM zu bestimmen, und eine Korrelation zwischen der Überlebensrate und der TIL-bzw. TAM Häufigkeit aufzuzeigen

Reinforcement learning in local energy markets

Local energy markets (LEMs) are well suited to address the challenges of the European energy transition movement. They incite investments in renewable energy sources (RES), can improve the integration of RES into the energy system, and empower local communities. However, as electricity is a low involvement good, residential households have neither the expertise nor do they want to put in the time and effort to trade themselves on their own on short-term LEMs. Thus, machine learning algorithms are proposed to take over the bidding for households under realistic market information. We simulate a LEM on a 15 min merit-order market mechanism and deploy reinforcement learning as strategic learning for the agents. In a multi-agent simulation of 100 households including PV, micro-cogeneration, and demand shifting appliances, we show how participants in a LEM can achieve a self-sufficiency of up to 30% with trading and 41,4% with trading and demand response (DR) through an installation of only 5kWp PV panels in 45% of the households under affordable energy prices. A sensitivity analysis shows how the results differ according to the share of renewable generation and degree of demand flexibility

Disfluency as a Desirable Difficulty—The Effects of Letter Deletion on Monitoring and Performance

Desirable difficulties initiate learning processes that foster performance. Such a desirable difficulty is generation, e.g., filling in deleted letters in a deleted letter text. Likewise, letter deletion is a manipulation of processing fluency: A deleted letter text is more difficult to process than an intact text. Disfluency theory also supposes that disfluency initiates analytic processes and thus, improves performance. However, performance is often not affected but, rather, monitoring is affected. The aim of this study is to propose a specification of the effects of disfluency as a desirable difficulty: We suppose that mentally filling in deleted letters activates analytic monitoring but not necessarily analytic cognitive processing and improved performance. Moreover, once activated, analytic monitoring should remain for succeeding fluent text. To test our assumptions, half of the students (n = 32) first learned with a disfluent (deleted letter) text and then with a fluent (intact) text. Results show no differences in monitoring between the disfluent and the fluent text. This supports our assumption that disfluency activates analytic monitoring that remains for succeeding fluent text. When the other half of the students (n = 33) first learned with a fluent and then with a disfluent text, differences in monitoring between the disfluent and the fluent text were found. Performance was significantly affected by fluency but in favor of the fluent texts, and hence, disfluency did not activate analytic cognitive processing. Thus, difficulties can foster analytic monitoring that remains for succeeding fluent text, but they do not necessarily improve performance. Further research is required to investigate how analytic monitoring can lead to improved cognitive processing and performance

A comprehensive modelling framework for demand side flexibility in smart grids

The increasing share of renewable energy generation in the electricity system comes with significant challenges, such as the volatility of renewable energy sources. To tackle those challenges, demand side management is a frequently mentioned remedy. However, measures of demand side management need a high level of exibility to be successful. Although extensive research exists that describes, models and optimises various processes with exible electrical demands, there is no unified notation. Additionally, most descriptions are very process-specific and cannot be generalised. In this paper, we develop a comprehensive modelling framework to mathematically describe demand side exibility in smart grids while integrating a majority of constraints from different existing models. We provide a universally applicable modelling framework for demand side exibility and evaluate its practicality by looking at how well Mixed-Integer Linear Program (MIP) solvers are able to optimise the resulting models, if applied to artificially generated instances. From the evaluation, we derive that our model improves the performance of previous models while integrating additional exibility characteristics

A Block-Free Distributed Ledger for P2P Energy Trading:Case with IOTA?

& #x00A9; 2019, Springer Nature Switzerland AG. Across the world, the organisation and operation of the electricity markets is quickly changing, moving towards decentralised, distributed, renewables-based generation with real-time data exchange-based solutions. In order to support this change, blockchain-based distributed ledgers have been proposed for implementation of peer-to-peer energy trading platform. However, blockchain solutions suffer from scalability problems as well as from delays in transaction confirmation. This paper explores the feasibility of using IOTA’s DAG-based block-free distributed ledger for implementation of energy trading platforms. Our agent-based simulation research demonstrates that an IOTA-like DAG-based solution could overcome the constraints that blockchains face in the energy market. However, to be usable for peer-to-peer energy trading, even DAG-based platforms need to consider specificities of energy trading markets (such as structured trading periods and assured confirmation of transactions for every completed period)