96 research outputs found

    Beam scanning by liquid-crystal biasing in a modified SIW structure

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    A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

    Subsystem-Based Methods for Global and Local Optical Properties

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    The accurate calculation of optical properties in complex systems is a challenging task due to the quantum-mechanical nature of optical processes and the computational demands involved. Subsystem-based methods offer a promising approach to tackle these challenges by partitioning the full system into smaller, computationally more efficient subsystems while maintaining the necessary accuracy. In this thesis, two distinct subsystem-based approaches for calculating optical properties are investigated: local embedding methods and global fragmentation schemes. In the local embedding methods, different polarizable embedding schemes were employed to calculate optical excitations on solvated para-nitroaniline and pentameric formyl thiophene acetic acid. By dissecting the individual interaction effects in a common theoretical framework and developing an extensive computational setup, a one-to-one comparison between polarizable embedding and frozen-density embedding was performed. This comparison provided valuable insights into the importance of separate interaction effects and highlighted the strengths and limitations of each approach. The results revealed the major significance of mutual ground-state polarization in the embedding schemes and the partial importance of dynamical environment effects. Specifically, a strong dependence on the underlying structural geometries could be observed. The global fragmentation schemes allow the calculation of optical properties for various zeolitic imidazolate frameworks (ZIFs). In an extensive study, a general computational protocol was established in order to obtain geometries and calculate refractive indices for ZIF-8, yielding accurate results with reduced computational demands. The implemented fragmentation schemes were then extended to predict optical properties for ZIFs with the same topology but substituted organic linkers. The results indicated the high potential of this approach for the rationalization and prediction of optical properties in empty ZIFs. Moreover, the framework was adapted to incorporate guest molecules into the porous structure of the ZIFs, providing rough estimates of the number of molecules incorporated per pore. Overall, this thesis introduces novel theoretical and computational frameworks for the accurate calculation of optical properties in complex systems. The presented approaches offer a promising direction for future research in the field of optical properties, enabling a deeper understanding of the interactions and their effects on the optical response of various molecules. This paves the way for an improved characterization and design of advanced materials with precise optical functionalities

    1-D broadside-radiating leaky-wave antenna based on a numerically synthesized impedance surface

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    A newly-developed deterministic numerical technique for the automated design of metasurface antennas is applied here for the first time to the design of a 1-D printed Leaky-Wave Antenna (LWA) for broadside radiation. The surface impedance synthesis process does not require any a priori knowledge on the impedance pattern, and starts from a mask constraint on the desired far-field and practical bounds on the unit cell impedance values. The designed reactance surface for broadside radiation exhibits a non conventional patterning; this highlights the merit of using an automated design process for a design well known to be challenging for analytical methods. The antenna is physically implemented with an array of metal strips with varying gap widths and simulation results show very good agreement with the predicted performance

    Optimization and Mathematical Modelling for Path Planning of Co-operative Intra-logistics Automated Vehicles

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    Small indoor Autonomous Vehicles have revolutionized the operation of pick-pack-and-ship warehouses. The challenges for path planning and co-operation in this domain stem from uncontrolled environments including workspaces shared with humans and human-operated vehicles. Solutions are needed which scale up to the largest existing sites with thousands of vehicles and beyond. These challenges might be familiar to anyone modelling road traffic control with the introduction of Autonomous Vehicles, but key differences in the level of decision autonomy lead to different approaches to conflict-resolution. This thesis proposes a decomposition of site-wide conflict-free motion planning into individual shortest paths though a roadmap representing the free space across the site, zone-based speed optimization to resolve conflicts in the vicinity of one intersection and individual path optimization for local obstacles. In numerical tests the individual path optimization based on clothoid basis functions created paths traversable by different vehicle configurations (steering rate limit, lateral acceleration limit and wheelbase) only by choosing an appropriate maximum longitudinal speed. Using two clothoid segments per convex region was sufficient to reach any goal, and the problem could be solved reliably and quickly with sequential quadratic programming due to the approximate graph method used to determine a good sequence of obstacle-free regions to the local goal. A design for zone-based intersection management, obtained by minimizing a linear objective subject to quadratic constraints was refined by the addition of a messaging interface compatible with the path adaptations based on clothoids. A new approximation of the differential constraints was evaluated in a multi-agent simulation of an elementary intersection layout. The proposed FIFO ordering heuristic converted the problem into a linear program. Interior point methods either found a solution quickly or showed that the problem was infeasible, unlike a quadratic constraint formulation with ordering flexibility. Subsequent tests on more complex multi-lane intersection geometries showed the quadratic constraint formulation converged to significantly better solutions than FIFO at the cost of longer and unpredictable search time. Both effects were magnified as the number of vehicles increased. To properly address site-wide conflict-free motion planning, it is essential that the local solutions are compatible with each other at the zone boundaries. The intersection management design was refined with new boundary constraints to ensure compatibility and smooth transitions without the need for a backup system. In numerical tests it was found that the additional boundary constraints were sufficient to ensure smooth transitions on an idealized map including two intersections

    Measuring knowledge sharing processes through social network analysis within construction organisations

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    The construction industry is a knowledge intensive and information dependent industry. Organisations risk losing valuable knowledge, when the employees leave them. Therefore, construction organisations need to nurture opportunities to disseminate knowledge through strengthening knowledge-sharing networks. This study aimed at evaluating the formal and informal knowledge sharing methods in social networks within Australian construction organisations and identifying how knowledge sharing could be improved. Data were collected from two estimating teams in two case studies. The collected data through semi-structured interviews were analysed using UCINET, a Social Network Analysis (SNA) tool, and SNA measures. The findings revealed that one case study consisted of influencers, while the other demonstrated an optimal knowledge sharing structure in both formal and informal knowledge sharing methods. Social networks could vary based on the organisation as well as the individuals’ behaviour. Identifying networks with specific issues and taking steps to strengthen networks will enable to achieve optimum knowledge sharing processes. This research offers knowledge sharing good practices for construction organisations to optimise their knowledge sharing processes

    Machine Learning assisted Digital Twin for event identification in electrical power system

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    The challenges of stable operation in the electrical power system are increasing with the infrastructure shifting of the power grid from the centralized energy supply with fossil fuels towards sustainable energy generation. The predominantly RES plants, due to the non-linear electronic switch, have brought harmonic oscillations into the power grid. These changes lead to difficulties in stable operation, reduction of outages and management of variations in electric power systems. The emergence of the Digital Twin in the power system brings the opportunity to overcome these challenges. Digital Twin is a digital information model that accurately represents the state of every asset in a physical system. It can be used not only to monitor the operation states with actionable insights of physical components to drive optimized operation but also to generate abundant data by simulation according to the guidance on design limits of physical systems. The work addresses the topic of the origin of the Digital Twin concept and how it can be utilized in the optimization of power grid operation.Die Herausforderungen für den zuverfässigen Betrieb des elektrischen Energiesystems werden mit der Umwandlung der Infrastruktur in Stromnetz von der zentralen Energieversorgung mit fossilen Brennstoffen hin zu der regenerativen Energieeinspeisung stetig zugenommen. Der Ausbau der erneuerbaren Energien im Zuge der klimapolitischen Zielsetzung zur CO²-Reduzierung und des Ausstiegs aus der Kernenergie wird in Deutschland zügig vorangetrieben. Aufgrund der nichtlinearen elektronischen Schaltanlagen werden die aus EE-Anlagen hervorgegangenen Oberschwingungen in das Stromnetz eingebracht, was nicht nur die Komplexität des Stromnetzes erhöht, sondern auch die Stabilität des Systems beeinflusst. Diese Entwicklungen erschweren den stabilen Betrieb, die Verringerung der Ausfälle und das Management der Netzschwankungen im elektrischen Energiesystem. Das Auftauchen von Digital Twin bringt die Gelegenheit zur Behebung dieser Herausforderung. Digital Twin ist ein digitales Informationsmodell, das den Zustand des physikalischen genau abbildet. Es kann nicht nur zur Überwachung der Betriebszustände mit nachvollziehbarem Einsichten über physischen Komponenten sondern auch zur Generierung der Daten durch Simulationen unter der Berücksichtigung der Auslegungsgrenze verwendet werden. Diesbezüglich widmet sich die Arbeit zunächste der Fragestellung, woher das Digital Twin Konzept stammt und wie das Digitan Twin für die Optimierung des Stromnetzes eingesetzt wird. Hierfür werden die Perspektiven über die dynamische Zustandsschätzung, die Überwachung des des Betriebszustands, die Erkennung der Anomalien usw. im Stromnetz mit Digital Twin spezifiziert. Dementsprechend wird die Umsetzung dieser Applikationen auf dem Lebenszyklus-Management basiert. Im Rahmen des Lebenszyklusschemas von Digital Twin sind drei wesentliche Verfahren von der Modellierung des Digital Twins zur deren Applizierung erforderlich: Parametrierungsprozess für die Modellierung des Digital Twins, Datengenerierung mit Digital Twin Simulation und Anwendung mit Machine Learning Algorithmus für die Erkennung der Anomalie. Die Validierung der Zuverlässigkeit der Parametrierung für Digital Twin und der Eventserkennung erfolgt mittels numerischer Fallstudien. Dazu werden die Algorithmen für Online und Offline zur Parametrierung des Digital Twins untersucht. Im Rahmen dieser Arbeit wird das auf CIGRÉ basierende Referenznetz zur Abbildung des Digital Twin hinsichtlich der Referenzmessdaten parametriert. So sind neben der Synchronmaschine und Umrichter basierende Einspeisung sowie Erreger und Turbine auch regler von Umrichter für den Parametrierungsprozess berücksichtigt. Nach der Validierung des Digital Twins werden die zahlreichen Simulationen zur Datengenerierung durchgeführt. Jedes Event wird mittels der Daten vo Digital Twin mit einem "Fingerprint" erfasst. Das Training des Machine Learning Algorithmus wird dazu mit den simulierten Daten von Digital Twin abgewickelt. Das Erkennungsergebnis wird durch die Fallstudien validiert und bewertet

    System- and Data-Driven Methods and Algorithms

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    An increasing complexity of models used to predict real-world systems leads to the need for algorithms to replace complex models with far simpler ones, while preserving the accuracy of the predictions. This two-volume handbook covers methods as well as applications. This first volume focuses on real-time control theory, data assimilation, real-time visualization, high-dimensional state spaces and interaction of different reduction techniques

    Computational Modelling of Concrete and Concrete Structures

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    Computational Modelling of Concrete and Concrete Structures contains the contributions to the EURO-C 2022 conference (Vienna, Austria, 23-26 May 2022). The papers review and discuss research advancements and assess the applicability and robustness of methods and models for the analysis and design of concrete, fibre-reinforced and prestressed concrete structures, as well as masonry structures. Recent developments include methods of machine learning, novel discretisation methods, probabilistic models, and consideration of a growing number of micro-structural aspects in multi-scale and multi-physics settings. In addition, trends towards the material scale with new fibres and 3D printable concretes, and life-cycle oriented models for ageing and durability of existing and new concrete infrastructure are clearly visible. Overall computational robustness of numerical predictions and mathematical rigour have further increased, accompanied by careful model validation based on respective experimental programmes. The book will serve as an important reference for both academics and professionals, stimulating new research directions in the field of computational modelling of concrete and its application to the analysis of concrete structures. EURO-C 2022 is the eighth edition of the EURO-C conference series after Innsbruck 1994, Bad Gastein 1998, St. Johann im Pongau 2003, Mayrhofen 2006, Schladming 2010, St. Anton am Arlberg 2014, and Bad Hofgastein 2018. The overarching focus of the conferences is on computational methods and numerical models for the analysis of concrete and concrete structures
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