Denna avhandling är fokuserad på modellering av elkraftsystemkomponenter och deras representation vid simuleringar av elkraftsystem. Avhandlingen jämför olika linjäriseringstekniker. Dessa tekniker är såväl numeriska som analytiska och används vid linjärisering av ett dynamiskt system. Efter en linjärisering är det möjligt att beräkna egenvärdena av det linjäriserade systemet samt använda andra verktyg ämnade för studier av linjära system. I avhandlingen visas hur olika linjäriseringtekniker influerar egenvärdesberäkningen av det linjära systemet. I avhandlingen tas fram bandviddsreducerade linjära modeller av en kraftsystemkomponent med hjälp av två tekniker. Senare görs simuleringar med de linjära modellerna tillsammans med ett introducerat gränssnitt. Den studerade kraftsystemkomponenten är en tyristorstyrd seriekondensator (TCSC). En fördel med att använda en linjär representation av en kraftsystemkomponent är att det förenklar simuleringarna. Storleken på komplexiteten av en simulering vid lösandet av ekvationerna minskar och den konsumerade fysiska tiden att simulera minskar. En nackdel med en linjär modell är att dess giltighet kan vara begränsad. Behovet av att bygga linjära modeller av kraftsystemkomponenter torde även finnas i framtiden. Med dagens horisont (år 2006) finns behov av att bygga linjära modeller utgående från detaljerade modeller av bl a högspända likströmslänkar (HVDC-länkar), reaktiva effektkompensatorer (SVC) samt tyristorstyrda seriekondensatorer (TCSC). Hur skall dessa representeras när vi vill studera dynamiken av ett helt kraftsystem och det då är nödvändigt att reducera deras komplexitet? Denna frågeställning uppkommer när vi vill genomföra tidsdomänsimuleringar på en inte alltför detaljerad nivå av de individuella kraftsystemkomponenterna eller när vi vill linjärisera kraftsystemet för att studera dess stabilitet med hjälp av småsignalanalys. / This thesis is focused in modelling of power system components and their representation in simulations of power systems. The thesis compares different linearization techniques. These techniques are both numerical as well as analytical and are utilized when linearization of a dynamic system is desired. After a linearization it is possible to calculate the eigenvalues of the linearized system as well as to perform other applicable activities on a linear system. In the thesis it is shown how the linearization techniques influence the calculation of eigenvalues of the linear system. In the thesis bandwidth-reduced linear models of a power system component are developed using two techniques. The simulations with the linear models are done with an introduced interface system. The studied power system component is a Thyristor-Controlled Series Capacitor (TCSC). One advantage with using a linear representation of a power system component is that it simplifies the simulations. The size of the complexity of a simulation when solving the equations decreases and the consumed physical time to simulate becomes shorter. A disadvantage of a linear model is that its validity might be limited. The need of building linear models of power systems will continue to attract interest in the future. With the horizon of today (year 2006) there is a need of among other models to build linear models of detailed models of High Voltage Direct Current-links (HVDC-links), Static Var Compensators (SVCs), as well as Thyristor-Controlled Series Capacitors (TCSCs). How should these be represented when we want to study the dynamics of a whole power system and it is necessary to reduce their complexity? This question rises when we want to perform time-domain simulations with a not too detailed level of complexity of each individual power system component or if we want to linearize the power system and study it within small-signal stability analysis. / QC 20100915

<p>As the interest in intentional electromagnetic interference (IEMI) increases, so does the need of a suitable antenna which endures those demanding conditions. The ultrawideband (UWB) technology provides an elegant way of generating high-voltage UWB pulses which can be used for IEMI. One UWB antenna, invented solely for the purpose of radiating pulses, is the impulse radiating antenna (IRA). In the course of this master thesis work, a suitable geometry of the IRA is suggested, and modelled, for the high-voltage application of 90 kV.</p>

As the interest in intentional electromagnetic interference (IEMI) increases, so does the need of a suitable antenna which endures those demanding conditions. The ultrawideband (UWB) technology provides an elegant way of generating high-voltage UWB pulses which can be used for IEMI. One UWB antenna, invented solely for the purpose of radiating pulses, is the impulse radiating antenna (IRA). In the course of this master thesis work, a suitable geometry of the IRA is suggested, and modelled, for the high-voltage application of 90 kV.

<p>The numerical simulation of physical systems plays a key role in different fields of science and engineering. The popularity of numerical methods stems from their ability to simulate complex physical phenomena for which analytical solutions are only possible for limited combinations of geometry, boundary, and initial conditions. Despite their flexibility, the computational demand of classical numerical methods quickly escalates as the size and complexity of the model increase. To address this limitation, and motivated by the unprecedented success of Deep Learning (DL) in computer vision, researchers started exploring the possibility of developing computationally efficient DL-based algorithms to simulate the response of complex systems. To date, DL techniques have been shown to be effective in simulating certain physical systems. However, their practical application faces an important common constraint: trained DL models are limited to a predefined set of configurations. Any change to the system configuration (e.g., changes to the domain size or boundary conditions) entails updating the underlying architecture and retraining the model. It follows that existing DL-based simulation approaches lack the flexibility offered by classical numerical methods. An important constraint that severely hinders the widespread application of these approaches to the simulation of physical systems.</p> <p><br></p> <p>In an effort to address this limitation, this dissertation explores DL models capable of combining the conceptual flexibility typical of a numerical approach for structural analysis, the finite element method, with the remarkable computational efficiency of trained neural networks. Specifically, this dissertation introduces the novel concept of <em>“Finite Element Network Analysis”</em> (FENA), a physics-informed, DL-based computational framework for the simulation of physical systems. FENA leverages the unique transfer knowledge property of bidirectional recurrent neural networks to provide a uniquely powerful and flexible computing platform. In FENA, each class of physical systems (for example, structural elements such as beams and plates) is represented by a set of surrogate DL-based models. All classes of surrogate models are pre-trained and available in a library, analogous to the finite element method, alleviating the need for repeated retraining. Another remarkable characteristic of FENA is the ability to simulate assemblies built by combining pre-trained networks that serve as surrogate models of different components of physical systems, a functionality that is key to modeling multicomponent physical systems. The ability to assemble pre-trained network models, dubbed <em>network concatenation</em>, places FENA in a new category of DL-based computational platforms because, unlike existing DL-based techniques, FENA does not require <em>ad hoc</em> training for problem-specific conditions.</p> <p><br></p> <p>While FENA is highly general in nature, this work focuses primarily on the development of linear and nonlinear static simulation capabilities of a variety of fundamental structural elements as a benchmark to demonstrate FENA's capabilities. Specifically, FENA is applied to linear elastic rods, slender beams, and thin plates. Then, the concept of concatenation is utilized to simulate multicomponent structures composed of beams and plate assemblies (stiffened panels). The capacity of FENA to model nonlinear systems is also shown by further applying it to nonlinear problems consisting in the simulation of geometrically nonlinear elastic beams and plastic deformation of aluminum beams, an extension that became possible thanks to the flexibility of FENA and the intrinsic nonlinearity of neural networks. The application of FENA to time-transient simulations is also presented, providing the foundation for linear time-transient simulations of homogeneous and inhomogeneous systems. Specifically, the concepts of Super Finite Network Element (SFNE) and network concatenation in time are introduced. The proposed concepts enable training SFNEs based on data available in a limited time frame and then using the trained SFNEs to simulate the system evolution beyond the initial time window characteristic of the training dataset. To showcase the effectiveness and versatility of the introduced concepts, they are applied to the transient simulation of homogeneous rods and inhomogeneous beams. In each case, the framework is validated by direct comparison against the solutions available from analytical methods or traditional finite element analysis. Results indicate that FENA can provide highly accurate solutions, with relative errors below 2 % for the cases presented in this work and a clear computational advantage over traditional numerical solution methods. </p> <p><br></p> <p>The consistency of the performance across diverse problem settings substantiates the adaptability and versatility of FENA. It is expected that, although the framework is illustrated and numerically validated only for selected classes of structures, the framework could potentially be extended to a broad spectrum of structural and multiphysics applications relevant to computational science.</p>

Osteoporose und gestörte Heilungsverläufe von Knochenbrüchen verursachen immer noch beachtliche klinische Komplikationen. Ein vielversprechender Ansatz für die nichtinvasive und nichtionisierende Abschätzung des Frakturrisikos und der Bildgebung von Frakturheilung ist quantitativer Ultraschall (QUS). Dennoch liegt die derzeitige Akzeptanz für die Knochenqualitätsabschätzung noch weit hinter herkömmlichen röntgenbasierten Anwendungen. Es wurden akustische Mikroskopie und Synchrotronstrahlen-Mikrotomographie für die Anatomie und altersabhängige Erfassung von strukturellen und elastischen Variationen auf der mikroskopischen Ebene von humanen Femora verwendet. Die gewonnenen Daten dienten als Grundlage für die Erstellung mikromechanischer Modelle von Knochen für numerische Simulationen der Schallausbreitung im humanen Femurhals. Dabei wurde der Aufbau eines US-basierten Femur-Scanners in transversaler Transmission (TT) nachempfunden. Im letzten Abschnitt der Arbeit wurde QUS in TT in in vitro Experimenten am Rattenfrakturmodell auf eine Anwendung für die Bildgebung der Frakturheilung getestet. Die Studien konnten zeigen, dass ein Großteil der adaptiven Fähigkeiten von Knochen auf mikroskopischer Ebene auf eine Kombination von extrazellulärer Matrixelastizität und Gewebeporosität zurückzuführen ist. Die Simulationen des zweiten Teils konnten die Existenz von geführten Wellen im humanen Femurhals bestätigen. Die sensitive Abhängigkeit von US-parametern von frakturrelevanten Knocheneigenschaften zeigt das hohe Potential von QUS für die Frakturrisikoabschätzung. Der zweite Teil der Arbeit konnte erfolgreich die Möglichkeit von QUS in TT zur Diskriminierung von zeitigen Heilungsstadien demonstrieren. Zusammenfassend bestätigt die Studie das hohe Potential von QUS für die Frakturrisikoabschätzung und die Bildgebung der Frakturheilung. / Osteoporosis and impaired bone healing are of high relevance. A promising non-invasive, non-ionizing candidate for fracture risk prediction and monitoring fracture healing is quantitative ultrasound (QUS). However, the acceptance of QUS for bone quality assessment is still not comparable to X-ray based methods. Scanning acoustic microscopy (SAM) and Synchrotron Radiation micro-computer tomography (SRµCT) has been used to investigate anatomical and age dependent variations of micro elastic, structural and mineralization parameters at the tissue level of human femoral bone. Femoral neck models were created based on these data for numerical sound propagation simulations emulating a transverse transmission (TT) setup of an in vivo QUS prototype. In the last part of the project the TT approach has been tested in ex vivo experiments in a rat healing model. The power of QUS, to discriminate two early healing stages has been compared to µCT measurements at the same specimens. It was found that the major contributor to bone adaptation is related to a combination of extracellular matrix elasticity and tissue porosity. It is hypothesized that these parameters are likely to have a considerable impact on the reliability of in silico models. The simulations of the second part confirmed the existence of guided wave propagation in the cortical shell and a high dependency of US parameters on fracture relevant bone properties. The results demonstrate the high potential for bone fracture risk prediction at the femoral neck using QUS. Finally, it was successfully demonstrated that early healing stage discrimination of QUS in TT was superior compared to µCT. In summary these investigations not only show the importance for a precise estimation of micro mechanical properties for numerical modelling but also demonstrate the feasibility and high potential of QUS for bone quality assessment and monitoring of fracture healing.