Дисертації з теми "Complex mdof structural systems"

Nonlinear response history analysis is accepted as the most accurate analytical tool for seismic response determination. However, accurate estimation of displacement responses using conceptually simple, approximate analysis procedures is preferable, since there are shortcomings in the application of nonlinear response history analysis resulting from its complexity. An equivalent linearization procedure, which utilizes the familiar response spectrum analysis as the analysis tool and benefits from the capacity principles, is developed in this thesis study as an approximate method for predicting the inelastic seismic displacement response of MDOF systems under earthquake excitations. The procedure mainly consists of the construction of an equivalent linear system by reducing the stiffness of structural members which are expected to respond in the inelastic range. Different from similar studies in literature, equivalent damping is not explicitly employed in this study. Instead, predetermined spectral displacement demands are utilized in each mode of the equivalent linear system for the determination of global displacement demands. Response predictions of the equivalent linearization procedure are comparatively evaluated by using the benchmark nonlinear response history analysis results and other approximate methods including conventional pushover analysis and modal pushover analysis (MPA). It is observed that the proposed procedure results in similar accuracy with approximate methods which employ nonlinear analysis. Considering the conceptual simplicity of the procedure and the conventional analysis tools used in its application, presented equivalent linearization procedure can be suggested as a practically applicable method for the prediction of inelastic seismic displacement response parameters with sufficient accuracy.

Thesis (Ph. D.)--University of Akron, Dept. of Chemistry, 2006.
"December, 2006." Title from electronic dissertation title page (viewed 04/24/2008). Advisor, Chrys Wesdemiotis; Committee members, Matthew P. Espe, Jun Hu, Wiley J. Youngs, Frank W. Harris; Department Chair, Kim C. Calvo; Dean of the College, Ronald F. Levant; Dean of the Graduate School, George R. Newkome. Includes bibliographical references.

With the rapid development in today&rsquo
s technology, reliability and performance requirements on components of various mechanical systems, which tend to be much lighter and work under much more severe working conditions, dramatically increased. In general, analysis techniques based on simplified model of structural components with linearity assumption may provide time saving for solutions with reasonable accuracy. However, since most engineering structures are often very complex and intrinsically nonlinear, in some cases they may behave in a different manner which cannot be fully described by linear mathematical models, or linear treatments may not be applicable at all. In fact, some studies revealed that deviations in the modal properties of dynamic structures gathered from measured data are due to nonlinearities in the structure. Hence, in problems where accuracy is the primary concern, taking the nonlinear effects into account becomes inevitable. In this thesis, it is aimed to analyze the harmonic response characteristics of multi degree of freedom nonlinear structures having different type of nonlinearities. The amplitude dependencies of nonlinearities are modelled by using describing function method. To increase the accuracy of the results, effect of the higher order harmonic terms will be considered by using multi harmonic describing function theory. Mathematical formulations are embedded in a computer program developed in MATLAB®
with graphical user interface. The program gets the system matricies from the file which is obtained by using substructuring analysis in ANSYS®
, and nonlinearities in the system can easily be defined through the graphical user interface of the MATLAB®
program.

This thesis aims at developing new methodologies for the reliability analysis of structural systems with applications to offshore and aeronautical fields. In general, sructures of practical interest are complex redundant systems, in which more than one element is required to fail in order to have catastrophic failure. Moreover, ramdomness inherently exists in both material properties and external loads. As a result, complex structural systems are typically characterised by a huge number of possible failure sequences, of which only some are most likely to occour. Therefore, for an efficient risk analysis, only the dominant failure modes need to be considered, so as to minimise the number of failure paths as well as the computational costs associated to their enumeration and evaluation. However, although several techniques have been developed for the identification of the critical failure sequences, these methods are still either time-demanding or prone to miss potential failure modes. These challenges motivated the first part of the thesis, in which the merits of a risk assessment framework recently developed for truss and frame structures are here investigated in view of its extensive application to the offshore field. To this end, the case study of a jacket-type platform under an extreme sea state is considered. First, the dominant failure modes of the structure are rapidly identified by a multi-point parallel search employing a genetic algorithm. Then, a multi-scale system reliability analysis is performed, in which the statistical dependence among both structural elements and failure modes is fully considered through simple matrix operations. Finally, the accuracy and the efficiency of the proposed approach are successfully validated against crude Monte Carlo simulation. In the second part of the thesis, system reliability theory is applied to the uncertainty quantification of the longitudinal tensile strength of UniDirectional (UD) composites, a structural component very common in aircraft structures. Predictive models for size effects in this class of materials are paramount for scaling small-coupon experimental results to the design of large composite structures. In this respect, a Monte Carlo progressive failure analysis is proposed to calculate the strength distributions of hierarchical fibre bundles, which are formed by grouping a predefined number of smaller-order bundles into a larger-order one. The present approach is firstly validated against a recent analytical model to be later applied to more complex load-sharing configurations. The resulting distributions are finally used to analyse the damage accumulation process and the formation of clusters of broken fibres during progressive failure.
Lo scopo principale di questa tesi è lo sviluppo di nuove metodologie per determinare l’affidabilità dei sistemi strutturali con applicazioni sia in campo offshore che aeronautico. In generale, strutture di interesse pratico sono caratterizzate da un elevato grado di ridondanza, per cui il collasso globale richiede la rottura simulatanea e/o progressiva di più elementi. Inoltre, i sistemi fisici sono influenzati da diverse fonti di incertezza, quali le prorietà dei materiali e le condizioni ambientali e operative. Pertanto, il collasso strutturale può avvenire con diverse modalità (modi di guasto), di cui solo alcune possiedono una probabilità di accadimento significativa (modi di guasto dominanti). Per una valutazione efficiente del rischio risulta dunque indispensabile limitare l’analisi ai soli modi dominanti, così da ridurre il costo computazionale associato alle fasi di identificazione e di valutazione dei modi stessi. Tuttavia, nonostante in letteratura vi siano numerose soluzioni per l’analisi del rischio, tali metodi richiedono ancora tempi di calcolo notevoli e sono inclini a tralasciare potenziali modi di guasto. Queste motivazioni conducono alla prima parte delle tesi, in cui si ripropone un metodo recentemente sviluppato per l’analisi del rischio di strutture discrete (reticolari e telai) in previsione di una sua applicazione al campo offshore. A tale scopo si considera il caso di studio di una piattaforma di tipo jacket in condizioni di mare estremo. Dapprima, i modi di guasto dominanti vengono rapidamente identificati per mezzo di un algoritmo genetico. In seguito, l’affidabilità del sistema viene calcolata mediante un approccio multi-scala che fa uso di semplici operazioni matriciali, in cui la dipendenza statistica viene considerata sia tra le componenti strutturali che tra i modi di guasto dominanti. Infine, l’accuratezza e l’efficienza del metodo vengono testate con successo tramite comparazione con Monte Carlo. Nella seconda parte della tesi, la teoria dell’affidabilità dei sistemi viene applicata per la quantificazione dell’incertezza nella resistenza a trazione di compositi UniDirezionali (UD), problema di notevole interesse per l’ambito aeronautico e non solo. Infatti, il comportamento aletorio di questi materiali è fortemente influenzato da effetti di scala, che limitano la progettazione di strutture in composito di grandi dimensioni sulla base dei dati sperimentali ricavati da provini. In quest’ottica, si propone di modellare fasci di fibre secondo una legge di scala gerarchica, ossia raggruppando un numero prestabilito di fasci più piccoli in un fascio di ordine superiore. La distribuzione di resistenza di tali fasci viene quindi simulata attraverso un’analisi di collasso progressivo. Questo approccio, dapprima validato rispetto ad un modello analitico recentemente sviluppato per disposizioni semplici di fasci, viene poi esteso a configurazioni più realistiche. I risultati così ottenuti sono infine processati per l’analisi statistica del danno.

Complex networks is a vibrant research field and has received much attention over the last decade. Central to this area is the question of how networks around us are constructed. The essential notion of network research is that these systems are assembled in a decentralised way, thus no central agent is planning the network beforehand. Despite this lack of central coordination, many networks present intriguing universalities, such as broad degree distributions, in the form of power-laws. The subject of study in this thesis is a class of networks that are constructed by a node intrinsic variable, called fitness. The way these networks grow could be called a rich-get-richer mechanism. The fitter a node is, the more likely it is to acquire new connections inside the network. Several aspects that are directly connected to these networks are explored in this thesis. In the first part, the properties of growing networks that are driven by fitness are investigated and it is shown that the introduction of growth leads to a topological structure that is different from its static counterpart. In the subsequent chapter, percolation on fitness driven networks is studied. The results give insights into possible mechanisms that can stabilise systems. Furthermore, the theory can be used to identify vulnerable structures around us. In the following chapter, the world trade network is discussed. This numerical investigation highlights possible improvements to the methodology to make statistical analysis more robust. That chapter is followed by an analysis of time-varying networks. Time-varying networks represent an interesting construct that allows a formulation of stochastic processes on the same time-scale as the evolution of the network itself. This possibility is highly relevant to the investigation of epidemics, for instance. In the last chapter, a study of a system of clusters and their self-organised formation is presented.

In educational and organisational settings it has become common practice to use computer-based complex problems that represent dynamic systems for assessment and training purposes. In the interpretation of performance scores and the design of training programs, it is often assumed that the capacity to effectively control the outcomes of a dynamic system depends on the acquisition of structural knowledge. Control performance scores are generally interpreted as evidence of individual differences in the capacity to acquire and utilise structural knowledge and training programs typically try to improve learners‘ mental models of the system of interest. However, a causal relationship between the acquisition of structural knowledge and successful system control has not been established, and some findings suggest that it may be possible to control dynamic systems in the absence of structural knowledge. Therefore, the goals of this project were to determine the conditions that are required to learn how to control dynamic systems and the psychological processes that separate successful from less successful problem solvers in the performance of this task. The main emphasis of this investigation was to clarify the role of structural knowledge in the control of dynamic systems and to identify sources of individual differences in problem solvers‘ capacity to acquire such knowledge and apply it in a goal-orientated application. In a series of studies, a combined experimental and differential approach was adopted to address these goals. This consisted of the experimental manipulation of the task and structural characteristics of complex problems combined with the use of process indicators and external psychometric tests. Study 1 examined whether problem solvers need to directly interact with a dynamic system in order to acquire structural knowledge that is useful for system control. Study 2 examined whether increments in structural knowledge lead to improvements in control performance and whether dynamic systems can be successfully controlled without structural knowledge. Study 3 examined whether the relationship between structural knowledge and control performance is moderated by system complexity. Each of these studies also investigated the role of fluid intelligence in the acquisition and application of knowledge. Additional methodological contributions include the application of Cognitive Load Theory to the design of the instructions used to manipulate structural knowledge, the use of randomly generated control performance scores to evaluate the success of performance and the development of a theoretically driven operationalisation of system complexity. Across the studies, it was found that structural knowledge was a necessary condition of better than random performance and that there was a causal relationship between structural knowledge and control performance. However, the likelihood that structural knowledge would be acquired and utilised was found to be dependent on the complexity of the system. Small increments in system complexity resulted in floor effects on performance. Fluid intelligence was found to play a crucial role in the acquisition and subsequent application of knowledge. Overall, the results indicate that the complexity of the system determines the amount of knowledge that is acquired by the problem solver, which in turn, combined with their intelligence, determines the quality of their control performance.

The main performance objective in an electric power grid entails timely and efficient generation and delivery to the time-varying electricity demand. As the electricity industry is witnessing proliferation of the mainstream renewables, the minute-by-minute variations in wind and solar power generation may result in temporary electricity scarcity that jeopardizes grid stability and quality of service. The evolving electricity markets are aimed at incentivizing the conventional generators to reinforce their operating flexibility. This dissertation concerns the goal of enhancing the dynamic response rates of interconnected controllable resources by means of a multi-layered fuel input control of electrically coupled heterogeneous energy conversion components. Both power engineering and large-scale control contributions are made in support of this enhancement. First, improved fuel input controls are designed to enable flexible physics-based energy conversion dynamics required by the interconnected grid. To efficiently utilize the resources load-following and regulation problems are stated. The efficacy of proposed fuel input control designs in enhancing the dynamic response rates is illustrated on IEEE 14-bus system. Second, the problem is formalized as multi-input multioutput time-varying trajectory tracking based on a decentralized spatiotemporal composite control design. The concepts of vector-Lyapunov function and singular perturbation are invoked to formalize model decompositions, over space and time, respectively. Next, the assumptions for model simplifications are relaxed and the problem of parametric uncertainty is addressed. A minimumcost resilient co-design approach is introduced for storage-sensors-communication channels in a complex electric power grid. The notion of selective strong structural fixed modes is explored as a characterization of feasible decentralized control laws for an arbitrary system realization satisfying a pre-specified structure. Finally, it is proposed that planning of generation portfolio must be driven by the objective of maintaining adequate operating flexibility in the system. The goal is to ensure sufficient ramp capacity to sustain the significant integration of intermittent renewable resources.

Experimental nonlinear dynamics is an important area of study in the modern engineering field, with engineering applications in structural dynamics, structural control, and structural health monitoring. As a result, the discipline has experienced a great influx of research efforts to develop a versatile and reliable experimental methodology. A technical challenge in many experimental studies is the procurement of a device that exhibits the desired nonlinear behavior. As a result, many researchers have longed for a versatile, but accurate, testing methodology that has complete freedom to simulate a wide range of nonlinearities and stochastic behaviors. The objective of this study is to develop a reconfigurable test setup as a tool to be used in a wide range of nonlinear dynamic studies. The main components include a moving mass whose restoring force can accurately be controlled and reprogrammed (with software) based upon measured displacement and velocity readings at each time step. The device offers control over nonlinear characteristics and the equation of dynamic motion. The advantage of having such an experimental setup is the ability to simulate various types of nonlinearities with the same test setup. As a result, the data collected can be used to help validate nonlinear modeling, system identification, and stochastic analysis studies. A physical test apparatus was developed, and various mechanical, electrical, and programming calibrations were performed for reliable experimental studies. To display potential uses for the reconfigurable approach, examples are presented where the device has been used to create physical data for use in change detection and deterioration studies. In addition, a demonstration is presented of the device's ability to physically simulate a large-scale orifice viscous damper, devices commonly used for vibration mitigation in bridges and buildings.; For a large-scale viscous damper, physical testing is required to ensure structural design properties. However, due to the large scale of the dampers, expensive dynamic loading tests can be carried out at a very limited number of facilities. Using the reconfigurable test setup, the dynamic signature of the large-scale viscous damper can accurately be simulated with pre-collected data. The development of a system capable of emulating the restoring force of a nonlinear device with software is a novel approach and requires further calibration for increased reliability and accuracy. A discussion regarding the challenges faced when developing the methodology is presented and possible solutions are recommended. The methodology introduced by this apparatus is very promising. The device is a valuable experimental tool for researchers and designers, allowing for physical data collection, modeling, analysis, and validation of a wide class of nonlinear phenomena that commonly occur in a wide variety of engineering applications.
ID: 030423494; System requirements: World Wide Web browser and PDF reader.; Mode of access: World Wide Web.; Thesis (M.S.)--University of Central Florida, 2011.; Includes bibliographical references (p. 132-137).
M.S.
Masters
Civil, Environmental and Construction Engineering
Engineering and Computer Science

Netzwerke sind überall, von der elektrischen Stromversorgung über die Biochemie der Zellen, das Internet bis hin zu sozialen Netzen. Netzwerke als mathematisches Konzept haben sich in den letzten Jahren zu einem wichtigen Werkzeug der Beschreibung komplexer Systeme entwickelt. Ihre grundlegende Eigenschaft ist, dass sie aus einer grö{ss}en Anzahl dynamischer Elemente bestehen, die sich gegenseitig beeinflussen und dabei nicht linear gekoppelt sind. Die moderne Netzwerkwissenschaft will die Wechselwirkung zwischen den einzelnen Untereinheiten erklären und davon ausgehend verständlich machen, auf welche Weise Prozesse auf einem Netzwerk stattfinden können. Zum Beispiel wird untersucht, wie die Struktur sozialer Netze die Ausbreitung von Information oder von Krankheiten beeinflusst, wie die Topologie des World Wide Web das Surf-Verhalten oder die Funktionalität von Suchmaschinen beeinträchtigt oder welche Auswirkungen die Hierarchie in ökologischen Nischen auf die Populationsdynamik der einzelnen Spezies hat. Darüber hinaus gilt es herauszufinden, welche grundlegenden Prinzipien der Evolution realer Netzwerke zugrunde liegen, das heißt nach welchen Regeln sich einerseits die Untereinheiten entwickeln und welchen Einfluss andererseits deren Vernetzung hat. Die vorliegende Dissertation beschäftigt sich sowohl mit der Topologie verschiedener Netzwerke als auch mit den der Evolution zugrunde liegenden Prinzipien. Schwerpunkte liegen dabei auf den folgenden zwei Aspekten: erstens dem Einfluss von so gennanten ``vertex-pair correlations'''', das heißt Korrelationen zwischen den Untereinheiten, auf die Topologie und zweitens der Auswirkung der Geographie auf die Netzwerkentwicklung. Es wird der bedeutende Einfluss aufgezeigt, den die Korrelationen auf wichtige statistische Größen der Netzwerke haben. Weiterhin analysieren wir die Perkolationseigenschaften, die Aufschluss über die Empfindlichkeit gegenüber Störungen in der Vernetzung geben. Damit können zum Beispiel Fragen aus der Epidemiologie diskutiert werden. Es zeigt sich, dass die Topologie vieler Netzwerke und ihre Perkolationseigenschaften deutlich von Korrelationen beeinflusst werden. Schließlich untersuchen wir im letzten Teil dieser Arbeit, wie die Einbettung von Netzwerken in eine endlich-dimensionale Geographie auf die Modellierung und Entwicklung Web-ähnlicher Systeme Einfluss nimmt.
Networks are all around us, from electrical power grids to the biochemistry of cells, from the Internet to social webs. The mathematical concept of network has recently been turned into an important tool for describing complex systems, whose principal characteristic is that they consist of a large number of mutually interacting dynamical parts which are coupled in a nonlinear fashion. Modern network science attempts to explain the structure of interactions between the subunits of a system in order to understand their functioning and the processes taking place in them. It tries, for instance, to grasp how the structure of social networks affects the spread of information or human diseases, how the structure of the World Wide Web influences the search engines and surfing behavior, or how the hierarchy of ecological niches affects population dynamics. Beyond this, the ultimate goal of network science is to discover what generating principles exist behind the evolution of real systems. It tries to find the fundamental principles under which the subunits evolve, and the wiring of interactions. This thesis centres both on the study of the topological structure of networks and the analysis of the underlying principles responsible for their evolution. More specifically, it concentrates on the following aspects: the influence of vertex-pair correlations on network topology, the network percolation problem, which is closely related to the spreading of epidemics and the robustness of networks, and the effects of geography as a generating element. We show that important topological and percolation properties change considerably when modifying the connection probabilities between vertices, and that geography as well plays a crucial role in the modeling of evolving real web-like systems.

In der vorliegenden Arbeit werden zwei Enzymsysteme – Ni-haltige Kohlenmonoxid-Dehydrogenase (CODH) und [NiFe]-haltige Hydrogenase – strukturell untersucht. Im 1. Teil werden die Untersuchungen des ACDS-Komplexes aus A. fulgidus mittels Transmissionselektronenmikroskopie (Negativkontrastierung und der Kryo-Einbettung) geschildert. Die 3D-Rekonstruktion mit einer Auflösung von 29 Å wird de novo ermittelt und drei mögliche Positionen für die CODH-Untereinheit vorgeschlagen. Im 2. Teil wird die Röntgenkristallstrukturanalyse der CODH-Untereinheit des ACDS Komplexes aus A. fulgidus geschildert. Das Protein besteht aus α- und ε-Untereinheiten, die zusammen eine α2ε2-Stöchiometrie bilden (Afα2ε). Während die Gesamtstruktur von Afα2ε2 jener von M. barkeri (Mbα2ε2) ähnelt, führt der Austausch der koordinierenden Cys zu Asp und Glu zu einer Deletion des verbrückenden FeS-Zentrums. Die Rolle der ε-Untereinheit wird durch kinetische Studien untersucht. Die CO-abhängige FAD-Reduktionsaktivität von Afα2ε2 folgt einer Michaelis-Menten Kinetik. Die Mbα2ε2 hat ein ähnliches Kinetikverhalten. Im Gegensatz dazu weist die CODH-II von C. hydrogenoformans, die keine ε-Untereinheit hat, eine lineare Abhängigkeit der CO-abhängigen FAD-Reduktionsaktivität von Flavin auf. Diese Beobachtungen sind im Einklang mit der Annahme, dass die ε-Untereinheit ein Gerüst für die Flavinbindung bereitstellt. Der 3. Teil ist der F420-reduzierenden Hydrogenase aus M. barkeri (MbFRH) gewidmet. Die Struktur von MbFRH wird mittels Röntgenkristallographie bestimmt und ergibt eine dodekamerische Anordnung von ca. 1.2 MDa. Zusammen mit der etablierten Elektronenübertragungskette, beobachtet in FRH aus M. marburgensis, wird in MbFRH auch ein [2Fe2S]-Cluster und eine Fe-Stelle detektiert. Schließlich führen die schwingungsspektroskopischen Analysen zusammen mit der Röntgenkristallographie zu dem Schluss, dass MbFRH in einem bisher strukturell nicht charakterisierten, katalytisch aktiven Nia-S Zustand isoliert wird.
In this work, we structurally characterize two metal-based enzyme systems from archaea: Ni-containing CO dehydrogenase (CODH) and [NiFe] containing hydrogenase. In the first chapter we investigate, using transmission electron microscopy, the ACDS complex from A. fulgidus (AfACDS). The purified ACDS complex can be visualized as an intact globular protein particle by negative stain and vitrification techniques. The 3D reconstruction is determined de novo to 29 Å-resolution by single-particle analysis. We suggest three possible positions for the CODH subunit within ACDS by rigid-body fitting. In the second chapter we determine the X-ray crystal structure of the CODH subunit. The 220 kDa protein is composed of α- and ε-subunits that form a heterodimer with (α2ε2) stoichiometry (Afα2ε2). While the overall structure of Afα2ε2 resembles the previously reported structure of the α2ε2-subunit from M. barkeri (Mbα2ε2), the naturally-occurring exchange of the Cys to Asp and Glu results in a depletion of the bridging iron-sulfur cluster. The role of the ε-subunit is investigated by kinetics studies. CO-dependent FAD reduction activity of Afα2ε2 exhibits Michaelis-Menten type kinetics. The same kinetic type is demonstrated for the Mbα2ε2-subunit. In contrast, the ε-subunit lacking CODH-II from C. hydrogenoformans shows linear dependency between CO-dependent FAD reduction activity and flavin concentration. The data suggests that the ε-subunit provides a scaffold for the flavin binding. In the third chapter we study the F420-reducing hydrogenase from M. barkeri (MbFRH). Its structure is solved by X-ray crystallography, revealing a dodecameric arrangement of 1.2 MDa. Along with the established ET chain observed in FRH from M. marburgensis, one solvent-exposed [2Fe2S] cluster and an additional Fe metal site are detected. The combined approach of X-ray crystallography and vibrational spectroscopy reveals that MbFRH is isolated in the previously structurally uncharacterized Nia-S state.

This dissertation explores the ways in which the everyday (in)securities of people in southwestern Ukraine can illuminate our understanding of contemporary political life. Rather than using traditional units of analysis or given categories—the state, the individual, identity—the dissertation focuses on relations between people in and connected to a single village to develop a novel framework for analyzing politics and the political. The dissertation opens with an interrogation of the practical and theoretical challenges associated with current conceptualizations of security; our understanding of the political; and the role of ethnography in theorization and presents a research design meant to address those challenges. Drawing upon extensive participant-observation and other immersion-based research in a post-Soviet borderland wedged between Ukraine and Slovakia, and using an analytical tool I call “togetherness,” the thesis presents an ethnographic account of social interactions, economy, and authority in this largely Hungarian-speaking rural area. The third part of the dissertation applies the idea of an ontological shift and draws on complex systems and structuration theory (Luhmann and Giddens, respectively) to rethink the ethnographic analysis and to highlight relationships between structural and existential realms of political life. Here, the concept of security becomes central to the theorization, and the overall argument illuminates the intimate relationship between the idea of security and the political. Ultimately, this approach allows us to expand the scope of political ethnography: theorizing beyond thick description; integrating broader perspectives without losing the texture of the local; and developing an approach to research that can be replicated in other settings.

Deux systèmes biologiques distincts, les muscles squelettiques et les sites d'adhésion de cellules kératocytes en mouvement, sont considérés dans un même cadre en raison de la similitude profonde de leur structure et de leur fonctionnalité. La réponse passive de l'un et de l'autre peut être modélisée à l'aide d'un grand nombre d'unités multi-stables couplées par des interactions à longue portée, et exposées à un désordre spatial fixé et un bruit thermique/mécanique. Les interactions à longue portée dans de tels systèmes conduisent à une synchronisation malgré les fluctuations temporelles et spatiales. Bien que les deux systèmes biologiques considérés présentent des différences structurelles importantes, nous montrons que l'on peut identifier une structure de verre de spin sous-jacente commune. À la lumière de cette analogie, ces systèmes vivants semblent être proches de points critiques et, à cet égard, le désordre gelé, reflétant l’incommensurabilité stérique des unités parallèles, peut être fonctionnel. Un autre paramètre important fixant la réponse est la rigidité interne du système qui couple les unités entre elles
Two biological systems, a half-sarcomere of a skeletal muscle and an adhesive cluster of a crawling keratocyte, are considered in parallel because of the deep similarity in their structure and functionality. Their passive response can be modeled by a large number of multi-stable units coupled through long-range interactions, frustrated by quenched disorder and exposed to thermal noise. In such systems, long-range interactions lead to synchronization, defying temporal and spatial fluctuations. We use a mean-field description to obtain analytic results and elucidate the remarkable ensemble-dependence of the mechanical behavior of such systems in the thermodynamic limit. Despite important structural differences between muscle cross-bridges and adhesive binders, one can identify a common underlying spin glass structure, which we fully exploit in this work. Our study suggests that the muscle machinery is fine-tuned to operate near criticality, and we argue that in this respect the quenched disorder, reflecting here steric incommensuration, may be functional. We use the analogy between cell detachment and thermal fracture of disordered solids to study the statistics of fluctuations during cellular adhesion. We relate the obtained results to recent observations of intermittent behavior involved in cell debonding, also suggesting near-criticality. In addition to the study of the equilibrium properties of adhesive clusters, we also present the first results on their kinetic behavior in the presence of time-dependent loading

Orientador: Renato Pavanello
Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia mecanica
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Resumo: Microsistemas eletromecânicos geralmente têm seu princípio de funcionamento baseado na interação entre dois ou mais campos físicos. Para seu projeto são necessárias ferramentas de simulação multi-domínios. Este trabalho visa estudar o fenômeno de acoplamento eletromecânico em microsistemas e construir uma ferramenta de simulação numérica para este tipo de problema. São apresentados métodos de análise estática, modal e transiente baseados em modelos de elementos finitos e de ordem reduzida. Na análise estática cada domínio é resolvido separadamente. Foi mostrado um método de transferência das forças eletrostáticas para o domínio mecânico e proposto e testado um esquema de atualização da malha elétrica. Para a análise dinâmica transiente foi implementado um método de Newmark adaptado de forma a considerar os efeitos do acoplamento eletromecânico. Outro método de análise dinâmica apresentado é baseado numa estratégia de perturbação do sistema em equilíbrio em conjunto com a resolução de um problema de autovalor / autovetor. O método de perturbação fornece diretamente as freqüências naturais de vibração do sistema acoplado. A técnica de redução de ordem apresentada é baseada numa projeção de Galerkin da equação diferencial governante utilizando como funções de base os modos empíricos do sistema dinâmico. Um programa computacional para modelagem numérica multi-domínios com solução particionada para o acoplamento eletromecânico foi proposto e implementado. O código computacional, denominado MefLab, onde os métodos numéricos foram programados, usa o paradigma de orientação a objetos e a linguagem C++. Resultados com as diversas metodologias são mostrados e analisados
Abstract: Microelectromechanical systems have their working principIes based on the interaction between two or more physical fields. To design them, multi-physics simulation tools are needed. This work aims to study the coupled field effects in microsystems and build a computer code for numerical simulation of this problem. Static, dynamic modal and transient methods are introduced. They are based on finite elements and reduced order models. The static analysis is done by a staggered treatment. A method for transfering electrostatic pressures to the mechanical domain was shown and a mesh updating scheme was proposed and tested. For the transient analysis, in order to consider the electromechanical coupling effects, an adaptation was inserted in the classical Newmark direct integration method. A dynamical modal method based in a perturbation strategy was presented. It involves the staggered static algorithm and the solution of an eigenvalue/eigenvector problem. This method is able to give the natural coupled frequencies of the system with low computational costs. A reduced order modeI was constructed by using a Galekin projection of the governing differential equations in an empirical basis. This basis was obtained through results of transient finite element analysis. A multi-domain project with staggered procedures for the electromechanical coupling was proposed and implemented. It refers to the software MefLab++, a computer code written in C++ where numerical strategies are programmed according to the oriented object paradigm. Numerical results for the static, dynamic modal and transient methodologies are shown
Doutorado
Mecanica dos Sólidos e Projeto Mecanico
Doutor em Engenharia Mecânica

Ce travail de thèse porte sur le développement de méthodes de spectrométrie de masse (MS) structurale pour la caractérisation de systèmes protéiques complexes, souvent réfractaires aux approches biophysiques classiques. Dans ce contexte, les développements entrepris furent notamment focalisés sur la caractérisation de complexes impliqués dans la biogénèse des ribosomes et dans la régulation transcriptionnelle, fonctions cellulaires essentielles pouvant être liées à de nombreuses pathologies humaines dont certains cancers. Ainsi, les approches par MS native, pontage chimique et d’HDX-MS ont permis de renseigner sur la connectivité, les proximités spatiales ou encore la dynamique conformationnelle retrouvées au sein des complexes étudiés. Parmi ces techniques, l’HDX-MS permet une approche comparative basée sur les mesures d’incorporations en deutérium renseignant sur la dynamique conformationnelle d’une protéine sous différents états. Aussi, la combinaison d’approches de MS structurale a permis d’approfondir la caractérisation des systèmes complexes étudiés, démontrant ainsi l’intérêt d’une approche intégrative dans ce contexte
This PhD thesis focuses on developing methods in structural mass spectrometry (MS) to characterize complex protein systems, given their size and their heterogeneity, frequently inaccessible by classical biophysic approaches. In this context, methodological developments have particularly focused on the characterization of protein complexes involved in ribosomes biogenesis and transcriptional regulation. These fundamental cellular processes are related to numerous diseases such as cancers and genetic diseases. Thus native MS, crosslink, and hydrogen/deuterium exchange coupled to MS (HDX-MS) allowed gaining insights about the stoechiometry, spatial proximities and conformational dynamics of studied systems. Among these approaches, HDX-MS enables a comparative approach based on deuterium incorporation measurements giving information about the conformational dynamics of labeled proteins in various experimental conditions. Finally, the combination of structural approaches enables to deeply characterize complex protein systems, highlighting the advantages of an integrative approach in this context

Tesi presentata e approvata dalla Facoltà di Architettura, Ingegneria Civile e Scienze Ambientali Technische Universität Braunschweig e dal Dipartimento di Ingegneria Civile e Ambientale dell'Università degli Studi di Firenze per candidarsi al titolo di Doktor-Ingenieur (Dr.-Ing.) / Dottore di Ricerca in Ingegneria Civile e Ambientale*) *) Può essere utilizzata la forma tedesca o italiana del titolo. Dissertation submitted to and approved by the Faculty of Architecture, Civil Engineering and Environmental Sciences Technische Universität Braunschweig and the Department of Civil and Environmental Engineering University of Florence in candidacy for the degree of a Doktor-Ingenieur (Dr.-Ing.) / Dottore di Ricerca in Civil and Environmental Engineering*) *) Either the German or the Italian form of the title may be used.

Abstract This research work details a systematic study of the structure and function of supramolecular host-guest systems. Host-guest inclusion complexes were formed between β-Cyclodextrin (β-CD) and its copolymers (as hosts), with several types of guest molecules both in aqueous solution and the solid state. The research is divided into two themes; (1) structural characterization and dynamic properties of the inclusion compounds of β-CD with various guest systems in aqueous solution and the solid phase, and (2) heterogeneous adsorption and structural studies of β-CD based copolymers with various guest systems in aqueous solutions. The guest systems include alkyl and perfluoroalkyl carboxylates, perfluoroalkyl sulfonate, and p-nitrophenol (PNP) at variable experimental conditions. In the first theme (chapter 2-5), host-guest complexes in the solid state were prepared using dissolution and slow cool methods at variable host/guest mole ratios (i.e., 1:1 and 2:1). The complexes were further characterized using 19F/13C DP/MAS and CP/MAS solid-state NMR spectroscopy. The solution state complexes were prepared in D2O for structural characterization using 1H/19F NMR spectroscopy. The NMR studies were complemented using FT-IR, thermal analyses (DSC, and TGA), and powder X-ray diffraction (PXRD). Evidence for the formation of host-guest inclusion compounds (ICs) was provided using CP/MAS solids NMR spectroscopy and complexation-induced chemical shift (CIS) values of 1H/19F nuclei in aqueous solution. The β-CD/PFC ICs displayed variable guest geometry and hydration states as determined by the host-guest stoichiometry and the conformation of the guest. PFOA and SPFO form 1:1 and 2:1 ICs with β-CD, wherein the guest adopts a range of gauche and trans conformations, respectively. 1:1 host-guest complexes were concluded for short perfluorocarbon chains (i.e., PFBA) where the gauche conformation of the PFC guest in the bound state was favoured. In the second theme (chapters 6–8), β-CD based copolymers were used as host materials. The structural characterization of a soluble poly-CD material (known as HDI-1) revealed that the solution behaviour of such polymeric hosts are sensitive to the presence of guest compounds such as p-nitrophenol (PNP) (i.e. chemo-responsive), as well as temperature variations (i.e. thermo-responsive). The host-guest chemistry of the soluble poly-CD material, as studied by 2-D solution NMR and induced circular dichroism (ICD) spectroscopy, indicates that PNP was bound within the cavity sites of β-CD and the interstitial domains of the copolymer (cf. Scheme 1.6 and chapter 6). The observed responsive nature of such polymeric host materials to temperature variation and chemical potential resembles behaviour characteristic of ‘smart materials’. Herein, ‘smart materials’ refer to systems which are responsive to external stimuli (e.g. temperature and chemical). The adsorption properties of the soluble (HDI-1) and insoluble (HDI-3 and -6) poly-CD adsorbents with octyl and perfluorooctyl carboxylate and sulfonate anions were estimated using the Sips and BET models. The hydrocarbon (HC) and fluorocarbon (FC) anions form monolayer and multilayer structures at the surface of the polymeric adsorbents, respectively. The formation of layered structures was controlled by the relative hydrophobicity of the alkyl/perfluoroalkyl chains and their mutual miscibility with the adsorbent surface. Other factors include the inductive effects of the alkyl/perfluoroalkyl head groups and their interactions with aqueous solvent or dipolar domains of the adsorbent surface. The adsorbed species at the liquid-solid interface were characterized using FT-IR spectroscopy, thermal analyses, and contact angle.

Uncertainty propagation in engineering mechanics and dynamics is a highly challenging problem that requires development of analytical/numerical techniques for determining the stochastic response of complex engineering systems. In this regard, although Monte Carlo simulation (MCS) has been the most versatile technique for addressing the above problem, it can become computationally daunting when faced with high-dimensional systems or with computing very low probability events. Thus, there is a demand for pursuing more computationally efficient methodologies. Further, most structural systems are likely to exhibit nonlinear and time-varying behavior when subjected to extreme events such as severe earthquake, wind and sea wave excitations. In such cases, a reliable identification approach is behavior and for assessing its reliability. Current work addresses two research themes in the field of stochastic engineering dynamics related to the above challenges. In the first part of the dissertation, the recently developedWiener Path Integral (WPI) technique for determining the joint response probability density function (PDF) of nonlinear systems subject to Gaussian white noise excitation is generalized herein to account for non-white, non-Gaussian, and non-stationary excitation processes. Specifically, modeling the excitation process as the output of a filter equation with Gaussian white noise as its input, it is possible to define an augmented response vector process to be considered in the WPI solution technique. A significant advantage relates to the fact that the technique is still applicable even for arbitrary excitation power spectrum forms. In such cases, it is shown that the use of a filter approximation facilitates the implementation of the WPI technique in a straightforward manner, without compromising its accuracy necessarily. Further, in addition to dynamical systems subject to stochastic excitation, the technique can also account for a special class of engineering mechanics problems where the media properties are modeled as non-Gaussian and non-homogeneous stochastic fields. Several numerical examples pertaining to both single- and multi-degree-of freedom systems are considered, including a marine structural system exposed to flow-induced non-white excitation, as well as a beam with a non-Gaussian and non-homogeneous Young’s modulus. Comparisons with MCS data demonstrate the accuracy of the technique. In the second part of the dissertation, a novel multiple-input/single-output (MISO) system identification technique is developed for parameter identification of nonlinear time-variant multi-degree-of-freedom oscillators with fractional derivative terms subject to incomplete non-stationary data. The technique utilizes a representation of the nonlinear restoring forces as a set of parallel linear subsystems. In this regard, the oscillator is transformed into an equivalent MISO system in the wavelet domain. Next, a recently developed L1-norm minimization procedure based on compressive sampling theory is applied for determining the wavelet coefficients of the available incomplete non-stationary input-output (excitation-response) data. Finally, these wavelet coefficients are utilized to determine appropriately defined time- and frequency-dependent wavelet based frequency response functions and related oscillator parameters. A nonlinear time-variant system with fractional derivative elements is used as a numerical example to demonstrate the reliability of the technique even in cases of noise corrupted and incomplete data.

The thesis involves computer simulation and theoretical studies of dynamics of water under confinement and structural transformation in different complex systems. Based on the systems and phenomena of interest, the work has been classified in to three major parts: I. Dynamics of water under confinement II. Dynamics of water in presence of amphiphilic solutes III. Structural transformation in complex systems The three parts have further been divided into nine chapters. Brief chapter wise outline of the thesis is discussed below. Part I deals with the dynamics of water in confined systems. In Chapter I.1, we provide a brief introduction of water dynamics inc on fined systems. We also give a brief outline of relevant experimental and theoretical techniques used to study the water dynamics under confinement. Chapter I.2 describes a model based analytical study of dynamical correlation in confined systems. Here, we introduce a novel one dimensional Ising model to investigate the propagation and annihilation of dynamical correlations in confined systems and to understand the intriguing shortening of the orientational relaxation time that has been reported for small sized reverse micelles (RMs).In our model, the two spins located at the two end cells are oriented in the opposite directions to mimic the surface effects present in the real systems. These produce opposing polarizations which propagate from the surface to the center, thus producing bulk like condition at the center. This model can be solved analytically for short chains. For long chains, we solve the model numerically with Glauber spin flip dynamics (and also with Metropolis single-spin flip Monte Carlo algorithm).We show that the model satisfactorily reproduces many of the features observed in experiments. Due to the destructive interference among correlations that propagate from the surface to the core, one of the rotational relaxation time components decays faster than the bulk. In general, the relaxation of spins is non-exponential due to the interplay between various interactions. In the limit of strong coupling between the spins or in the limit of low temperature, the nature of the relaxation of spins undergoes a change with the emergence of homogeneous dynamics, where the decay is predominantly exponential. In Chapter I.3, layer-wise distance dependent orientation relaxation of water confined in reverse micelle s(RM)is studied using theoretical and computational tools. We use both a newly constructed spins on a ring (SOR) Ising-type model with modified Shore-Zwanzig rotational dynamics and atomistic simulations with explicit water. Our study explores the size effect of RMs and the role of intermolecular correlations, compromised by the presence of a highly polar surface, on the distance (from the surface) dependence of water relaxation. The SOR model can capture some aspects of distance dependent orientation relaxation, such as acceleration of orientation relaxation at intermediate layers. In atomistic simulations, layer-wise decomposition of hydrogen bond (H-bond) formation pattern clearly reveal that the H-bond arrangement of water at a certain distance away from the surface can remain frustrated due to interaction with the polar surface head groups. We show that this layer-wise analysis also reveals the presence of a non-monotonic, slow relaxation component which can be attributed to the frustration effect and is accentuated in small to intermediate size RMs. For larger RMs, the long-time component decreases monotonically from the interface to the interior of the RMs with slowest relaxation observed at the interface. In ChapterI.4, we present theoretical two dimensional infrared spectroscopic (2D-IR) studies of water confined within RMs of various sizes. Here we focus again mainly on the altered dynamics of confined water by performing a layer-wise decomposition of water. We aim to quantify the relative contributions to the calculated 2D-IR spectra by water molecules located in different layers. The spectra of 0-1 transition clearly show substantial elongation along the diagonal, due to in homogeneous broadening and incomplete spectral diffusion, in the surface water layer of different size of RMs studied in this work. Our study reveals that the motion of the surface water molecules is sub-diffusive, establishing the constrained nature of their dynamics. This is further supported by the two peak nature of the angular analogue of the van Hove correlation function. With increasing system size the motion of water molecules becomes more diffusive in nature and the structural diffusion is observed to be almost completed in the central layer of larger RMs. Comparisons between experiment and simulation help establishing the correspondence between the spectral decomposition available in experimental 2D-IR with the spatial decomposition of simulated 2D-IR. Simulations also allow a quantitative exploration of the relative role of water, sodium ions and sulfonate head groups in irrational dephasing. Interestingly, the negative cross correlation between forces on oxygen and hydrogen of O-H bond in bulk water significantly decreases in the surface layer of different RMs. This negative cross correlation gradually increases in the central layer with increasing size of the RMs and this is found to be partly responsible for the faster relaxation rate of water in the central layer. Part II consists of two chapters and focuses on the dynamics of water in presence of amphiphilic solutes. In Chapter II.1, we present a brief introduction of water – DMSO binary mixture and various anomalous properties of the same. In Chapter II.2, we present theoretical IR study of water dynamics in water–DMSO binary mixtures of different compositions. We show that with increasing DMSO concentration, the IR absorption peak maxima show the presence of structural transformation in similar concentration range, observed in earlier studies. Analysis of H-bonded network near hydrophilic and hydrophobic part of DMSO also suggests that average number of hydrogen bonds near the hydrophobic parts possess maxima at the same concentration range. We also show that with increasing DMSO concentration water dynamics becomes very slow. This has been supported by the diagonal elongation of the 2D-IR spectra and also the slow decay of frequency fluctuation correlation n function (FFCF) and the orientation time correlation function (OTCF). The decoupling of the OTCF establishes that water-DMSOH-bond is much stronger than that of water-water. The last part (Part III) consists of three chapters that deal with structural transformation in various complex systems. In Chapter III.1, we introduce polydisperse systems and present existing theoretical, computer simulation and experimental studies. It also contains the importance and diversity of polydisperse system in nature. In Chapter III.2, we present computer simulation study of melting of polydisperse Lennard-Jones (LJ) system with Gaussian polydispersity in size. The phase diagram reproduces the existence of an early temperature in variant terminal polydispersity (δt0.11), with no signature of re-entrant melting. The absence of re-entrant melting can be attributed to the influence of attractive part of the potential on melting. We find that at terminal polydispersity the fractional density change approaches zero that seems to arise from vanishingly small compressibility of the disordered phase. At constant temperature and volume fraction system undergoes a sharp transition from crystalline solid to disordered state with increasing polydispersity. This has been quantified by second and third order rotational invariant bond orientational orders as well as by the average inherent structure energy. The translational order parameter also indicates similar structural change The free energy calculation further supports the nature of the transition. The third order bond orientational order shows that with increasing polydispersity, local cluster favors more icosahedral-like arrangements and thus the system loses its crystalline symmetry. In Chapter III.3, we present study of phase transition and effect of confinement on it in SOR model. This system is similar to our SOR model discussed in Chapter I.3. The spins execute continuous rotation under a modified XY Hamiltonian. In order to understand the nature of phase transition in such confined spin systems we have performed extensive Monte Carlo simulations. The system size dependence of Binders cumulant, specific heat, order parameter and finite size scaling of order parameter universally suggest the existence of a phase transition. The absence of hysteresis and Scaling of Binders energy cumulant minimum confirm the continuous nature of the transition. The finite size scaling analyses give rise to the mean field nature of the transition. Plausible applications of the proposed model in modeling dipolar liquids in confined systems are also discussed. In Appendix A, we discuss a preliminary study of front propagation in a non-equilibrium system. The model system analogous to the super cooled liquid shows non-Avrami domain growth during rejuvenation. The origin of the non-Avrami nature of the domain growth and the presence of cross over are also discussed. In Appendix B, we discuss umbrella a sampling technique and WHAM analysis which is used in ChapterIII.2 to get the free energy of polydisperse LJ system.

碩士
中原大學
土木工程研究所
103
The objective of study is researching adjacent structures with attached dampers , this study discussed five-floor adjacent structures and twenty-floor adjacent structures as an example. Based on the free vibration theory, the complex modal analysis is presented to calculate the natural frequency, damped frequency, damping ratio, mode shape and the curve of damping ratio versus damper coefficient for each mode of the system. The optimal damping ratio with the corresponding damper coefficient and damper stiffness for each of the system can therefore be obtained. Under considerations of these optimal damper parameters, the forced vibration analysis is conducted to calculate the time histories of structural response under seismic excitation for four types of systems: without control, passive control, fuzzy sliding mode control and neural network control. The results can be used to assess the effectiveness of attached dampers, as well as to capture the dynamic characteristics of adjacent structures.

In many engineering applications, it is a formidable task to construct mathematical models that are expected to produce accurate predictions of the behavior of a system of interest. During the construction of such predictive models, errors due to imperfect modeling and uncertainties due to incomplete information about the system and its environment (e.g., input or excitation) always exist and can be accounted for appropriately by using probability logic. To assess the system performance subjected to dynamic excitations, a stochastic system analysis considering all the uncertainties involved has to be performed. In engineering, evaluating the robust failure probability (or its complement, robust reliability) of the system is a very important part of such stochastic system analysis. The word ‘robust’ is used because all uncertainties, including those due to modeling of the system, are taken into account during the system analysis, while the word ‘failure’ is used to refer to unacceptable behavior or unsatisfactory performance of the system output(s). Whenever possible, the system (or subsystem) output (or maybe input as well) should be measured to update models for the system so that a more robust evaluation of the system performance can be obtained. In this thesis, the focus is on stochastic system analysis, model and reliability updating of complex systems, with special attention to complex dynamic systems which can have high-dimensional uncertainties, which are known to be a very challenging problem. Here, full Bayesian model updating approach is adopted to provide a robust and rigorous framework for these applications due to its ability to characterize modeling uncertainties associated with the underlying system and to its exclusive foundation on the probability axioms.

First, model updating of a complex system which can have high-dimensional uncertainties within a stochastic system model class is considered. To solve the challenging computational problems, stochastic simulation methods, which are reliable and robust to problem complexity, are proposed. The Hybrid Monte Carlo method is investigated and it is shown how this method can be used to solve Bayesian model updating problems of complex dynamic systems involving high-dimensional uncertainties. New formulae for Markov Chain convergence assessment are derived. Advanced hybrid Markov Chain Monte Carlo simulation algorithms are also presented in the end.

Next, the problem of how to select the most plausible model class from a set of competing candidate model classes for the system and how to obtain robust predictions from these model classes rigorously, based on data, is considered. To tackle this problem, Bayesian model class selection and averaging may be used, which is based on the posterior probability of different candidate classes for a system. However, these require calculation of the evidence of the model class based on the system data, which requires the computation of a multi-dimensional integral involving the product of the likelihood and prior defined by the model class. Methods for solving the computationally challenging problem of evidence calculation are reviewed and new methods using posterior samples are presented.

Multiple stochastic model classes can be created even there is only one embedded deterministic model. These model classes can be viewed as a generalization of the stochastic models considered in Kalman filtering to include uncertainties in the parameters characterizing the stochastic models. State-of-the-art algorithms are used to solve the challenging computational problems resulting from these extended model classes. Bayesian model class selection is used to evaluate the posterior probability of an extended model classe and the original one to allow a data-based comparison. The problem of calculating robust system reliability is also addressed. The importance and effectiveness of the proposed method is illustrated with examples for robust reliability updating of structural systems. Another significance of this work is to show the sensitivity of the results of stochastic analysis, especially the robust system reliability, to how the uncertainties are handled, which is often ignored in past studies.

A model validation problem is then considered where a series of experiments are conducted that involve collecting data from successively more complex subsystems and these data are to be used to predict the response of a related more complex system. A novel methodology based on Bayesian updating of hierarchical stochastic system model classes using such experimental data is proposed for uncertainty quantification and propagation, model validation, and robust prediction of the response of the target system. Recently-developed stochastic simulation methods are used to solve the computational problems involved.

Finally, a novel approach based on stochastic simulation methods is developed using current system data, to update the robust failure probability of a dynamic system which will be subjected to future uncertain dynamic excitations. Another problem of interest is to calculate the robust failure probability of a dynamic system during the time when the system is subjected to dynamic excitation, based on real-time measurements of some output from the system (with or without corresponding input data) and allowing for modeling uncertainties; this generalizes Kalman filtering to uncertain nonlinear dynamic systems. For this purpose, a novel approach is introduced based on stochastic simulation methods to update the reliability of a nonlinear dynamic system, potentially in real time if the calculations can be performed fast enough.

abstract: This dissertation presents the development of structural health monitoring and prognostic health management methodologies for complex structures and systems in the field of mechanical engineering. To overcome various challenges historically associated with complex structures and systems such as complicated sensing mechanisms, noisy information, and large-size datasets, a hybrid monitoring framework comprising of solid mechanics concepts and data mining technologies is developed. In such a framework, the solid mechanics simulations provide additional intuitions to data mining techniques reducing the dependence of accuracy on the training set, while the data mining approaches fuse and interpret information from the targeted system enabling the capability for real-time monitoring with efficient computation. In the case of structural health monitoring, ultrasonic guided waves are utilized for damage identification and localization in complex composite structures. Signal processing and data mining techniques are integrated into the damage localization framework, and the converted wave modes, which are induced by the thickness variation due to the presence of delamination, are used as damage indicators. This framework has been validated through experiments and has shown sufficient accuracy in locating delamination in X-COR sandwich composites without the need of baseline information. Besides the localization of internal damage, the Gaussian process machine learning technique is integrated with finite element method as an online-offline prediction model to predict crack propagation with overloads under biaxial loading conditions; such a probabilistic prognosis model, with limited number of training examples, has shown increased accuracy over state-of-the-art techniques in predicting crack retardation behaviors induced by overloads. In the case of system level management, a monitoring framework built using a multivariate Gaussian model as basis is developed to evaluate the anomalous condition of commercial aircrafts. This method has been validated using commercial airline data and has shown high sensitivity to variations in aircraft dynamics and pilot operations. Moreover, this framework was also tested on simulated aircraft faults and its feasibility for real-time monitoring was demonstrated with sufficient computation efficiency. This research is expected to serve as a practical addition to the existing literature while possessing the potential to be adopted in realistic engineering applications.
Dissertation/Thesis
Doctoral Dissertation Mechanical Engineering 2019

Recent years have seen a concurrent development of new sensor technologies and high-fidelity modeling capabilities. At the junction of these two topics lies an interesting opportunity for real-time system monitoring and damage assessment of structures. During monitoring, measurements from a structure are used to learn the parameters and equations characterizing a physics-based model of the system; thus enabling damage identification. Since monitored quantities are physical, these methods offer precious insight into the damage state of the structure (localization, type of damage and its extent). Furthermore, one obtains a model of the structure in its current condition, an essential element in predicting the future behavior of the structure and enabling adequate decision-making procedures. This dissertation focuses more specifically on solving some of the challenges associated with the use of online Bayesian learning algorithms, also called sequential filtering algorithms, for damage detection and characterization in nonlinear structural systems. A major challenge regarding online Bayesian filtering algorithms lies in achieving good accuracy for large dimensional systems and complex nonlinear non-Gaussian systems, where non-Gaussianity can arise for instance in systems which are not globally identifiable. In the first part of this dissertation, we show that one can derive algorithmic enhancements of filtering techniques, mainly based on innovative ways to reduce the dimensionality of the problem at hand, and thus obtain a good trade-off between accuracy and computational complexity of the learning algorithms. For instance, for particle filtering techniques (sampling-based algorithms) subjected to the so-called curse of dimensionality, the concept of Rao-Blackwellisation can be used to greatly reduce the dimension of the sampling space. On the other hand, one can also build upon nonlinear Kalman filtering techniques, which are very computationally efficient, and expand their capabilities to non-Gaussian distributions. Another challenge associated with structural health monitoring is the amount of uncertainties and variabilities inherently present in the system, measurements and/or inputs. The second part of this dissertation aims at demonstrating that online Bayesian filtering algorithms are very well-suited for SHM applications due to their ability to accurately quantify and take into account these uncertainties in the learning process. First, these algorithms are well-suited to address ill-conditioned problems, where not all parameters can be learnt from the available noisy data, a problem which frequently arises when considering large dimensional nonlinear systems. Then, in the case of unknown stochastic inputs, a method is derived to take into account in this sequential filtering framework unmeasured stationary excitations whose spectral properties are known but uncertain.

The heart and microvasculature have characteristics of a complex adaptive system. Extreme challenges faced by these organ systems cause structural changes which lead to global adaptation. To assess the impact of myocardial interstitial edema on the mechanical properties of the left ventricle and the myocardial interstitium, we induced acute and chronic interstitial edema in dogs. With chronic edema, the primary form of collagen changed from type I to III and left ventricular chamber compliance significantly increased. The resulting functional adaptation allows the chronically edematous heart to maintain left ventricular chamber compliance when challenged with acute edema, thus, preserving cardiac function over a wide range of interstitial fluid pressures. To asses the effect of microvascular occlusions, we reintroduced the Pallid bat wing model and developed a novel mathematical model. We hypothesized that microvessels can switch from predominantly pressure-mediated to shear-mediated responses to ensure dilation during occlusions. Arterioles of unanesthetized Pallid bats were temporarily occluded upstream (n=8) and parallel (n=4) to vessels of interest (20-65 mm). In both cases, the vessels of interest rapidly dilated (36+24 %, 37+33 %), illustrating that they responded appropriately to either decreased pressure or increased shear stress. The model not only reproduced this switching behavior, but reveals its origin as the nonlinear shear-pressure-radius relationship. The properties of the heart and microvasculature were extended to characterize a “Research-Intensive Community” (RIC) model, to provide a feasible solution consistent with the Boyer Commission, to create a sustainable physiology research program. We developed and implemented the model with the aim of aligning diverse goals of participants while simultaneously optimizing research productivity. While the model radically increases the number of undergraduate students supported by a single faculty member, the inherent resilience and scalability of this complex adaptive system enables it to expand without formal institutionalization.

Among all structures, high-rise buildings pose specific design challenges with respect of fire safety for a number of reasons, in particular the evaluation of both the fire development (fire action) and response of the structural system to fire (structural behaviour). In relation to the fire action, large compartments and open hallways often present in modern high-rise buildings don’t let themselves to be designed within compliance to current codes and standards. A comprehensive analysis of the fire environment is required to understand the fire dynamics in these cases. A Computational Fluid Dynamic (CFD) model allows a quite accurate representation of realistic fire scenarios, because it takes into account the distribution of fuel, the geometry, the occupancy of individual compartments and the temperature rise in structural elements that are located outside the tributary area of fire scenario. In relation to the structural behaviour under fire, the passive fire resistance of structural elements and the intrinsic robustness of the system are the only measures to rely on in order to maintain the structural integrity of the building during and after the fire and avoid major economic losses due to structural failures and prolonged inoperability of the premises. Disproportionate damages induced by fire can be avoided with a proper design of the structure, aimed at reducing the vulnerability of the elements to fire (i.e. their sensitivity to fire) or at increasing the robustness of the structural system (i.e. its sensitivity to local damages). The topic of this thesis is the evaluation of the structural safety in case of fire by means of advanced multi-physics analyses with direct reference to the modern Performance-Based Fire Design (PBFD) framework. A fundamental aspect is how some basic failure mechanisms can be triggered or modified by the presence of fire on a part of a structural system, such as three hinge mechanism, bowing effects, catenary action, thermal buckling and snap-through, sway and non-sway collapse. High rise buildings, which are expected to be susceptible to fire-induced progressive collapse, will be investigated. Critical elements will be identified in the system and countermeasure for enhancement of structural integrity will be suggested. The investigation of the response of such a complex structures subjected to fire scenarios requires the use of CFD and Finite Element (FE) models for a realistic evaluation of the fire action and of the structural response respectively.

Adaptability and flexibility are some of the most important human characteristics. Learning based on new experiences enables adaptation by changing the structural connectivity of the brain through plasticity mechanisms. But the human brain can also adapt to new tasks and situations in a matter of milliseconds by dynamic coordination of functional activation. To understand how this flexibility can be achieved in the computations performed by neural networks, we have to understand how the relatively fixed structural backbone interacts with the functional dynamics. In this thesis, I will analyze these interactions between the structural network connectivity and functional activations and their dynamic interactions on different levels of abstraction and spatial and temporal scales. One of the big questions in neuroscience is how functional interactions in the brain can adapt instantly to different tasks while the brain structure remains almost static. To improve our knowledge of the neural mechanisms involved, I will first analyze how dynamics in functional brain activations can be simulated based on the structural brain connectivity obtained with diffusion tensor imaging. In particular, I will show that a dynamic model of functional connectivity in the human cortex is more predictive of empirically measured functional connectivity than a stationary model of functional dynamics. More specifically, the simulations of a coupled oscillator model predict 54\% of the variance in the empirically measured EEG functional connectivity. Hypotheses of temporal coding have been proposed for the computational role of these dynamic oscillatory interactions on fast timescales. These oscillatory interactions play a role in the dynamic coordination between brain areas as well as between cortical columns or individual cells. Here I will extend neural network models, which learn unsupervised from statistics of natural stimuli, with phase variables that allow temporal coding in distributed representations. The analysis shows that synchronization of these phase variables provides a useful mechanism for binding of activated neurons, contextual coding, and figure ground segregation. Importantly, these results could also provide new insights for improvements of deep learning methods for machine learning tasks. The dynamic coordination in neural networks has also large influences on behavior and cognition. In a behavioral experiment, we analyzed multisensory integration between a native and an augmented sense. The participants were blindfolded and had to estimate their rotation angle based on their native vestibular input and the augmented information. Our results show that subjects alternate in the use between these modalities, indicating that subjects dynamically coordinate the information transfer of the involved brain regions. Dynamic coordination is also highly relevant for the consolidation and retrieval of associative memories. In this regard, I investigated the beneficial effects of sleep for memory consolidation in an electroencephalography (EEG) study. Importantly, the results demonstrate that sleep leads to reduced event-related theta and gamma power in the cortical EEG during the retrieval of associative memories, which could indicate the consolidation of information from hippocampal to neocortical networks. This highlights that cognitive flexibility comprises both dynamic organization on fast timescales and structural changes on slow timescales. Overall, the computational and empirical experiments demonstrate how the brain evolved to a system that can flexibly adapt to any situation in a matter of milliseconds. This flexibility in information processing is enabled by an effective interplay between the structure of the neural network, the functional activations, and the dynamic interactions on fast time scales.