Submitted by Maicon Juliano Schmidt (maicons) on 2015-06-05T13:48:30Z No. of bitstreams: 1 Guilherme Sperling Trapp.pdf: 2436697 bytes, checksum: e28516dbd4b7d9f11a41310b229ce39a (MD5) / Made available in DSpace on 2015-06-05T13:48:30Z (GMT). No. of bitstreams: 1 Guilherme Sperling Trapp.pdf: 2436697 bytes, checksum: e28516dbd4b7d9f11a41310b229ce39a (MD5) Previous issue date: 2015-03-05 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / O mercado de geração de energia está em contínua expansão no Brasil. Este mercado é formado por diferentes fontes de geração de energia, as quais acarretam diferentes impactos socioeconômicos e ambientais atreladas as suas operações. Estes impactos geram externalidades na forma de dano para a sociedade e a compensação destes não é de consenso comum entre os atores envolvidos. Neste sentido, a presente dissertação busca, através da aplicação do método do pensamento sistêmico, criar um modelo computacional de dinâmica de sistemas que possibilite a avaliação do custo sistêmico total, considerando as externalidades sociais econômicas e ambientais da geração de energia. Para este fim foi aplicada uma adaptação do método do PSPC - Pensamento Sistêmico e Planejamento por Cenários. Na primeira fase, gerou-se um entendimento maior sobre a temática; em seguida,realizou-se a construção do modelo computacional; e, posteriormente, o modelo foi aplicado a três usinas reais a fim de se fazer uma avaliação da inserção da energia eólica frente a substituição das fontes hidroelétrica e termoelétrica a carvão. Os resultados obtidos apresentaram uma evolução no entendimento na comparação do real custo da energia para sociedade, devido ao aprendizado proporcionado pela utilização do pensamento sistêmico em conjunto com a modelagem dinâmica de sistemas. Os dados encontrados também apontaram para uma mudança nas decisões sobre a matriz energética, se adotado um custo sistêmico para avaliação de novos projetos. / The power generation market still expanding in Brazil. This market consists of different sources of power generation, which cause different socioeconomic and environmental impacts besides its operations. These impacts generate externalities in the form of damage to society and the compensation of these are not common consensus among stakeholders. In this way, this thesis research through the application of systems thinking method, create a computer model of systems dynamic that enable the evaluation of the total systemic cost, considering the externalities of power generation. To this end was applied an adaptation of the method PSPC - Systems Thinking and Planning for Scenarios. In the first phase was generate a greater understanding on the subject. After there was the construction of the computational model, and finally the model was applied to three real plants in order to assessment the option of wind energy against replacement of hydroelectric sources and thermal coal. The results showed an evolution in understanding the comparison of the real cost of energy to society due to the learning provided by the use of systems thinking in conjunction with the systems dynamic modeling. The data also pointed to a change in decisions about energy matrix, if adopted a total systemic cost for evaluation of new projects.

Made available in DSpace on 2014-12-17T14:56:07Z (GMT). No. of bitstreams: 1 DanielGVF_DISSERT.pdf: 2881772 bytes, checksum: 5236953fb6bb70560393eeeaa01f96f9 (MD5) Previous issue date: 2012-08-31 / Conselho Nacional de Desenvolvimento Cient?fico e Tecnol?gico / The control, automation and optimization areas help to improve the processes used by industry. They contribute to a fast production line, improving the products quality and reducing the manufacturing costs. Didatic plants are good tools for research in these areas, providing a direct contact with some industrial equipaments. Given these capabilities, the main goal of this work is to model and control a didactic plant, which is a level and flow process control system with an industrial instrumentation. With a model it is possible to build a simulator for the plant that allows studies about its behaviour, without any of the real processes operational costs, like experiments with controllers. They can be tested several times before its application in a real process. Among the several types of controllers, it was used adaptive controllers, mainly the Direct Self-Tuning Regulators (DSTR) with Integral Action and the Gain Scheduling (GS). The DSTR was based on Pole-Placement design and use the Recursive Least Square to calculate the controller parameters. The characteristics of an adaptive system was very worth to guarantee a good performance when the controller was applied to the plant / As ?reas de controle, automa??o e otimiza??o contribuem para a melhoria dos processos utilizados pelas ind?strias, permitindo uma linha de produ??o r?pida, aprimorando a qualidade do produto final e reduzindo os custos de produ??o. Boas ferramentas para o desenvolvimento de pesquisas nestas ?reas s?o as plantas did?ticas, pois proporcionam um contato direto com equipamentos semelhantes ou at? mesmo usados no setor industrial. Em vista dessas capacidades, o objetivo deste trabalho ? modelar e controlar uma planta did?tica que consiste de um sistema de controle de processo para vaz?o e n?vel com instrumenta??o industrial. Com o modelo ? poss?vel construir um simulador capaz de permitir estudos a respeito do funcionamento do sistema, sem os gastos com a opera??o do processo real. ? o caso de experimentos com controladores, que podem ser testados diversas vezes antes de serem efetivamente utilizados no processo real. Dentre os diversos tipos de controladores existentes, foi dado foco aos de tipo adaptativo, principalmente ao auto-sintoniz?vel direto (Direct Self-Tuning Regulator DSTR) com a??o integral e ao controlador com Escalonamento de Ganho (Gain Scheduling GS). O controlador DSTR foi projetado com base no m?todo de posicionamento de p?los e teve seus par?metros calculados atrav?s da t?cnica dos m?nimos quadrados recursivos. As caracter?sticas dos sistemas adaptativos foram de grande valia para garantir um desempenho satisfat?rio dos controladores, quando aplicados ? planta

The study of randomly excited nonlinear dynamical systems forms the focus of this thesis. We discuss two classes of problems: first, the characterization of nonlinear random response of the system before it comes into existence and, the second, assimilation of measured responses into the mathematical model of the system after the system comes into existence. The first class of problems constitutes forward problems while the latter belongs to the class of inverse problems. An outstanding feature of these problems is that they are almost always not amenable for exact solutions. We tackle in the present study these two classes of problems using Monte Carlo simulation tools in conjunction with Markov process theory, Bayesian model updating strategies, and particle filtering based dynamic state estimation methods. It is well recognized in literature that any successful application of Monte Carlo simulation methods to practical problems requires the simulation methods to be reinforced with effective means of controlling sampling variance. This can be achieved by incorporating any problem specific qualitative and (or) quantitative information that one might have about system behavior in formulating estimators for response quantities of interest. In the present thesis we outline two such approaches for variance reduction. The first of these approaches employs a substructuring scheme, which partitions the system states into two sets such that the probability distribution of the states in one of the sets conditioned on the other set become amenable for exact analytical solution. In the second approach, results from data based asymptotic extreme value analysis are employed to tackle problems of time variant reliability analysis and updating of this reliability. We exemplify in this thesis the proposed approaches for response estimation and model updating by considering wide ranging problems of interest in structural engineering, namely, nonlinear response and reliability analyses under stationary and (or) nonstationary random excitations, response sensitivity model updating, force identification, residual displacement analysis in instrumented inelastic structures under transient excitations, problems of dynamic state estimation in systems with local nonlinearities, and time variant reliability analysis and reliability model updating. We have organized the thesis into eight chapters and three appendices. A resume of contents of these chapters and appendices follows. In the first chapter we aim to provide an overview of mathematical tools which form the basis for investigations reported in the thesis. The starting point of the study is taken to be a set of coupled stochastic differential equations, which are obtained after discretizing spatial variables, typically, based on application of finite element methods. Accordingly, we provide a summary of the following topics: (a) Markov vector approach for characterizing time evolution of transition probability density functions, which includes the forward and backward Kolmogorov equations, (b) the equations governing the time evolution of response moments and first passage times, (c) numerical discretization of governing stochastic differential equation using Ito-Taylor’s expansion, (d) the partial differential equation governing the time evolution of transition probability density functions conditioned on measurements for the study of existing instrumented structures, (e) the time evolution of response moments conditioned on measurements based on governing equations in (d), and (f) functional recursions for evolution of multidimensional posterior probability density function and posterior filtering density function, when the time variable is also discretized. The objective of the description here is to provide an outline of the theoretical formulations within which the problems of response estimation and model updating are formulated in the subsequent chapters of the present thesis. We briefly state the class of problems, which are amenable for exact solutions. We also list in this chapter major text books, research monographs, and review papers relevant to the topics of nonlinear random vibration analysis and dynamic state estimation. In Chapter 2 we provide a review of literature on solutions of problems of response analysis and model updating in nonlinear dynamical systems. The main focus of the review is on Monte Carlo simulation based methods for tackling these problems. The review accordingly covers numerical methods for approximate solutions of Kolmogorov equations and associated moment equations, variance reduction in simulation based analysis of Markovian systems, dynamic state estimation methods based on Kalman filter and its variants, particle filtering, and variance reduction based on Rao-Blackwellization. In this review we chiefly cover papers that have contributed to the growth of the methodology. We also cover briefly, the efforts made in applying the ideas to structural engineering problems. Based on this review, we identify the problems of variance reduction using substructuring schemes and data based extreme value analysis and, their incorporation into response estimation and model updating strategies, as problems requiring further research attention. We also identify a range of problems where these tools could be applied. We consider the development of a sequential Monte Carlo scheme, which incorporates a substructuring strategy, for the analysis of nonlinear dynamical systems under random excitations in Chapter 3. The proposed substructuring ensures that a part of the system states conditioned on the remaining states becomes Gaussian distributed and is amenable for an exact analytical solution. The use of Monte Carlo simulations is subsequently limited for the analysis of the remaining system states. This clearly results in reduction in sampling variance since a part of the problem is tackled analytically in an exact manner. The successful performance of the proposed approach is illustrated by considering response analysis of a single degree of freedom nonlinear oscillator under random excitations. Arguments based on variance decomposition result and Rao-Blackwell theorems are presented to demonstrate that the proposed variance reduction indeed is effective. In Chapter 4, we modify the sequential Monte Carlo simulation strategy outlined in the preceding chapter to incorporate questions of dynamic state estimation when data on measured responses become available. Here too, the system states are partitioned into two groups such that the states in one group become Gaussian distributed when conditioned on the states in the other group. The conditioned Gaussian states are subsequently analyzed exactly using the Kalman filter and, this is interfaced with the analysis of the remaining states using sequential importance sampling based filtering strategy. The development of this combined Kalman and sequential importance sampling filtering method constitutes one of the novel elements of this study. The proposed strategy is validated by considering the problem of dynamic state estimation in linear single and multi-degree of freedom systems for which exact analytical solutions exist. In Chapter 5, we consider the application of the tools developed in Chapter 4 for a class of wide ranging problems in nonlinear random vibrations of existing systems. The nonlinear systems considered include single and multi-degree of freedom systems, systems with memoryless and hereditary nonlinearities, and stationary and nonstationary random excitations. The specific applications considered include nonlinear dynamic state estimation in systems with local nonlinearities, estimation of residual displacement in instrumented inelastic dynamical system under transient random excitations, response sensitivity model updating, and identification of transient seismic base motions based on measured responses in inelastic systems. Comparisons of solutions from the proposed substructuring scheme with corresponding results from direct application of particle filtering are made and a satisfactory mutual agreement is demonstrated. We consider next questions on time variant reliability analysis and corresponding model updating in Chapters 6 and 7, respectively. The research effort in these studies is focused on exploring the application of data based asymptotic extreme value analysis for problems on hand. Accordingly, we investigate reliability of nonlinear vibrating systems under stochastic excitations in Chapter 6 using a two-stage Monte Carlo simulation strategy. For systems with white noise excitation, the governing equations of motion are interpreted as a set of Ito stochastic differential equations. It is assumed that the probability distribution of the maximum over a specified time duration in the steady state response belongs to the basin of attraction of one of the classical asymptotic extreme value distributions. The first stage of the solution strategy consists of selection of the form of the extreme value distribution based on hypothesis testing, and, the next stage involves the estimation of parameters of the relevant extreme value distribution. Both these stages are implemented using data from limited Monte Carlo simulations of the system response. The proposed procedure is illustrated with examples of linear/nonlinear systems with single/multiple degrees of freedom driven by random excitations. The predictions from the proposed method are compared with the results from large scale Monte Carlo simulations, and also with the classical analytical results, when available, from the theory of out-crossing statistics. Applications of the proposed method for vibration data obtained from laboratory conditions are also discussed. In Chapter 7 we consider the problem of time variant reliability analysis of existing structures subjected to stationary random dynamic excitations. Here we assume that samples of dynamic response of the structure, under the action of external excitations, have been measured at a set of sparse points on the structure. The utilization of these measurements in updating reliability models, postulated prior to making any measurements, is considered. This is achieved by using dynamic state estimation methods which combine results from Markov process theory and Bayes’ theorem. The uncertainties present in measurements as well as in the postulated model for the structural behaviour are accounted for. The samples of external excitations are taken to emanate from known stochastic models and allowance is made for ability (or lack of it) to measure the applied excitations. The future reliability of the structure is modeled using expected structural response conditioned on all the measurements made. This expected response is shown to have a time varying mean and a random component that can be treated as being weakly stationary. For linear systems, an approximate analytical solution for the problem of reliability model updating is obtained by combining theories of discrete Kalman filter and level crossing statistics. For the case of nonlinear systems, the problem is tackled by combining particle filtering strategies with data based extreme value analysis. The possibility of using conditional simulation strategies, when applied external actions are measured, is also considered. The proposed procedures are exemplified by considering the reliability analysis of a few low dimensional dynamical systems based on synthetically generated measurement data. The performance of the procedures developed is also assessed based on limited amount of pertinent Monte Carlo simulations. A summary of the contributions made and a few suggestions for future work are presented in Chapter 8. The thesis also contains three appendices. Appendix A provides details of the order 1.5 strong Taylor scheme that is extensively employed at several places in the thesis. The formulary pertaining to the bootstrap and sequential importance sampling particle filters is provided in Appendix B. Some of the results on characterizing conditional probability density functions that have been used in the development of the combined Kalman and sequential importance sampling filter in Chapter 4 are elaborated in Appendix C.

Einfache oder komplexe dynamische Systeme stellen Individuen und Gesellschaften gleichermaßen vor mitunter große Herausforderungen, wie regionale und globale Krisen immer wieder zeigen. Ein basales und allgemeines Verständnis dynamischer Zusammenhänge scheint daher nicht nur wünschenswert, sondern mit Blick auf ausgewählte aktuelle Krisen sogar notwendiger denn je. Doch auch in alltäglichen Situationen oder im Schulkontext kann ein fundamentales Verständnis dynamischer Systeme die individuellen Entscheidungen oder den mathematischen Erkenntnisgewinn unterstützen. Allerdings zeigt eine breite Basis empirischer Befunde, dass bereits relativ einfache Dynamiken, wie Fluss-Bestands-Systeme (FB-Systeme), nur unzureichend erfasst zu werden scheinen. Diese Dissertationsschrift verfolgt daher die generelle Fragestellung, wie sich ein basales Verständnis formal einfacher FB-Systeme fördern oder generieren lassen könnte. Aufgrund einer bislang fehlenden einheitlichen theoretischen Beschreibung des FB-Verständnisses und dessen Einflussfaktoren basiert die vorgestellte Untersuchungsserie einerseits auf drei ausgewählten generellen theoretischen Perspektiven und daraus abgeleiteten Einflussfaktoren. Zusätzlich wurden einzelne weitere theoretische Modelle und eine Vielzahl spezifischer empirischer Befunde, zur Wirksamkeit verschiedener Präsentationsformate auf kognitive Fähigkeiten, für die Begründung der experimentellen Manipulationen herangezogen. In einer Serie von sieben experimentellen Untersuchungen wurden diverse Möglichkeiten grafischer Darstellungen, isoliert und in Wechselwirkung mit verschiedenen Personenmerkmalen, empirisch bezüglich ihres Einflusses auf das basale Verständnis illustrierter FB-Systeme überprüft. Unter Anwendung geltender wissenschaftlicher Standards und durch Nutzung moderner inferenzstatistischer Verfahren erlauben die gewonnen Ergebnisse eine fundierte Beurteilung der untersuchten Einflussfaktoren. Organisiert in drei Teilen, konnten in einer Folge von einfachen statischen Abbildungen, über passive dynamische Repräsentationen, bis hin zu interaktiven animierten Interventionsformaten, zahlreiche Illustrationsvarianten in ihrer Wirkung auf ein basales FB-Verständnis beurteilt werden. In den Experimenten 1 bis 3 wurden zunächst ausgewählte statische Darstellungsformate, spezifische Kontexteinbettungen und adaptierte Instruktionsansätze überprüft. Dabei zeigte sich keiner der manipulierten Darstellungsaspekte als genereller Wirkfaktor auf das basale FB-Verständnis. Weder kombinierte oder angepasste Diagrammdarstellungen, noch Zusatzinformationen oder überlebensrelevante Kontexteinbettungen führten zu den erwarteten Verbesserungen des FB-Verständnisses. Selbst, auf etablierten pädagogischen Interventionen basierende Instruktionsformen zeigten keinen systematischen Einfluss auf die Lösungsraten von FB-Aufgaben. In den anschließenden Experimenten 4 bis 6 konnten unter passiven dynamischen Darstellungen – rezipierende Animationen ohne Eingriffsmöglichkeiten – gleichfalls keine generell wirksamen Formate identifiziert werden. Ob fließend oder segmentiert, einmalig oder repetitiv, einzeln oder kombiniert: Keine der untersuchten passiven Animationsarten schlug sich in verbesserten Lösungsraten nieder. Im letzten Teil der Dissertation wurden schließlich interaktiv dynamische Formate am Beispiel von eigens konzipierten computerspielbasierten Lerninterventionen empirisch untersucht. Erneut zeigten sich keine Haupteffekte für die Attribute dieser Art der Informationsvermittlung. Einerseits bieten die gewonnenen Daten insgesamt keine konkreten Hinweise darauf, welche Formate generell geeignet sein könnten, FB-Zusammenhänge verständlich zu kommunizieren. Andererseits ließen sich wiederholt relevante Individualfaktoren identifizieren, die, spezifisch und in Wechselwirkungen mit den Repräsentationsformaten, das Ausmaß des individuellen FB-Verständnisses substanziell zu beeinflussen scheinen. Bereits in den ersten Experimenten traten spezifische Personenmerkmale hervor, die sich über die gesamte Untersuchungsserie hinweg als eigenständige Determinanten prädiktiv für das FB-Verständnis zeigten. Das Geschlecht (wobei Männer im Mittel ein besseres FB-Verständnis zeigten) und die mathematischen Fähigkeiten der Versuchspersonen bestimmen offenbar das Verständnis einfacher dynamischer Systeme deutlich stärker, als jedes der manipulierten Darstellungsformate. Gleichfalls scheinen sie für alle untersuchten Varianten der Repräsentationsformate vergleichbar und unabhängig voneinander relevant zu sein – wie statistische Kontrollmaßnahmen zeigen konnten. Vereinzelt, aber weniger stringent, konnten ebenfalls prädiktive Einflüsse motivationaler und kognitiver Faktoren, wie räumliche Intelligenz, beobachtet werden. Einige dieser Personenmerkmale traten wiederholt, wenn auch ohne erkennbare Systematik, in Wechselwirkung mit den experimentellen Darstellungsvarianten in Erscheinung. In Abhängigkeit von bestimmten Personenmerkmalen wirkten sich demnach einige der untersuchten Darstellungsformen unterschiedlich auf die Leistung in FB-Aufgaben aus. Insbesondere für animierte Präsentationsformate zeigten sich dabei Interaktionseffekte mit dem Geschlecht, wonach Männer und Frauen offenbar von verschiedenen Illustrationsarten profitieren. In nahezu allen Experimenten der Teile II und III konnte ein derartiger Geschlechter-Darstellungsformat-Interaktionseffekt beobachtet werden. Weitaus seltener zeigten sich hingegen Moderatoreffekte von motivationalen oder kognitiven Faktoren. Obwohl die mathematischen Fähigkeiten über alle Experimente hinweg als substanzieller Prädiktor des FB-Verständnisses in Erscheinung traten, fanden sich überdies durchgängig keine Anzeichen für dementsprechende Interaktionseffekte. Darüber hinaus boten explorative Vergleiche zwischen den verschiedenen Experimenten weitere interessante Hinweise auf die Hintergründe des generell relativ schwach ausgeprägten basalen FB-Verständnisses. Da Experiment 6 in Kooperation mit der Pädagogischen Hochschule Heidelberg durchgeführt werden konnte, ließen sich mathematisch sehr gut vorgebildete Versuchspersonen für die Teilnahme gewinnen. Diese zeigten, im Vergleich zu den Kohorten der vorangegangenen Experimente, ein sehr hohes Verständnis der illustrierten FB-Systeme. Dies unterstreicht, über die Bedeutung individueller mathematischer Fähigkeiten hinaus, dass ein gutes bis sehr gutes FB-Verständnis prinzipiell realisierbar ist. Weitere explorative Analysen deuten überdies auf eine besonders positive Wirkung passiver dynamischer Repräsentationen im Kontext der kreierten computerspielbasierten Interventionen. Die in Experiment 7 ursprünglich als Kontrollbedingung konzipierte Darstellungsvariante führte gegenüber einer vergleichbaren Stichprobe weiblicher Versuchsteilnehmer zu deutlich verbesserten Lösungsraten. Ergänzend zu vergleichenden Diskussionen der drei separaten Dissertationsteile folgt eine abschließende Generaldiskussion. Neben generellen Aspekten der Ergebnisse werden darin die zentralen Schlussfolgerungen und Erkenntnisse zusammengefasst. Die Erörterung potenzieller theoretischer und praktischer Implikationen sowie die Vorstellung spezifischer Anschlussfragestellungen und zukünftiger Forschungsanstrengungen bilden den Abschluss dieser Dissertationsschrift.