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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
861

Global Sensitivity Analysis of Inverter-Based Resources for Bulk Power System Dynamic Studies

Guddanti, Balaji January 2022 (has links)
No description available.
862

Computational modeling and design of nonlinear mechanical systems and materials

Tang, Pengbin 03 1900 (has links)
Les systèmes et matériaux mécaniques non linéaires sont largement utilisés dans divers domaines. Cependant, leur modélisation et leur conception ne sont pas triviales car elles nécessitent une compréhension complète de leurs non-linéarités internes et d'autres phénomènes. Pour permettre une conception efficace, nous devons d'abord introduire des modèles de calcul afin de caractériser avec précision leur comportement complexe. En outre, de nouvelles techniques de conception inverse sont également nécessaires pour comprendre comment le comportement change lorsque nous modifions les paramètres de conception des systèmes mécaniques non linéaires et des matériaux. Par conséquent, dans cette thèse, nous présentons trois nouvelles méthodes pour la modélisation informatique et la conception de systèmes mécaniques non linéaires et de matériaux. Dans le premier article, nous abordons le problème de la conception de systèmes mécaniques non linéaires présentant des mouvements périodiques stables en réponse à une force périodique. Nous présentons une méthode de calcul qui utilise une approche du domaine fréquentiel pour la simulation dynamique et la puissante analyse de sensibilité pour l'optimisation de la conception afin de concevoir des systèmes mécaniques conformes avec des oscillations de grande amplitude. Notre méthode est polyvalente et peut être appliquée à divers types de systèmes mécaniques souples. Nous validons son efficacité en fabriquant et en évaluant plusieurs prototypes physiques. Ensuite, nous nous concentrons sur la modélisation informatique et la caractérisation mécanique des matériaux non linéaires dominés par le contact, en particulier les matériaux à emboîtement discret (DIM), qui sont des tissus de cotte de mailles généralisés constitués d'éléments d'emboîtement quasi-rigides. Contrairement aux matériaux élastiques conventionnels pour lesquels la déformation et la force de rappel sont directement couplées, la mécanique des DIM est régie par des contacts entre des éléments individuels qui donnent lieu à des contraintes de déformation cinématique anisotrope. Pour reproduire le comportement biphasique du DIM sans simuler des structures à micro-échelle coûteuses, nous introduisons une méthode efficace de limitation de la déformation anisotrope basée sur la programmation conique du second ordre (SOCP). En outre, pour caractériser de manière exhaustive la forte anisotropie, le couplage complexe et d'autres phénomènes non linéaires du DIM, nous introduisons une nouvelle approche d'homogénéisation pour distiller des limites de déformation à grande échelle à partir de simulations à micro-échelle et nous développons un modèle macromécanique basé sur des données pour simuler le DIM avec des contraintes de déformation homogénéisées. / Nonlinear mechanical systems and materials are broadly used in diverse fields. However, their modeling and design are nontrivial as they require a complete understanding of their internal nonlinearities and other phenomena. To enable their efficient design, we must first introduce computational models to accurately characterize their complex behavior. Furthermore, new inverse design techniques are also required to capture how the behavior changes when we change the design parameters of nonlinear mechanical systems and materials. Therefore, in this thesis, we introduce three novel methods for computational modeling and design of nonlinear mechanical systems and materials. In the first article, we address the design problem of nonlinear mechanical systems exhibiting stable periodic motions in response to a periodic force. We present a computational method that utilizes a frequency-domain approach for dynamical simulation and the powerful sensitivity analysis for design optimization to design compliant mechanical systems with large-amplitude oscillations. Our method is versatile and can be applied to various types of compliant mechanical systems. We validate its effectiveness by fabricating and evaluating several physical prototypes. Next, we focus on the computation modeling and mechanical characterization of contact-dominated nonlinear materials, particularly Discrete Interlocking Materials (DIM), which are generalized chainmail fabrics made of quasi-rigid interlocking elements. Unlike conventional elastic materials for which deformation and restoring forces are directly coupled, the mechanics of DIM are governed by contacts between individual elements that give rise to anisotropic kinematic deformation constraints. To replicate the biphasic behavior of DIM without simulating expensive microscale structures, we introduce an efficient anisotropic strain-limiting method based on second-order cone programming (SOCP). Additionally, to comprehensively characterize strong anisotropy, complex coupling, and other nonlinear phenomena of DIM, we introduce a novel homogenization approach for distilling macroscale deformation limits from microscale simulations and develop a data-driven macromechanical model for simulating DIM with homogenized deformation constraints.
863

Reduzierte thermische Modelle für das gesamte Thermit-Schweißverfahren

Manzke, Sebastian 17 November 2022 (has links)
Ziel der Dissertation ist die ebenso valide wie effiziente Vorhersage der Schmelz- und der Wärmeeinflusszone der Schweißverbindung beim Thermit-Schweißen. Dazu werden reduzierte Modelle vorgestellt, darunter ein niederdimensionales Modell des Schienenstegs und dreidimensionale Modelle des Gießsystems für das Schweißverfahren. Mit dem niederdimensionalen Modell werden mittels Parameterschätzung unbekannte Randbedingungen der Vorwärmung des Gießsystems ermittelt sowie mittels Sensitivitätsanalyse systematisch Einflüsse auf die Schmelz- und die Wärmeeinflusszone untersucht. Durch den systematischen Vergleich der vorgestellten Modelle werden Gültigkeitsgrenzen der Modelle gezielt auf die Modellreduktionen zurückgeführt und über die Modelle hinausgehende Aspekte für die Beschreibung des Schmelz- und Erstarrungsverhaltens identifiziert. Dabei wird die Validität der Modelle anhand von experimentellen Daten der Schmelz- und der Wärmeeinflusszone im Schienenlängsschnitt untersucht. / This dissertation aims at providing a valid and efficient prediction of the melting zone and heat-affected zone of thermite welds. For this purpose, reduced models are presented, including a low-dimensional model of the rail web and three-dimensional models of the casting system for the welding process. With the low-dimensional model, unknown boundary conditions of the preheating of the casting system are determined by means of parameter estimation and influences on the melting zone and the heat-affected zone are systematically examined by means of a sensitivity analysis. By a systematic comparison of the models presented, the validity limits of the models are specifically traced back to the model reductions and aspects beyond these models for the description of the melting and solidification behavior are identified. The validity of the models is examined on the basis of experimental data from the melting zone and the heat-affected zone in the longitudinal section of the rails.
864

[pt] OTIMIZAÇÃO DIMENSIONAL E DE FORMA DE TRELIÇAS ESPACIAIS MODELADAS COM CURVAS DE BÉZIER / [en] SIZE AND SHAPE OPTIMIZATION OF SPACE TRUSSES MODELED BY BÉZIER CURVES

WALDY JAIR TORRES ZUNIGA 18 December 2019 (has links)
[pt] Estruturas treliçadas espaciais são arranjos geométricos de barras amplamente utilizados em coberturas de edificações. Diversos fatores favorecem o seu uso, tais como a capacidade de vencer grandes vãos e a facilidade em assumir diversas formas. A busca pela geometria ótima é um objetivo importante no projeto de estruturas, onde o interesse principal é minimizar o custo da estrutura. O objetivo deste trabalho é apresentar um sistema computacional capaz de minimizar o peso de estruturas treliçadas cuja geometria é definida por curvas de Bézier. Portanto, os pontos de controle das curvas de Bézier são utilizados como variáveis de projeto. As áreas das seções transversais das barras e a altura da treliça também são consideradas como variáveis de projeto e restrições sobre a tensão de escoamento e a tensão crítica de Euler são impostas no problema de otimização. A estrutura é analisada por meio do método dos elementos finitos considerando a hipótese do comportamento linear físico e geométrico. Os algoritmos de otimização usados neste trabalho utilizam o gradiente da função objetivo e das restrições em relação às variáveis de projeto. O sistema computacional desenvolvido neste trabalho foi escrito em linguagem MATLAB e conta com uma integração com o SAP2000 por meio da OAPI (Open Application Programming Interface). Os resultados numéricos obtidos demonstram a eficiência e a aplicabilidade deste sistema. / [en] Spatial truss structures are geometrical arrangements of bars widely used in building roofs. Several factors favor their use, such as the ability to overcome large spans and the capability of assuming a variety of configurations. The search for optimal geometry is an important goal in the design of structures, where the main interest is to minimize the cost of the structure. The objective of this work is to present a computational system capable of minimizing the weight of truss structures whose geometry is defined by Bézier curves. Therefore, the control points of the Bézier curves are used as design variables. The cross-sectional areas of the bars and the truss height are also considered as design variables and constraints on the yield stress and Euler critical stress are imposed on the optimization problem. The structure is analyzed using truss elements considering the physical and geometric linear behavior. The optimization algorithms used in this work require the gradient of the objective function and constraints with respect to the design variables. The computational system developed in this work was written in MATLAB and has an integration with SAP2000 through the OAPI (Open Application Programming Interface). The obtained numerical results demonstrate the efficiency and applicability of the developed system.
865

[pt] OTIMIZAÇÃO TOPOLÓGICA DE ESTRUTURAS GEOMETRICAMENTE NÃOLINEARES BASEADA EM UM ESQUEMA DE INTERPOLAÇÃO DE ENERGIA / [en] TOPOLOGY OPTIMIZATION OF GEOMETRICALLY NONLINEAR STRUCTURES BASED ON AN ENERGY INTERPOLATION SCHEME

ANDRE XAVIER LEITAO 26 May 2020 (has links)
[pt] Em muitos problemas de engenharia, e.g., no projeto de próteses biomédicas flexíveis ou em dispositivos de absorção de energia, estruturas sofrem grandes deslocamentos. Nestes casos, a não linearidade geométrica deve ser levada em conta na resposta estrutural. Contudo, algoritmos de otimização topológica considerando não linearidades, e modelados segundo o método de elementos finitos, sofrem instabilidades numéricas causadas por distorções excessivas nas regiões de baixa densidade dentro do domínio de projeto. Em particular, a matriz de rigidez pode não ser positiva definida comprometendo a convergência do processo de otimização. Esta dissertação visa estudar um esquema de interpolação entre as formulações lineares e não lineares de elementos finitos para aliviar tais distorções. Em cada etapa da otimização, para determinar a configuração de equilíbrio, o sistema de equações não-lineares é resolvido pelo procedimento de Newton-Raphson. Utilizando-se das informações dos gradientes calculadas através do método adjunto, o Método das Assíntotas Móveis é empregado para atualizar as variáveis de projeto. Por meio de problemas de referência considerando grandes deslocamentos, são demonstradas a eficácia e a eficiência deste esquema de interpolação. Mais especificamente, as topologias otimizadas estão de acordo com aquelas obtidas na literatura e exibem a dependência esperada em relação ao nível de carga. O esquema de interpolação em estudo desempenha papel crucial na solução de problemas não lineares em níveis elevados de carga, permitindo que a rotina de otimização convirja e se obtenha a distribuição de material ótima. / [en] In many engineering problems, e.g., design of flexible biomedical prostheses or energy absorption devices, structures undergo large displacements. In those problems, the structural response must take into account the geometric nonlinearity. However, topology optimization algorithms regarding nonlinearities, and based on the finite element method, typically suffer from numerical instabilities caused by excessive distortions of low-density regions within the design domain. In particular, the stiffness matrix may be no longer positive definite, which can jeopardize the convergence of the optimization process. This thesis aims to study an interpolation scheme between linear and nonlinear finite element formultation to alleviate this convergence issue. At each step of the optimization, the nonlinear state equation is solved by the Newton-Raphson procedure to determine the equilibrium configuration. Making use of the gradient information computed from the adjoint method, the Method of Moving Asymptotes is employed to update the design variables. Through several benchmark problems considering large displacements, it is demonstrated the effectiveness and efficiency of this interpolation scheme. More specifically, the optimized designs are in agreement with those obtained in the literature and exhibit correct load-level dependence. The investigated interpolation scheme plays a crucial role in the solution of nonlinear problems with high load levels, allowing the optimization routine to converge and to obtain the optimal material arrangement.
866

Analysis of survey data in the presence of non-ignorable missing-data and selection mechanisms

Hammon, Angelina 04 July 2023 (has links)
Diese Dissertation beschäftigt sich mit Methoden zur Behandlung von nicht-ignorierbaren fehlenden Daten und Stichprobenverzerrungen – zwei häufig auftretenden Problemen bei der Analyse von Umfragedaten. Beide Datenprobleme können die Qualität der Analyseergebnisse erheblich beeinträchtigen und zu irreführenden Inferenzen über die Population führen. Daher behandle ich innerhalb von drei verschiedenen Forschungsartikeln, Methoden, die eine Durchführung von sogenannten Sensitivitätsanalysen in Bezug auf Missing- und Selektionsmechanismen ermöglichen und dabei auf typische Survey-Daten angewandt werden können. Im Rahmen des ersten und zweiten Artikels entwickele ich Verfahren zur multiplen Imputation von binären und ordinal Mehrebenen-Daten, welche es zulassen, einen potenziellen Missing Not at Random (MNAR) Mechanismus zu berücksichtigen. In unterschiedlichen Simulationsstudien konnte bestätigt werden, dass die neuen Imputationsmethoden in der Lage sind, in allen betrachteten Szenarien unverzerrte sowie effiziente Schätzungen zuliefern. Zudem konnte ihre Anwendbarkeit auf empirische Daten aufgezeigt werden. Im dritten Artikel untersuche ich ein Maß zur Quantifizierung und Adjustierung von nicht ignorierbaren Stichprobenverzerrungen in Anteilswerten, die auf der Basis von nicht-probabilistischen Daten geschätzt wurden. Es handelt sich hierbei um die erste Anwendung des Index auf eine echte nicht-probabilistische Stichprobe abseits der Forschergruppe, die das Maß entwickelt hat. Zudem leite ich einen allgemeinen Leitfaden für die Verwendung des Index in der Praxis ab und validiere die Fähigkeit des Maßes vorhandene Stichprobenverzerrungen korrekt zu erkennen. Die drei vorgestellten Artikel zeigen, wie wichtig es ist, vorhandene Schätzer auf ihre Robustheit hinsichtlich unterschiedlicher Annahmen über den Missing- und Selektionsmechanismus zu untersuchen, wenn es Hinweise darauf gibt, dass die Ignorierbarkeitsannahme verletzt sein könnte und stellen erste Lösungen zur Umsetzung bereit. / This thesis deals with methods for the appropriate handling of non-ignorable missing data and sample selection, which are two common challenges of survey data analysis. Both issues can dramatically affect the quality of analysis results and lead to misleading inferences about the population. Therefore, in three different research articles, I treat methods for the performance of so-called sensitivity analyses with regards to the missing data and selection mechanism that are usable with typical survey data. In the first and second article, I provide novel procedures for the multiple imputation of binary and ordinal multilevel data that are supposed to be Missing not At Random (MNAR). The methods’ suitability to produce unbiased and efficient estimates could be demonstrated in various simulation studies considering different data scenarios. Moreover, I could show their applicability to empirical data. In the third article, I investigate a measure to quantify and adjust non-ignorable selection bias in proportions estimated based on non-probabilistic data. In doing so, I provide the first application of the suggested index to a real non-probability sample outside its original research group. In addition, I derive general guidelines for its usage in practice, and validate the measure’s performance in properly detecting selection bias. The three presented articles highlight the necessity to assess the sensitivity of estimates towards different assumptions about the missing-data and selection mechanism if it seems realistic that the ignorability assumption might be violated, and provide first solutions to enable such robustness checks for specific data situations.
867

Regression modeling with missing outcomes : competing risks and longitudinal data / Contributions aux modèles de régression avec réponses manquantes : risques concurrents et données longitudinales

Moreno Betancur, Margarita 05 December 2013 (has links)
Les données manquantes sont fréquentes dans les études médicales. Dans les modèles de régression, les réponses manquantes limitent notre capacité à faire des inférences sur les effets des covariables décrivant la distribution de la totalité des réponses prévues sur laquelle porte l'intérêt médical. Outre la perte de précision, toute inférence statistique requière qu'une hypothèse sur le mécanisme de manquement soit vérifiée. Rubin (1976, Biometrika, 63:581-592) a appelé le mécanisme de manquement MAR (pour les sigles en anglais de « manquant au hasard ») si la probabilité qu'une réponse soit manquante ne dépend pas des réponses manquantes conditionnellement aux données observées, et MNAR (pour les sigles en anglais de « manquant non au hasard ») autrement. Cette distinction a des implications importantes pour la modélisation, mais en général il n'est pas possible de déterminer si le mécanisme de manquement est MAR ou MNAR à partir des données disponibles. Par conséquent, il est indispensable d'effectuer des analyses de sensibilité pour évaluer la robustesse des inférences aux hypothèses de manquement.Pour les données multivariées incomplètes, c'est-à-dire, lorsque l'intérêt porte sur un vecteur de réponses dont certaines composantes peuvent être manquantes, plusieurs méthodes de modélisation sous l'hypothèse MAR et, dans une moindre mesure, sous l'hypothèse MNAR ont été proposées. En revanche, le développement de méthodes pour effectuer des analyses de sensibilité est un domaine actif de recherche. Le premier objectif de cette thèse était de développer une méthode d'analyse de sensibilité pour les données longitudinales continues avec des sorties d'étude, c'est-à-dire, pour les réponses continues, ordonnées dans le temps, qui sont complètement observées pour chaque individu jusqu'à la fin de l'étude ou jusqu'à ce qu'il sorte définitivement de l'étude. Dans l'approche proposée, on évalue les inférences obtenues à partir d'une famille de modèles MNAR dits « de mélange de profils », indexés par un paramètre qui quantifie le départ par rapport à l'hypothèse MAR. La méthode a été motivée par un essai clinique étudiant un traitement pour le trouble du maintien du sommeil, durant lequel 22% des individus sont sortis de l'étude avant la fin.Le second objectif était de développer des méthodes pour la modélisation de risques concurrents avec des causes d'évènement manquantes en s'appuyant sur la théorie existante pour les données multivariées incomplètes. Les risques concurrents apparaissent comme une extension du modèle standard de l'analyse de survie où l'on distingue le type d'évènement ou la cause l'ayant entrainé. Les méthodes pour modéliser le risque cause-spécifique et la fonction d'incidence cumulée supposent en général que la cause d'évènement est connue pour tous les individus, ce qui n'est pas toujours le cas. Certains auteurs ont proposé des méthodes de régression gérant les causes manquantes sous l'hypothèse MAR, notamment pour la modélisation semi-paramétrique du risque. Mais d'autres modèles n'ont pas été considérés, de même que la modélisation sous MNAR et les analyses de sensibilité. Nous proposons des estimateurs pondérés et une approche par imputation multiple pour la modélisation semi-paramétrique de l'incidence cumulée sous l'hypothèse MAR. En outre, nous étudions une approche par maximum de vraisemblance pour la modélisation paramétrique du risque et de l'incidence sous MAR. Enfin, nous considérons des modèles de mélange de profils dans le contexte des analyses de sensibilité. Un essai clinique étudiant un traitement pour le cancer du sein de stade II avec 23% des causes de décès manquantes sert à illustrer les méthodes proposées. / Missing data are a common occurrence in medical studies. In regression modeling, missing outcomes limit our capability to draw inferences about the covariate effects of medical interest, which are those describing the distribution of the entire set of planned outcomes. In addition to losing precision, the validity of any method used to draw inferences from the observed data will require that some assumption about the mechanism leading to missing outcomes holds. Rubin (1976, Biometrika, 63:581-592) called the missingness mechanism MAR (for “missing at random”) if the probability of an outcome being missing does not depend on missing outcomes when conditioning on the observed data, and MNAR (for “missing not at random”) otherwise. This distinction has important implications regarding the modeling requirements to draw valid inferences from the available data, but generally it is not possible to assess from these data whether the missingness mechanism is MAR or MNAR. Hence, sensitivity analyses should be routinely performed to assess the robustness of inferences to assumptions about the missingness mechanism. In the field of incomplete multivariate data, in which the outcomes are gathered in a vector for which some components may be missing, MAR methods are widely available and increasingly used, and several MNAR modeling strategies have also been proposed. On the other hand, although some sensitivity analysis methodology has been developed, this is still an active area of research. The first aim of this dissertation was to develop a sensitivity analysis approach for continuous longitudinal data with drop-outs, that is, continuous outcomes that are ordered in time and completely observed for each individual up to a certain time-point, at which the individual drops-out so that all the subsequent outcomes are missing. The proposed approach consists in assessing the inferences obtained across a family of MNAR pattern-mixture models indexed by a so-called sensitivity parameter that quantifies the departure from MAR. The approach was prompted by a randomized clinical trial investigating the benefits of a treatment for sleep-maintenance insomnia, from which 22% of the individuals had dropped-out before the study end. The second aim was to build on the existing theory for incomplete multivariate data to develop methods for competing risks data with missing causes of failure. The competing risks model is an extension of the standard survival analysis model in which failures from different causes are distinguished. Strategies for modeling competing risks functionals, such as the cause-specific hazards (CSH) and the cumulative incidence function (CIF), generally assume that the cause of failure is known for all patients, but this is not always the case. Some methods for regression with missing causes under the MAR assumption have already been proposed, especially for semi-parametric modeling of the CSH. But other useful models have received little attention, and MNAR modeling and sensitivity analysis approaches have never been considered in this setting. We propose a general framework for semi-parametric regression modeling of the CIF under MAR using inverse probability weighting and multiple imputation ideas. Also under MAR, we propose a direct likelihood approach for parametric regression modeling of the CSH and the CIF. Furthermore, we consider MNAR pattern-mixture models in the context of sensitivity analyses. In the competing risks literature, a starting point for methodological developments for handling missing causes was a stage II breast cancer randomized clinical trial in which 23% of the deceased women had missing cause of death. We use these data to illustrate the practical value of the proposed approaches.
868

Contribution à la prise en compte des aspects thermiques des machines électriques dans un environnement mécatronique / Contribution to taking into consideration thermal aspects of electric machines in mechatronics environment

Assaad, Bassel 11 December 2015 (has links)
Les machines électriques jouent un rôle très important dans la conversion d'énergie dans plusieurs applications et domaines. Les contraintes thermiques jouent ainsi un rôle indispensable dans la conception des machines électriques de plus en plus petites et performantes. En effet, la performance des machines électriques est limitée par les températures maximales admissibles dans certaines zones critiques telles que le bobinage, les aimants permanents et les roulements. Deux approches principales peuvent être utilisées pour étudier le comportement thermique de la machine: la méthode nodale ou le circuit à constantes localisées ou les modèles numériques. Dans notre étude, nous proposons d'appliquer la méthode nodale sur une machine électrique intégrée dans un environnement mécatronique complexe. Le modèle thermique développé de la machine est ainsi présenté avec ses différents éléments. En effet, un modèle précis dépend fortement de plusieurs paramètres thermiques tels que les coefficients d'échange convectif, les conductances de contact, les conductivités équivalentes du bobinage, et autres paramètres. En conséquence, des techniques d'analyse de sensibilité sont ensuite appliquées sur le modèle thermique pour identifier les paramètres d'influence significative sur les températures de la machine ainsi que pour la réduction de ce modèle. Ensuite, nous appliquons deux méthodologies d'identification des paramètres thermiques incertains sont développées et appliquées afin de recaler le modèle thermique de la machine. Cette étape permet la validation de ce modèle par rapport à des mesures thermiques sur une machine synchrone à aimants permanents internes installée sur un banc de caractérisation de machine électriques. Finalement, nous intégrons le modèle recalé dans une approche système mécatronique comportant les lois de commande de la machine ainsi que son convertisseur. Ceci permettra ainsi d'étudier l'influence de la température d'une machine électrique sur le système mécatronique complet. / Electric machines play an important role in power conversion in several applications and fields. With the increasing demand for designing lighter and more efficient machines and optimizing the existing structures, thermal analysis becomes a necessary; in fact, the performance of electric machines islimited by the allowable temperatures in many critical components like windings, permanent magnetsand bearings. Two main approaches can be employed in order to study the machine thermal behavior : the lumped parameter thermal network (LPTN) or numerical models. Considering low-computationtime-consuming and the possibility to be integrated in a mechatronics system design, the LPTN method is considered in our study. The latter is mainly applied on electric machine integrated in a complex mechatronics environment. The thermal network is presented along with the definition of the principal elements constituting this network. In fact, an accurate and reliable network strongly depends on many critical parameters like heat transfer coefficients, interface gaps, impregnation goodness, among others. For this reason, different sensitivity analysis techniques are carried out in order to, first, identify the significance of uncertainties in the evaluation of these parameters on machine temperatures and second, to reduce the thermal network. Next, we propose two optimization algorithm-based identification methodologies in order to calibrate results of the thermal network with measured temperatures obtained from a test-bench of a permanent magnet based integrated starter-generator machine. The calibrated model is then integrated in a mechatronics system consisting of an electric model of the electric machine, along with its control strategy and the power converter. This final study allows us to evaluate the impact of the machine temperature rise on the mechatronic system.
869

Feed-and-bleed transient analysis of OSU APEX facility using the modern Code Scaling, Applicability, and Uncertainty method

Hallee, Brian Todd 05 March 2013 (has links)
The nuclear industry has long relied upon bounding parametric analyses in predicting the safety margins of reactor designs undergoing design-basis accidents. These methods have been known to return highly-conservative results, limiting the operating conditions of the reactor. The Best-Estimate Plus Uncertainty (BEPU) method using a modernized version of the Code-Scaling, Applicability, and Uncertainty (CSAU) methodology has been applied to more accurately predict the safety margins of the Oregon State University Advanced Plant Experiment (APEX) facility experiencing a Loss-of-Feedwater Accident (LOFA). The statistical advantages of the Bayesian paradigm of probability was utilized to incorporate prior knowledge when determining the analysis required to justify the safety margins. RELAP5 Mod 3.3 was used to accurately predict the thermal-hydraulics of a primary Feed-and-Bleed response to the accident using assumptions to accompany the lumped-parameter calculation approach. A novel coupling of thermal-hydraulic and statistical software was accomplished using the Symbolic Nuclear Analysis Package (SNAP). Uncertainty in Peak Cladding Temperature (PCT) was calculated at the 95/95 probability/confidence levels under a series of four separate sensitivity studies. / Graduation date: 2013
870

狀態轉換下利率與跳躍風險股票報酬之歐式選擇權評價與實證分析 / Option Pricing and Empirical Analysis for Interest Rate and Stock Index Return with Regime-Switching Model and Dependent Jump Risks

巫柏成, Wu, Po Cheng Unknown Date (has links)
Chen, Chang, Wen and Lin (2013)提出馬可夫調控跳躍過程模型(MMJDM)描述股價指數報酬率,布朗運動項、跳躍項之頻率與市場狀態有關。然而,利率並非常數,本論文以狀態轉換模型配適零息債劵之動態過程,提出狀態轉換下的利率與具跳躍風險的股票報酬之二維模型(MMJDMSI),並以1999年至2013年的道瓊工業指數與S&P 500指數和同期間之一年期美國國庫劵價格為實證資料,採用EM演算法取得參數估計值。經由概似比檢定結果顯示無論道瓊工業指數還是S&P 500指數,狀態轉換下利率與跳躍風險之股票報酬二維模型更適合描述報酬率。接著,利用Esscher轉換法推導出各模型下的股價指數之歐式買權定價公式,再對MMJDMSI模型進行敏感度分析以評估模型參數發生變動時對於定價公式的影響。最後,以實證資料對各模型進行模型校準及計算隱含波動度,結果顯示MMJDMSI在價內及價外時定價誤差為最小或次小,且此模型亦能呈現出波動度微笑曲線之現象。 / To model asset return, Chen, Chang, Wen and Lin (2013) proposed Markov-Modulated Jump Diffusion Model (MMJDM) assuming that the Brownian motion term and jump frequency are all related to market states. In fact, the interest rate is not constant, Regime-Switching Model is taken to fit the process of the zero-coupon bond price, and a bivariate model for interest rate and stock index return with regime-switching and dependent jump risks (MMJDMSI) is proposed. The empirical data are Dow Jones Industrial Average and S&P 500 Index from 1999 to 2013, together with US 1-Year Treasury Bond over the same period. Model parameters are estimated by the Expectation-Maximization (EM) algorithm. The likelihood ratio test (LRT) is performed to compare nested models, and MMJDMSI is better than the others. Then, European call option pricing formula under each model is derived via Esscher transformation, and sensitivity analysis is conducted to evaluate changes resulted from different parameter values under the MMJDMSI pricing formula. Finally, model calibrations are performed and implied volatilities are computed under each model empirically. In cases of in-the-money and out-the-money, MMJDMSI has either the smallest or the second smallest pricing error. Also, the implied volatilities from MMJDMSI display a volatility smile curve.

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