Les fours de maintien à induction sous vide sont utilisés pour la fabrication d'aubes de turbine à l'aide du procédé de fonderie à cire perdue. La maîtrise de la thermique de ce dernier est primordiale afin d'assurer la qualité de la production. Ce travail est composé de 3 grandes parties : la modélisation détaillée du four, la modélisation réduite et l'expérience. Un four axisymétrique et sa charge ont été modélisés numériquement à l'aide des logiciels FlexPDE et COMSOL Multiphysics. Ces modèles utilisent en entrée la puissance consommée par les inducteurs et fournissent en sortie la température en tout point du module de chauffe et de la charge. Les modèles réduits étudiés sont des modèles physiques dit convolutifs (enthalpique (0D), analytique de dimension 1 (1D)) et paramétrique de type AutoRégressifs avec variables eXogènes (ARX). Les modèles ARX ont été comparés aux modèles convolutifs (procédures d'identification, puis de validation, de modèles). L'objectif est d'accéder à des températures en certains points du four sans présence d'un capteur physique local (capteur virtuel). Les modélisations détaillées et réduites ont été comparées à des expériences réalisées sur four de production. Cette dernière partie n'est pas décrite dans ce mémoire / Vacuum holding induction furnaces are used for the manufacturing of turbine blades by loss wax foundry process. Heat transfer control in a holding furnace is crucial to ensure the quality of manufacturing. This work has 3 major parts, which are the detailed modelling, reduced modelling and experiments. An axyisymmetric furnace and its load have been numerically modelled using FlexPDE and COMSOL Multiphysics, finite element codes. Modelling takes as inputs the electric power consumed by inductors and gives the temperature field in the heating module. The studied reduced models are phyisical convolutive models (enthalpic (0D), 1 dimension analytical (1D)) and parametric like AutoRegressive with eXogeneous inputs (ARX). ARX models have been compared to convolutive models (identification, validation). The goal is to get temperatures in the heating module without local presence of physical sensors (virtual sensor). Detailed and reduced models have been compared to experiments on the furnace. This last part is not described in this document

A reação álcali-agregado (RAA) é uma reação química complexa envolvendo os álcalis presentes no cimento Portland e minerais silicosos presentes em alguns tipos de agregados reativos. O produto dessa reação expande quando em contato com a água, causando fissuração e deformação, o que afeta a durabilidade e importantes propriedades mecânicas das estruturas de concreto. Entretanto, estruturas que estejam sob tensão de compressão, aplicada por carregamentos ou induzida pela presença de armadura, apresentam expansão reduzida na direção comprimida. Uma das ferramentas que se utiliza para a previsão da vida útil de estruturas afetadas pela RAA é a modelagem numérica da expansão do concreto, possibilitando a previsão e projeto de reparos necessários. Nesse contexto, modelos paramétricos combinam os principais fatores que influenciam a reação (reatividade dos constituintes, porosidade, temperatura, umidade e estado de tensões), possibilitando a determinação da distribuição e da taxa de expansão na estrutura. Nessa pesquisa foi desenvolvido um modelo paramétrico cujo principal interesse foi o estudo das tensões induzidas pela presença de fibras e armadura na expansão do concreto afetado pela RAA. O modelo implementado em elementos finitos tem como parâmetros nodais as posições, uma formulação alternativa ao método dos elementos finitos baseado em deslocamentos; a medida de deformação utilizada é a de Green e a lei constitutiva dos materiais é a de Saint-Venant-Kirchhoff. O material composto é discretizado por meio de uma matriz elástica bi-dimensional reforçado por fibras lineares perfeitamente aderidas à matriz. Comparações entre os resultados obtidos por meio do modelo desenvolvido e resultados de trabalhos experimentais comprovam a aplicabilidade e potencialidade do modelo numérico apresentado. / The alkali-aggregate reaction (AAR) is a complex chemical reaction involving alkalis present in the Portland cement paste and siliceous minerals present in some types of reactive aggregates. The product of this reaction expands in contact with water, causing cracking and deformation that affects durability and important mechanical properties of the concrete structures. However, structures under compressive stress, applied or induced by the presence of rebars, the expansion is reduced in the compressed direction. One of the tools used to predict the service life of structures affected by AAR is the numerical modeling of the concrete expansion, allowing prevision and projecting repairs needed. In this context, parametric models combine the main factors that influence the reaction (reactivity of the constituents, porosity, temperature, humidity and stress state), enabling the determination of distribution and rate of expansion in the structure. In this research was developed a parametric model whose main interest was the study of stresses induced by the presence of fibers and rebars in the expansion of concrete affected by AAR. The model implemented in finite element has as nodal parameters positions, an alternative formulation to the finite element method based on displacement; the deformation measurement used is Green and the material constitutive law is the Saint-Venant-Kirchhoff. The composite material is discretized by a two-dimensional elastic matrix reinforced by perfectly adherent linear fibers. Comparisons between the results obtained from the developed model and results of experimental studies demonstrate the applicability and potential of the numerical model presented.

This master’s thesis is deal with the area of determining the static transmission error in spur gears. Nowadays, around vibration and noise formation, the issue of transmission error is being discussed quite often. In the first part of this master’s thesis, there is a description of the theoretical requirements concerning the above stated issue. These theoretical requirements are further applied in parametric planning of the spur gears as well as in measuring the transmission error itself. In this master’s thesis, both the detailed description of planning the parametric model of spur gears and the strain-stress analysis may be found. These requirements are needed for calculating the transmission error. In conclusion of this master’s thesis, specific results of the static transmission error in spur gears achieved at diversifying load torque, center distance modifications as well as tip relief modifications of spur gears are being presented. The issue was solved by computation approach with the help of CAD software called Creo Parametric and FEM software called Ansys Workbench and Ansys Mechanical APDL.

The importance of sound financial risk management has become increasingly emphasised in recent years, especially with the financial crisis of 2007-08. The Basel Committee sets the international standards and regulations for banks and financial institutions, and in particular under market risk, they prescribe the internal application of the measure Value-at-Risk. However, the most established non-parametric Value-at-Risk model, historical simulation, has been criticised for some of its unrealistic assumptions. This thesis investigates alternative approaches for estimating non-parametric Value-at-Risk, by examining and comparing the capability of three counterbalancing weighting methodologies for historical simulation: an exponentially decreasing time weighting approach, a volatility updating method and, lastly, a more general weighting approach that enables the specification of central moments of a return distribution. With real financial data, the models are evaluated from a performance based perspective, in terms of accuracy and capital efficiency, but also in terms of their regulatory suitability, with a particular focus on the Swedish market. The empirical study shows that the capability of historical simulation is improved significantly, from both performance perspectives, by the implementation of a weighting methodology. Furthermore, the results predominantly indicate that the volatility updating model with a 500-day historical observation window is the most adequate weighting methodology, in all incorporated aspects. The findings of this paper offer significant input both to existing research on Value-at-Risk as well as to the quality of the internal market risk management of banks and financial institutions. / Betydelsen av sund finansiell riskhantering har blivit alltmer betonad på senare år, i synnerhet i och med finanskrisen 2007-08. Baselkommittén fastställer internationella normer och regler för banker och finansiella institutioner, och särskilt under marknadsrisk föreskriver de intern tillämpning av måttet Value-at-Risk. Däremot har den mest etablerade icke-parametriska Value-at-Risk-modellen, historisk simulering, kritiserats för några av dess orealistiska antaganden. Denna avhandling undersöker alternativa metoder för att beräkna icke-parametrisk Value-at‑Risk, genom att granska och jämföra prestationsförmågan hos tre motverkande viktningsmetoder för historisk simulering: en exponentiellt avtagande tidsviktningsteknik, en volatilitetsuppdateringsmetod, och slutligen ett mer generellt tillvägagångssätt för viktning som möjliggör specifikation av en avkastningsfördelnings centralmoment. Modellerna utvärderas med verklig finansiell data ur ett prestationsbaserat perspektiv, utifrån precision och kapitaleffektivitet, men också med avseende på deras lämplighet i förhållande till existerande regelverk, med särskilt fokus på den svenska marknaden. Den empiriska studien visar att prestandan hos historisk simulering förbättras avsevärt, från båda prestationsperspektiven, genom införandet av en viktningsmetod. Dessutom pekar resultaten i huvudsak på att volatilitetsuppdateringsmodellen med ett 500 dagars observationsfönster är den mest användbara viktningsmetoden i alla berörda aspekter. Slutsatserna i denna uppsats bidrar i väsentlig grad både till befintlig forskning om Value-at-Risk, liksom till kvaliteten på bankers och finansiella institutioners interna hantering av marknadsrisk.

Cette thèse est consacrée à tester la présence de changements dans les paramètres d'un modèle de régression non-linéaire ainsi que dans un modèle de régression linéaire en très grande dimension. Tout d'abord, nous proposons une méthode basée sur la vraisemblance empirique pour tester la présence de changements dans les paramètres d'un modèle de régression non-linéaire. Sous l'hypothèse nulle, nous prouvons la consistance et la vitesse de convergence des estimateurs des paramètres de régression. La loi asymptotique de la statistique de test sous l'hypothèse nulle nous permet de trouver la valeur critique asymptotique. D'autre part, nous prouvons que la puissance asymptotique de la statistique de test proposée est égale à 1. Le modèle épidémique avec deux points de rupture est également étudié. Ensuite, on s'intéresse à construire les régions de confiance asymptotiques pour la différence entre les paramètres de deux phases d'un modèle non-linéaire avec des regresseurs aléatoires en utilisant la méthode de vraisemblance empirique. On montre que le rapport de la vraisemblance empirique a une distribution asymptotique χ2. La méthode de vraisemblance empirique est également utilisée pour construire les régions de confiance pour la différence entre les paramètres des deux phases d'un modèle non-linéaire avec des variables de réponse manquantes au hasard (Missing At Random (MAR)). Afin de construire les régions de confiance du paramètre en question, on propose trois statistiques de vraisemblance empirique : la vraisemblance empirique basée sur les données cas-complète, la vraisemblance empirique pondérée et la vraisemblance empirique par des valeurs imputées. On prouve que les trois rapports de vraisemblance empirique ont une distribution asymptotique χ2. Un autre but de cette thèse est de tester la présence d'un changement dans les coefficients d'un modèle linéaire en grande dimension, où le nombre des variables du modèle peut augmenter avec la taille de l'échantillon. Ce qui conduit à tester l'hypothèse nulle de non-changement contre l'hypothèse alternative d'un seul changement dans les coefficients de régression. Basée sur les comportements asymptotiques de la statistique de rapport de vraisemblance empirique, on propose une simple statistique de test qui sera utilisée facilement dans la pratique. La normalité asymptotique de la statistique de test proposée sous l'hypothèse nulle est prouvée. Sous l'hypothèse alternative, la statistique de test diverge / In this PHD thesis, we propose a nonparametric method based on the empirical likelihood for detecting the change in the parameters of nonlinear regression models and the change in the coefficient of linear regression models, when the number of model variables may increase as the sample size increases. Firstly, we test the null hypothesis of no-change against the alternative of one change in the regression parameters. Under null hypothesis, the consistency and the convergence rate of the regression parameter estimators are proved. The asymptotic distribution of the test statistic under the null hypothesis is obtained, which allows to find the asymptotic critical value. On the other hand, we prove that the proposed test statistic has the asymptotic power equal to 1. The epidemic model, a particular case of model with two change-points, under the alternative hypothesis, is also studied. Afterwards, we use the empirical likelihood method for constructing the confidence regions for the difference between the parameters of a two-phases nonlinear model with random design. We show that the empirical likelihood ratio has an asymptotic χ2 distribu- tion. Empirical likelihood method is also used to construct the confidence regions for the difference between the parameters of a two-phases nonlinear model with response variables missing at randoms (MAR). In order to construct the confidence regions of the parameter in question, we propose three empirical likelihood statistics : empirical likelihood based on complete-case data, weighted empirical likelihood and empirical likelihood with imputed va- lues. We prove that all three empirical likelihood ratios have asymptotically χ2 distributions. An another aim for this thesis is to test the change in the coefficient of linear regres- sion models for high-dimensional model. This amounts to testing the null hypothesis of no change against the alternative of one change in the regression coefficients. Based on the theoretical asymptotic behaviour of the empirical likelihood ratio statistic, we propose, for a deterministic design, a simpler test statistic, easier to use in practice. The asymptotic normality of the proposed test statistic under the null hypothesis is proved, a result which is different from the χ2 law for a model with a fixed variable number. Under alternative hypothesis, the test statistic diverges

[pt] Com as falências ocorridas nas últimas décadas, no setor de seguros, um movimento surgiu para desenvolver modelos matemáticos capazes de ajudar no gerenciamento do risco, os chamados modelos internos. No Brasil, a SUSEP, seguindo a tendência mundial, exigiu que as empresas, interessadas em atuar no país, utilizassem um modelo interno para risco de subscrição. Com isto, obter um modelo interno tornou-se primordial para as empresas seguradoras no país. O modelo proposto neste trabalho ilustrado para seguro de vida para risco de subscrição se baseia em Cadeias de Markov, no Teorema Central do Limite, parte paramétrica, e na Simulação de Monte Carlo, parte não paramétrica. Em sua estrutura foi considerada a dependência entre titular e dependentes. Uma aplicação a dados reais mascarados foi feita para analisar o modelo. O capital mínimo requerido calculado utilizando o método híbrido foi comparado com o valor obtido utilizando somente o método paramétrico. Em seguida foi feita a análise de sensibilidade do modelo. / [en] The bankruptcies occurred in recent decades in the insurance sector, a movement arose to develop mathematical models capable of assisting in the management of risk, called internal models. In Brazil, the SUSEP, following the worldwide trend, demanded that the companies, interested in working in the country, using an internal model for underwriting risk. Because of this, developing an internal model has become vital for insurance companies in the country. The proposed model in this work illustrated to life insurance for the underwriting risk was based on the Markov chains, on the Central Limit Theorem to the parametric method, and Monte Carlo Simulation to the non-parametric method. In its structure, the dependence between the holder and dependents was considered. An application to masked real data was made to analyze the model. The minimum required capital calculated using the hybrid method was compared with the value obtained using only the parametric method. Then the sensitivities of the model were investigated.