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This study focuses on the development of a stochastic simulation methodology to study the effects of cure kinetics uncertainty, in plane fibre misalignment and boundary conditions uncertainty on the cure process of composite materials. Differential Scanning Calorimetry was used to characterise cure kinetics variability of a commercial epoxy resin used in aerospace applications. It was found that cure kinetics uncertainty is associated with variations in the initial degree of cure, activation energy and reaction order. Image analysis was employed to characterise in plane fibre misalignment in a carbon fibre ±45º non-crimp fabric. The experimental results showed that variability in tow orientation was significant with a standard deviation of about 1.2º. A set of experiments using an infusion set-up was carried out to quantify boundary conditions uncertainty related to tool temperature, ambient temperature and surface heat transfer coefficient using thermocouples (tool/ambient temperature) and heat flux sensors (surface heat transfer coefficient). It was concluded that boundary conditions uncertainty can show considerable short term and long term variability. Conventional Monte Carlo and Probabilistic Collocation Method were integrated with a thermo-mechanical cure simulation model in order to investigate the effect of cure kinetics, fibre misalignment and boundary conditions variability on process outcome. The cure model was developed and implemented using a finite element model incorporating appropriate material sub-models of cure kinetics, specific heat capacity, thermal conductivity, moduli, thermal expansion and cure shrinkage. The effect of cure kinetics uncertainty on the temperature overshoot of a thick carbon fibre epoxy flat panel was investigated using the two stochastic simulation schemes. The stochastic simulation results showed that variability in cure kinetics can introduce a significant scatter in temperature overshoot, presenting a coefficient of variation of about 30%. Furthermore, it was shown that the collocation method can offer an efficient solution with significantly lower computational cost compared to Monte Carlo at comparable accuracy. Stochastic simulation of the cure of an angle shaped carbon fibre-epoxy component within the Monte Carlo scheme showed that fibre misalignment can cause considerable variability in the process outcome. The coefficient of variation of maximum residual stress can reach up to approximately 2% (standard deviation of 1 MPa) whilst qualitative and quantitative variations in final distortion of the cured part occur with the standard deviation in twist and corner angle reaching values of 0.4 º and 0.05º respectively. Simulation of the cure of a thin carbon fibre-epoxy panel within the Monte Carlo scheme indicated that surface heat transfer and tool temperature variability dominate variability in cure time, resulting in a coefficient of variation of about 22%. In addition to Monte Carlo, the effect of surface heat transfer coefficient and tool temperature variations on cure time was addressed using the collocation method. It was found that probabilistic collocation is capable of capturing variability propagation with good accuracy while offering tremendous benefits in terms of computational costs.

L’analyse probabiliste des ouvrages géotechniques est généralement réalisée en utilisant la méthode de simulation de Monte Carlo. Cette méthode n’est pas adaptée pour le calcul des faibles probabilités de rupture rencontrées dans la pratique car elle devient très coûteuse dans ces cas en raison du grand nombre de simulations requises pour obtenir la probabilité de rupture. Dans cette thèse, nous avons développé trois méthodes probabilistes (appelées AK-MCS, AK-IS et AK-SS) basées sur une méthode d’apprentissage (Active learning) et combinant la technique de Krigeage et l’une des trois méthodes de simulation (i.e. Monte Carlo Simulation MCS, Importance Sampling IS ou Subset Simulation SS). Dans AK-MCS, la population est prédite en utilisant un méta-modèle de krigeage qui est défini en utilisant seulement quelques points de la population, ce qui réduit considérablement le temps de calcul par rapport à la méthode MCS. Dans AK-IS, une technique d'échantillonnage plus efficace 'IS' est utilisée. Dans le cadre de cette approche, la faible probabilité de rupture est estimée avec une précision similaire à celle de AK-MCS, mais en utilisant une taille beaucoup plus petite de la population initiale, ce qui réduit considérablement le temps de calcul. Enfin, dans AK-SS, une technique d'échantillonnage plus efficace 'SS' est proposée. Cette technique ne nécessite pas la recherche de points de conception et par conséquent, elle peut traiter des surfaces d’état limite de forme arbitraire. Toutes les trois méthodes ont été appliquées au cas d'une fondation filante chargée verticalement et reposant sur un sol spatialement variable. Les résultats obtenus sont présentés et discutés. / The probabilistic analysis of geotechnical structures involving spatially varying soil properties is generally performed using Monte Carlo Simulation methodology. This method is not suitable for the computation of the small failure probabilities encountered in practice because it becomes very time-expensive in such cases due to the large number of simulations required to calculate accurate values of the failure probability. Three probabilistic approaches (named AK-MCS, AK-IS and AK-SS) based on an Active learning and combining Kriging and one of the three simulation techniques (i.e. Monte Carlo Simulation MCS, Importance Sampling IS or Subset Simulation SS) were developed. Within AK-MCS, a Monte Carlo simulation without evaluating the whole population is performed. Indeed, the population is predicted using a kriging meta-model which is defined using only a few points of the population thus significantly reducing the computation time with respect to the crude MCS. In AK-IS, a more efficient sampling technique ‘IS’ is used instead of ‘MCS’. In the framework of this approach, the small failure probability is estimated with a similar accuracy as AK-MCS but using a much smaller size of the initial population, thus significantly reducing the computation time. Finally, in AK-SS, a more efficient sampling technique ‘SS’ is proposed. This technique overcomes the search of the design points and thus it can deal with arbitrary shapes of the limit state surfaces. All the three methods were applied to the case of a vertically loaded strip footing resting on a spatially varying soil. The obtained results are presented and discussed.

Thesis (M.App.Sc.) - Carleton University, 2006. / Includes bibliographical references (p. 155-159). Also available in electronic format on the Internet.

Thèse numérisée par la Direction des bibliothèques de l'Université de Montréal.

Les troubles dépressifs représentent aujourd’hui l’une des causes de handicap et de mortalité précoce les plus fréquentes dans le monde. Les projections établies par l’Organisation Mondiale de la Sante à l’horizon 2020 prévoient qu’ils seront classés second, juste après les maladies cardiovasculaires. De nombreuses études ont montré l’efficacité des antidépresseurs dans la prévention des rechutes, cependant la situation semble moins claire s’agissant de la prévention des récidives. Un certain nombre de travaux de recherche ont été menés visant à évaluer l’impact médico-économique des stratégies thérapeutiques préventives, en recourant à la construction de modèles de simulation, ceux-ci permettant une représentation schématique de l’évolution de la pathologie au cours du temps. Cependant, afin d’être en mesure d’évaluer l’impact économique des stratégies de prévention des troubles dépressifs, un certain nombre de facteurs doivent être pris en considération dans l’élaboration du modèle représentatif de la pathologie. Nous montrons que l’intégration de l’ensemble des facteurs déterminants des récidives, tout en considérant un horizon temporel suffisamment large afin de capter les bénéfices thérapeutiques et (éventuellement) économiques sur le long terme, n’est pas sans poser problème. Nous montrons que les modèles disponibles dans la littérature sont seulement en mesure de proposer une forme partielle d’abstraction de la pathologie dépressive, généralement réduite à un ou deux facteurs de risque principaux, parmi lesquels l’observance du traitement, l’histoire médicale ou encore les caractéristiques sociodémographiques du patient. Nous proposons alors d’envisager les modèles de simulation à événements discrets en tant que réponse possible pour la représentation des facteurs de risque des troubles dépressifs récurrents, et détaillons les principes de la méthode. Nous tentons ensuite de développer un modèle ≪ princeps ≫ à partir de données épidémiologiques. Nous montrons alors que la flexibilité associée à ce type de modélisation permet de proposer un cadre d’analyse au plus près de la réalité de la pathologie dépressive / Depressive disorders represent today one of the most frequent causes of disability and premature death worldwide. Research on the natural history of depressive disorders has shown that it is indeed a chronic rather than an acute disease. Many studies have shown the effectiveness of antidepressants in preventing relapse; however, the situation seems less clear with regard to the prevention of recurrence. A number of research activities have been conducted to evaluate the pharmaco-economic impact of preventive strategies with the help of simulation models. These techniques represent a convenient tool enabling the schematic representation of disease progression over time. However, in order to be able to assess the economic impact of prevention strategies for depressive disorders, a number of factors must be taken into account when developing the model structure. We show that the integration of all determining factors, especially on a wide-enough time horizon in order to capture the therapeutic and possible economic benefits in the long term can be somewhat problematic. We show that the models available in the literature only present a partial framework aiming at depicting disease’s risk factors (medical history, treatment compliance or socio-demographic characteristics) and progression over time. We propose then to consider the use of discrete event simulation models as a possible tool for modelling recurrent depressive disorders, and we provide a detailed description of the principles of this methodology. We then try to develop a core model based on epidemiological evidence. We show that the flexibility associated with this type of modelling method can provide an analytical framework that depicts the characteristics of the depressive pathology in a more realistic fashion

First, this thesis introduces the system of pension insurance with state contribution including its proposed amendment made in 2009. Its aim is to forecast and to analyse expected development in pension insurance with state contribution. The main part of the thesis is focused on the simulation model of this insurance product. Within this model, annual interest on contributions is randomly generated and the amount of money a client of a hypothetical pension fund would receive is calculated. To facilitate this simulation, I programmed and attached (as a part of the thesis) an application in VBA language which enables to run this simulation in the preset number of replications. The thesis gives four examples of simulation experiments -- a simulation of pension insurance, and a simulation of pension saving, both versions both with and without contributions made by client's employer. The comparison of the expected efficiency of the both systems from the point of view of the government and a client is drawn at the end of the thesis.

Tendo em vista que a China domina o mercado das terras-raras e impõe restrições na produção e exportação sempre que surge a oportunidade, torna-se cada vez mais desafiador o desenvolvimento de empreendimentos nesse setor. No entanto, várias iniciativas foram tomadas nos últimos cinco anos no sentido de prospectar novos recursos e desenvolver a cadeia produtiva, inclusive a mineração, desses bens minerais ao redor do mundo. Porém, alguns fatores de incerteza já influenciaram ou vêm influenciando o futuro de alguns projetos em desenvolvimento, tais como o potencial de remuneração do depósito, a infraestrutura local existente, as expectativas de margens operacionais do empreendimento, as implicações dos elementos radioativos presentes nos depósitos, as expectativas potenciais de mercado com os elementos de terras-raras (ETR) produzidos e os fatores políticoeconômicos, conjunturais e de logística. Então, este trabalho introduz uma nova metodologia dinâmica de análise de competitividade (o modelo de competitividade e risco na mineração de terras-raras – MCRM-TR), na qual se consideram as variáveis/os fatores tidos como principais influências na performance de um empreendimento de mineração destinado ao aproveitamento dos ETR. Para o desenvolvimento do modelo, foram realizadas pesquisas nos âmbitos nacional e internacional, consultando empresas e instituições de pesquisa relacionadas com o setor de terras-raras. Por fim, com os dados obtidos nas pesquisas para cada fator dos respectivos depósitos, foram realizadas simulações por meio da geração de números aleatórios, utilizando-se para isso o método de Monte Carlo. A combinação dos principais resultados gerados produziu um índice de competitividade e risco na mineração de terras-raras (ICRM-TR). Com a aplicação dessa nova metodologia, foi possível constatar algumas realidades, como por exemplo o fato de o Projeto Mount Weld CLD – (AUS) da Lynas ter apresentado bons resultados no modelo e, no mundo real, ainda se manter resiliente diante das incertezas do setor de terras-raras, ao mesmo tempo em que o projeto da Molycorp apresentou resultados não tão satisfatórios e, no mundo real, passa por grandes dificuldades financeiras (em recuperação judicial). Também foi possível constatar que o Projeto Araxá da CBMM no Brasil está entre os mais competitivos do país. / It is known that China dominates the rare earth market and imposes constraints on production and export whenever the opportunity arises, so that it always becomes more and more challenging to develop enterprises in the sector. However, several initiatives have been taken in the last five years to explore new resources and develop the production chain, including mining, of these mineral assets around the world. However, some factors of uncertainty have already influenced or are still influencing the future of some projects under development, such as reservoir remuneration potential, existing local infrastructure, expectations of operating margins of the enterprise, implications of the radioactive elements present in the deposits, the potential market expectations with the rare earth elements (REE) produced and the political-economic, conjuncture and logistics factors. So, this work introduces a new dynamic methodology for competitiveness analysis (the competitiveness and risk model in rare earth mining ‒ MCRM-TR or CRM-REM), in which the variables/factors considered as main influences in the performance of a mining for the use of REEs are taken into account. In the development of the model, research was carried out at both national and international levels, consulting companies and research institutions related to the rare earth sector. Finally, with the data obtained in the surveys for each factor of the respective deposits, simulations were performed through the generation of random numbers, using the Monte Carlo method. The combination of the main results generated produced an index of competitiveness and risk in the rare earth mining (ICRM-TR or ICM-TRM). With the application of this new methodology, it was possible to verify some realities, such as the fact that the Mount Weld CLD Project (AUS) from Lynas had good results in the model and, in the real world, still remain resilient in the face of the uncertainties of the sector at the same time the Molycorp project presented less than satisfactory results and, in the real world, undergoing major financial difficulties (in judicial recovery). It was also possible to verify that the Araxá Project of CBMM in Brazil is among the most competitive in the country.

Submitted by admin tede (tede@pucgoias.edu.br) on 2018-05-14T19:00:50Z No. of bitstreams: 1 HUGO ALEXANDRE DO CARMO CENTENO.pdf: 1058068 bytes, checksum: 733a3144c0c1d4e70087e3a321855b0b (MD5) / Made available in DSpace on 2018-05-14T19:00:50Z (GMT). No. of bitstreams: 1 HUGO ALEXANDRE DO CARMO CENTENO.pdf: 1058068 bytes, checksum: 733a3144c0c1d4e70087e3a321855b0b (MD5) Previous issue date: 2018-04-04 / Delays in construction projects are a recurrent fact in several countries and regions for the most varied types of works. These delays, in addition to negatively impacting the image of companies contracted in the provision of construction services, cause several financial losses to interested parties: contractor and hired. Several researches that address the factors that lead to delays in the work schedule indicate the variation in the duration of the activities and, or the lack of an inadequate estimate for the duration of the activities, among the ten major delay factors in the projects. They contribute to the inadequate estimation of production of the activities, the lack of knowledge of the complexity of the activities, and also the lack or insufficiency of historical data that allow a safe estimate of the duration of the activities. Thus, this study aims to describe the behavior of variability in the duration of activities in an environment with little activity productivity data. The productivity data analyzed were taken from three similar construction projects carried out by the same company and which were provided as case studies of this work. For the analysis of the variability and description of the behavior of the activities, after the data collection of productivity of the project activities, the non-parametric Bootstrap data resampling associated with Monte Carlo Simulation (MCS) was used as techniques. Later, in order to verify the reliability of the results of variation in the duration of the activities, the schedule of the case studies was modeled using the Critical Path Method (CPM) and the Dependency Structure Matrix (DSM); and submitted to MCS. The simulated results of the total duration of the projects were adequate for the actual completion period of the projects studied, leading to the conclusion that the results of variation in the duration of the activities obtained by the cited technique are reliable. / Os atrasos em projetos de construção civil são um fato recorrente em diversos países e regiões para os mais variados tipos de obras. Esses atrasos, além de impactar negativamente a imagem das empresas contratadas na prestação de serviços de construção, acarretam diversos prejuízos financeiros às partes interessadas: contratante e contratado. Diversas pesquisas que abordam os fatores que acarretam em atrasos no cronograma de obras elencam a variação na duração das atividades e, ou, a falta de estimativa inadequada para duração das atividades, dentre os dez maiores fatores de atraso nos projetos. Contribuem para a inadequada estimativa de produção das atividades o desconhecimento da complexidade das mesmas e, também, a falta ou insuficiência de dados históricos que permitam estimar com segurança a duração das atividades. Assim, este trabalho visa descrever o comportamento de variabilidade na duração das atividades em um ambiente com poucos dados de produtividade das atividades. Os dados de produtividade analisados foram tomados de três projetos de construção civil semelhantes executados pela mesma empresa e que se prestaram como estudos de caso deste trabalho. Para análise da variabilidade e descrição do comportamento das atividades, após a coleta de dados de produtividade das atividades dos projetos, foram utilizadas como técnicas a reamostragem Bootstrap não paramétrica dos dados associada a Simulação de Monte Carlo (SMC). Posteriormente, para verificar a confiabilidade dos resultados de variação na duração das atividades, o cronograma dos estudos de caso foi modelado utilizando o Método do Caminho Crítico (CPM) e a Matriz de Estrutura de Dependência (DSM); e submetido a SMC. Os resultados simulados de duração total dos projetos mostraram-se adequados ao prazo real de conclusão dos projetos estudados, conduzindo à conclusão de que os resultados de variação na duração das atividades, obtidos pela técnica citada, são confiáveis.

Les clouds sont devenus ces dernières années des plate-formes incontournables dans le monde informatique, car ils permettent de provisionner des ressources à la demande et de ne payer qu’à l’usage. Ceci ouvre la possibilité de concevoir de nouvelles stratégies pour la planification et l’exécution des applications parallèles de type tâches indépendantes ou workflow. Cependant, trouver une stratégie bien adaptée aux contraintes des utilisateurs, que ce soit en termes de coûts et de temps d’exécution, est un problème difficile, pour lequel des outils de prédictions sont nécessaires. Néanmoins, la variabilité inhérente de ces plate-formes complexifient le développement d’un tel outil de prédiction. Notre thèse est que la simulation stochastique est une approche pertinente pour obtenir une prédiction s’accommodant de la variabilité, en produisant une distribution probabiliste des prédictions englobant les résultats réels observables. Pour le démontrer, nous utilisons une méthode de Monte-Carlo permettant de créer des simulations stochastiques par la répétitions de simulations déterministes. Nous montrons que cette méthode associée à certaines distributions d’entrée permettent de modéliser la variabilité d’une plate-forme à travers un unique paramètre. Pour évaluer la méthode proposée, nous comparons les résultats de notre méthode probabiliste à des exécutions réelles d’applications scientifiques. Nos expériences montrent que notre méthode permet de produire des prédictions représentatives des exécutions réelles observées. / The ability to provision resources on the fly and their pay-as-you-go nature has made cloud computing platforms a staple of modern computer infrastructure. Such platforms allow for new scheduling strategies for the execution of computing workloads. Finding a strategy that satisfies a user’s cost and time constraints is a difficult problem that requires a prediction tool. However the inherent variability of these platforms makes building such a tool a complex endeavor. Our thesis is that, by producing probability distributions of possible outcomes, stochastic simulation can be used to produce predictions that account for the variability. To demonstrate this we used Monte Carlo methods to produce a stochastic simulation by repeatedly running deterministic simulations. We show that this method used in conjunction with specific input models can model the variability of a platform using a single parameter. To validate our method we compare our results to real executions of scientific workloads. Our experiments show that our method produces predictions capable of representing theobserved real executions.