Abstract: The thesis work is part of a project that aims to develop a reliable CFD model to investigate the fluid-dynamics of a fluidized bubbling bed during gasification of refuse derived fuel (RDF) from sorted municipal solid waste (MSW). Gasification is a thermochemical process that converts carbon-containing materials into syngas. In this specific context scaling up is challenging because it implies dealing with a complex chemistry combined to heat and mass transfer phenomena in a multi-phase fluid environment. CFD modeling could represent a potential tool to predict the impact of the reactor configuration and operating conditions on gas yield, composition and potential contaminants. Validation of CFD simulations for such systems has been so far possible using different sophisticated experimental tools, allowing to link the model with experimental data. However, such high tech equipment may not always be available, especially at industrial scale. Hence, this work focuses on investigating the accuracy and numerical sensitivity of two different CFD models employed in the characterization of dense solid-particle flows in bubbling fluidized beds. The key parameter adopted to describe and quantify the dynamic behavior of this multiphase system is the power spectral density (PSD) distribution of pressure fluctuations. This PSD function was used to assess the accuracy of CFD models using one set of operating condition. The same type of analysis, extended to a wider range of operating conditions, may lead to a robust validation of the numerical models presented in this work. In spite of his measurement simplicity, pressure drop data present a strong connection with the bed fluid-dynamics and its interpretation could help to improve the fluidized bed technologies very fast, pushing CFD models closer to applications. / Résumé : Le but de ce projet est de développer un modèle CFD fiable pour étudier la dynamique des fluides d'un lit fluidisé en régime bullant pendant la gazéification de combustibles solides de récupération (CSR) triés à partir de déchets solides municipaux (DSM). La gazéification est un processus thermochimique qui convertit les matériaux contenant du carbone en gaz de synthèse. La mise à l'échelle est difficile dans ce cas car elle implique une chimie complexe combinée aux phénomènes de transfert de chaleur et de masse dans un environnement fluide multiphasique. La modélisation CFD représente un outil potentiel pour prédire l'impact de la configuration du réacteur et des conditions de fonctionnement sur le rendement, la composition et les contaminants potentiels du gaz. La validation des simulations CFD pour de tels systèmes a été jusqu'à présent possible grâce à l’utilisation de différents outils expérimentaux sophistiqués, permettant de lier le modèle aux données expérimentales. Toutefois, un tel équipement de pointe n’est pas toujours disponible, en particulier à l'échelle industrielle. Par conséquent, ce travail se concentre sur l'étude de la précision et de la sensibilité numérique de deux modèles CFD différents, utilisés dans la caractérisation des flux de particules solides denses dans les lits fluidisés bouillonnants. Le paramètre clé adopté pour décrire et quantifier le comportement dynamique de ce système multiphase est la distribution de la densité spectrale de puissance (DSP) des fluctuations de pression. La fonction DSP a été utilisée pour évaluer la précision des modèles CFD en utilisant un ensemble de conditions de fonctionnement. Le même type d'analyse, étendu à une plus large gamme de conditions de fonctionnement, peut conduire à une validation robuste des modèles numériques présentés dans ce travail. En dépit de sa simplicité de mesure, les données de chute de pression présentent une importante corrélation avec les lits fluidisés, de plus, leur interprétation pourrait aider à améliorer ces technologies très rapidement, poussant les modèles CFD plus près des applications.

A mesh-less method for solving incompressible, multi-phase flow problems has been developed and is discussed along with the presentation of benchmark results showing good agreement with theoretical and experimental results. Results of a systematic, parametric study of the single phase flow around a 2D circular cylinder at Reynolds numbers up to 1000 are presented and discussed. Simulation results show good agreement with experimental results. Extension of the method to deal with multiphase flow including liquid-to-vapor phase transition along with applications to cavitating flow are discussed. Insight gleaned from numerical experiments of the cavity closure problem are discussed along with recommendations for additional research. Several conclusions regarding the use of the method are made.

Energy consumption has become a first-class optimization goal in design and implementation of data-intensive computing systems. This is particularly true in the design of database management systems (DBMS), which is one of the most important servers in software stack of modern data centers. Data storage system is one of the essential components of database and has been under many research efforts aiming at reducing its energy consumption. In previous work, dynamic power management (DPM) techniques that make real-time decisions to transition the disks to low-power modes are normally used to save energy in storage systems. In this research, we tackle the limitations of DPM proposals in previous contributions and design a dynamic energy-aware disk storage system in database servers. We introduce a DPM optimization model integrated with model predictive control (MPC) strategy to minimize power consumption of the disk-based storage system while satisfying given performance requirements. It dynamically determines the state of disks and plans for inter-disk data fragment migration to achieve desirable balance between power consumption and query response time. Furthermore, via analyzing our optimization model to identify structural properties of optimal solutions, a fast-solution heuristic DPM algorithm is proposed that can be integrated in large-scale disk storage systems, where finding the most optimal solution might be long, to achieve near-optimal power saving solution within short periods of computational time. The proposed ideas are evaluated through running simulations using extensive set of synthetic workloads. The results show that our solution achieves up to 1.65 times more energy saving while providing up to 1.67 times shorter response time compared to the best existing algorithm in literature. Stream join is a dynamic and expensive database operation that performs join operation in real-time fashion on continuous data streams. Stream joins, also known as window joins, impose high computational time and potentially higher energy consumption compared to other database operations, and thus we also tackle energy-efficiency of stream join processing in this research. Given that there is a strong linear correlation between energy-efficiency and performance of in-memory parallel join algorithms in database servers, we study parallelization of stream join algorithms on multicore processors to achieve energy efficiency and high performance. Equi-join is the most frequent type of join in query workloads and symmetric hash join (SHJ) algorithm is the most effective algorithm to evaluate equi-joins in data streams. To best of our knowledge, we are the first to propose a shared-memory parallel symmetric hash join algorithm on multi-core CPUs. Furthermore, we introduce a novel parallel hash-based stream join algorithm called chunk-based pairing hash join that aims at elevating data throughput and scalability. We also tackle parallel processing of multi-way stream joins where there are more than two input data streams involved in the join operation. To best of our knowledge, we are also the first to propose an in-memory parallel multi-way hash-based stream join on multicore processors. Experimental evaluation on our proposed parallel algorithms demonstrates high throughput, significant scalability, and low latency while reducing the energy consumption. Our parallel symmetric hash join and chunk-based pairing hash join achieve up to 11 times and 12.5 times more throughput, respectively, compared to that of state-of-the-art parallel stream join algorithm. Also, these two algorithms provide up to around 22 times and 24.5 times more throughput, respectively, compared to that of non-parallel (sequential) stream join computation where there is one processing thread.

This master’s thesis deals with analytical and numerical description of surface gravity waves. Wave theories and their influence on water particle movement is described in the theoretical part of the thesis. Water particle moves in the same direction as wave propagation and this phenomenon is called Stokes drift. It has a significant influence on sediment transport and floating particle movement at water free surface. The experimental part consists of wave profile monitoring and water particle tracking in a wave flume with wave generator and beach model. The experimental results are compared with numerical simulation performed in the ANSYS Fluent software. Finally, the wave profiles obtained from simulation are compared with experimental wave profiles extracted by digital image processing.

The current demands on Swedish steel industry to produce low quantity batches of specialized products requires research on steel casting processes. There are several physical processes that need be taken into account for this problem to be viewed in full light such as thermal-processes, solidification and fluid dynamics. This work focuses on the fluid-dynamics part; more specifically, the dependence of flow quality within the caster on nozzle and mould geometry. The simulations are carried out using a scale-resolving method, in specific LES (Large Eddy Simulation) which is coupled with a DPM (Discrete Phase Model) to model Argon behaviour. The results of these simulations are presented and validated against physical experiment and data from industrial trials. Conclusions are drawn regarding optimal nozzle types in respect to different mould geometries. The mould eigenfrequencies are shown to exhibit a connection with the casting velocity. This results in so called sweet spots in casting velocity where flow irregularities due to sloshing is minimal. It is shown that the mountain type nozzle is preferable for smaller geometries whilst comparatively larger geometries benefit from a cup type. / FLOWFLEX CC

Submitted by Maicon Juliano Schmidt (maicons) on 2015-06-01T14:54:42Z No. of bitstreams: 1 Helena Beatriz Rower.pdf: 1700201 bytes, checksum: e1cfc9673a0cf09e95b5175f18b6ad50 (MD5) / Made available in DSpace on 2015-06-01T14:54:42Z (GMT). No. of bitstreams: 1 Helena Beatriz Rower.pdf: 1700201 bytes, checksum: e1cfc9673a0cf09e95b5175f18b6ad50 (MD5) Previous issue date: 2013-01-31 / Nenhuma / Objetivo: Verificar a associação entre autopercepção de nervosismo/stress, distúrbios psiquiátricos menores (DPM) e consumo adequado de frutas, verduras e legumes (FVL). Método: Este é um estudo transversal de base populacional com uma amostra representativa de 1100 adultos, com idade igual ou superior a 18 anos, residentes na zona urbana de um município de médio porte do sul do Brasil. O consumo adequado de frutas e legumes foi avaliado através de duas perguntas: uma sobre a quantidade de frutas ou suco natural de frutas consumido ao dia, e outra, a respeito do número de colheres de sopa de verduras/legumes consumidos ao dia. Considerou-se como consumo adequado a ingestão de três ou mais frutas ao dia concomitante com cinco ou mais colheres de sopa de verduras/legumes ao dia. As exposições principais variáveis foram autopercepção de nervosismo/stress e DPM. Para fornecer uma estimativa das razões de prevalências (RP) brutas e ajustadas, utilizou-se a regressão de Poisson. Potenciais fatores de confusão eram variáveis demográficas, socioeconômicas e comportamentais. Resultados: Ao analisar a amostra total, observaram-se associações significativas entre o desfecho com a autopercepção de nervosismo/stress e DPM. Após o controle de fatores de confusão, adultos relatando ausência de nervosismo/stress possuíam uma prevalência de consumo adequado duas vezes maior do que aqueles com resposta positiva (RP=1,99; IC95% 1,17-3,37; p=0,010). Similarmente, participantes com ausência de DPM possuíam uma prevalência de consumo adequado FVL 52% mais elevada quando comparados àqueles que relataram presença de DPM (RP=1,52; IC95% 1,10-2,10; p=0,016). Quando estratificada para o sexo, este efeito se manteve e aumentou nas mulheres, perdendo efeito e significância estatística entre os homens. Conclusão: Os resultados sugerem que a saúde mental pode ter papel importante para o consumo adequado de FVL, especialmente entre as mulheres. / Objective: To assess the association between mental health with adequate fruits and vegetables consumption. Method: This is a population based cross-sectional with a representative sample of 1,100 adult subjects living in the urban area of a medium size city in Souther Brazil. The adequate fruits and vegetables ́ intake was evaluated through two questions: one asking the quantity of fruit or fresh natural juice ingested on a daily basis; the other asking the number of soup spoons of vegetables consumed in a day. It was considered as an adequate intake the ingestion of three or more portions of fruit and of five or more soup spoons of vegetables. The main exposures were self-reported nervousness/stress and minor psychiatric disorders (MPD). In order to provide an estimation of the unadjusted and adjusted prevalence ratios (PR) Poisson regression was used. Potential confounding factors were demographic, socioeconomic and behavioral. Results: When the full sample was analyzed, a significant association between the nervousness/stress self-awareness and minor psychiatric disturbances was found. After controlling for the confounding factors, adults reporting absence were 2 times more likely to have the appropriate daily fruits and vegetables intake than those reporting presence of stressfull/nervous estates (PR=1,99; CI95% 1,17-3,37; p=0,010). In the same way, subjects not reporting mental disorders had a prevalence of adequate intake FVL 52% higher compared to those who had MPD (PR=1,52; CI95% 1,10-2,10; p=0,016). When stratified by gender, this effect was kept and increased in women. However, it lost its effect and statistical significance among men. Conclusion: The results suggests that the mental health may have an important role in the adequate intake of fruits and vegetables, especially among women.

Les applications temps réel modernes deviennent plus exigeantes en termes de ressources et de débit amenant la conception d'architectures multiprocesseurs. Ces systèmes, des équipements embarqués au calculateur haute performance, sont, pour des raisons d'autonomie et de fiabilité, confrontés des problèmes cruciaux de consommation d'énergie. Pour ces raisons, cette thèse propose de nouvelles techniques d'optimisation de la consommation d'énergie dans l'ordonnancement de systèmes multiprocesseur. La premiére contribution est un algorithme d'ordonnancement hiérarchique á deux niveaux qui autorise la migration restreinte des tâches. Cet algorithme vise á réduire la sous-optimalité de l'algorithme global EDF. La deuxiéme contribution de cette thèse est une technique de gestion dynamique de la consommation nommée Assertive Dynamic Power Management (AsDPM). Cette technique, qui régit le contrôle d'admission des tâches, vise á exploiter de manière optimale les modes repos des processeurs dans le but de réduire le nombre de processeurs actifs. La troisiéme contribution propose une nouvelle technique, nommée Deterministic Stretch-to-Fit (DSF), permettant d'exploiter le DVFS des processeurs. Les gains énergétiques observés s'approchent des solutions déjà existantes tout en offrant une complexité plus réduite. Ces techniques ont une efficacité variable selon les applications, amenant á définir une approche plus générique de gestion de la consommation appelée Hybrid Power Management (HyPowMan). Cette approche sélectionne, en cours d'exécution, la technique qui répond le mieux aux exigences énergie/performance.

La presente investigación se titula “Propuesta de uso de equipo LHD a batería como alternativa competitiva frente al equipo diésel en el proceso de limpieza de labores subterráneas horizontales en una operación minera mecanizada”. Este estudio tiene como objetivo evaluar una propuesta de uso del equipo LHD a batería como alternativa competitiva frente al equipo diésel en el proceso de limpieza de labores subterráneas horizontales determinando los factores productivos, analizando la sostenibilidad ambiental y evaluando los costos operacionales relacionada a los equipos LHD diésel y batería. Asimismo, para la evaluación comparativa de estos equipos se realizó un análisis a través de un caso de estudio en una unidad minera de operación mecanizada, con similares características a la Unidad Minera Atacocha. Los equipos que serán utilizados para la evaluación, bajo las mismas condiciones de trabajo, son el LHD a batería (ST7), de la empresa Epiroc, y el LHD diésel (R 1300G), de la empresa Caterpillar; ambos con una capacidad de carga útil nominal de 6,800 kg (4.2 yd3). Los resultados obtenidos a través del caso de estudio se dieron en tres niveles: productivo, sostenible ambiental y económico. En el aspecto productivo, se determinó que el equipo LHD a batería en comparación con el equipo LHD a diésel, para una distancia de acarreo de 150 m, tiene un menor tiempo de ciclo de 8.23%, y un mayor rendimiento productivo de 8.98%. En relación a los indicadores de gestión, se determinó que los equipos LHD a batería posee una ventaja frente a los equipos diésel como en los indicadores de disponibilidad mecánica, utilización efectiva, MTBF y MTTR, obteniendo que en los tres primeros es mayor en 0.51%, 10.45%, 4.8%, respectivamente; por el lado del indicador de MTTR, con un resultado de 12.52% menor que el equipo diésel. En el aspecto de sostenibilidad ambiental, cuando se tiene al equipo diésel en funcionamiento se incrementan los niveles de gases en comparación a los equipos a batería en 76.63% en CO, 72.45% en CO2, 50% en NO2; asimismo, en la zona de trabajo del equipo diésel, se genera un incremento de temperatura de 19°C mientras que con el equipo a batería se tiene un incremento de 3.5°C. Finalmente, en el aspecto económico, se determinó que los equipos a diésel poseen un menor costo de posesión de 35.28 $/hr, un mayor costo operacional de 31.24 $/hr y un mayor costo total, incluyendo costo ventilación, de 1.63 $/hr a diferencia del equipo a batería. / This research is titled "Proposal for the use of battery-powered LHD equipment as a competitive alternative to diesel equipment in the cleaning process of horizontal underground workings in a mechanized mining operation." This study aims to evaluate a proposal for the use of battery-powered LHD equipment as a competitive alternative to diesel equipment in the cleaning process of horizontal underground workings, determining the productive factors, analyzing environmental sustainability and evaluating the operational costs related to LHD equipment. diesel and battery. Likewise, for the comparative evaluation of these equipment, an analysis was carried out through a case study in a mechanized mining unit, with similar characteristics to the Atacocha Mining Unit. The equipment that will be used for the evaluation, under the same working conditions, are the battery-powered LHD (ST7), from Epiroc, and the diesel LHD (R 1300G), from Caterpillar; both with a rated payload capacity of 6,800 kg (4.2 yd3). The results obtained through the case study occurred at three levels: productive, environmentally sustainable and economic. In the productive aspect, it was determined that the battery-powered LHD equipment compared to the diesel-powered LHD equipment, for a hauling distance of 150 m, has a shorter cycle time of 8.23%, and a higher productive performance of 8.98%. In relation to the management indicators, it was determined that battery-powered LHD equipment has an advantage over diesel equipment as in the indicators of mechanical availability, effective use, MTBF and MTTR, obtaining that in the first three it is greater by 0.51% , 10.45%, 4.8%, respectively; on the side of the MTTR indicator, with a result of 12.52% lower than the diesel equipment. In the aspect of environmental sustainability, when the diesel equipment is in operation, the levels of gases are increased compared to battery-powered equipment by 76.63% in CO, 72.45% in CO2, 50% in NO2; Likewise, in the work area of the diesel equipment, a temperature increase of 19 °C is generated, while with the battery-powered equipment there is an increase of 3.5 °C. Finally, in the economic aspect, it was determined that diesel equipment has a lower cost of ownership of 35.28 $/hr, a higher operating cost of 31.24 $ /hr and a higher total cost, including ventilation cost, of 1.63 $/hr unlike battery powered equipment. / Tesis

Functional analyses have gained more interest as we have easier access to massive data sets. However, such data sets often contain large heterogeneities, noise, and dimensionalities. When generalizing the analyses from vectors to functions, classical methods might not work directly. This dissertation considers noisy information reduction in functional analyses from two perspectives: functional variable selection to reduce the dimensionality and functional clustering to group similar observations and thus reduce the sample size. The complicated data structures and relations can be easily modeled by a Bayesian hierarchical model, or developed from a more generic one by changing the prior distributions. Hence, this dissertation focuses on the development of Bayesian approaches for functional analyses due to their flexibilities. A nonparametric Bayesian approach, such as the Dirichlet process mixture (DPM) model, has a nonparametric distribution as the prior. This approach provides flexibility and reduces assumptions, especially for functional clustering, because the DPM model has an automatic clustering property, so the number of clusters does not need to be specified in advance. Furthermore, a weighted Dirichlet process mixture (WDPM) model allows for more heterogeneities from the data by assuming more than one unknown prior distribution. It also gathers more information from the data by introducing a weight function that assigns different candidate priors, such that the less similar observations are more separated. Thus, the WDPM model will improve the clustering and model estimation results. In this dissertation, we used an advanced nonparametric Bayesian approach to study functional variable selection and functional clustering methods. We proposed 1) a stochastic search functional selection method with application to 1-M matched case-crossover studies for aseptic meningitis, to examine the time-varying unknown relationship and find out important covariates affecting disease contractions; 2) a functional clustering method via the WDPM model, with application to three pathways related to genetic diabetes data, to identify essential genes distinguishing between normal and disease groups; and 3) a combined functional clustering, with the WDPM model, and variable selection approach with application to high-frequency spectral data, to select wavelengths associated with breast cancer racial disparities. / Doctor of Philosophy / As we have easier access to massive data sets, functional analyses have gained more interest to analyze data providing information about curves, surfaces, or others varying over a continuum. However, such data sets often contain large heterogeneities and noise. When generalizing the analyses from vectors to functions, classical methods might not work directly. This dissertation considers noisy information reduction in functional analyses from two perspectives: functional variable selection to reduce the dimensionality and functional clustering to group similar observations and thus reduce the sample size. The complicated data structures and relations can be easily modeled by a Bayesian hierarchical model due to its flexibility. Hence, this dissertation focuses on the development of nonparametric Bayesian approaches for functional analyses. Our proposed methods can be applied in various applications: the epidemiological studies on aseptic meningitis with clustered binary data, the genetic diabetes data, and breast cancer racial disparities.

Multi functional health monitoring wearable devices are quite prominent these days. Usually these devices are battery-operated and consequently are limited by their battery life (from few hours to a few weeks depending on the application). Of late, it was realized that these devices, which are currently being operated at fixed voltage and frequency, are capable of operating at multiple voltages and frequencies. By switching these voltages and frequencies to lower values based upon power requirements, these devices can achieve tremendous benefits in the form of energy savings. Dynamic Voltage and Frequency Scaling (DVFS) techniques have proven to be handy in this situation for an efficient trade-off between energy and timely behavior. Within imec, wearable devices make use of the indigenously developed MUSEIC v2 (Multi Sensor Integrated circuit version 2.0). This system is optimized for efficient and accurate collection, processing, and transfer of data from multiple (health) sensors. MUSEIC v2 has limited means in controlling the voltage and frequency dynamically. In this thesis we explore how traditional DVFS techniques can be applied to the MUSEIC v2. Experiments were conducted to find out the optimum power modes to efficiently operate and also to scale up-down the supply voltage and frequency. Considering the overhead caused when switching voltage and frequency, transition analysis was also done. Real-time and non real-time benchmarks were implemented based on these techniques and their performance results were obtained and analyzed. In this process, several state of the art scheduling algorithms and scaling techniques were reviewed in identifying a suitable technique. Using our proposed scaling technique implementation, we have achieved 86.95% power reduction in average, in contrast to the conventional way of the MUSEIC v2 chip’s processor operating at a fixed voltage and frequency. Techniques that include light sleep and deep sleep mode were also studied and implemented, which tested the system’s capability in accommodating Dynamic Power Management (DPM) techniques that can achieve greater benefits. A novel approach for implementing the deep sleep mechanism was also proposed and found that it can obtain up to 71.54% power savings, when compared to a traditional way of executing deep sleep mode. / Nuförtiden så har multifunktionella bärbara hälsoenheter fått en betydande roll. Dessa enheter drivs vanligtvis av batterier och är därför begränsade av batteritiden (från ett par timmar till ett par veckor beroende på tillämpningen). På senaste tiden har det framkommit att dessa enheter som används vid en fast spänning och frekvens kan användas vid flera spänningar och frekvenser. Genom att byta till lägre spänning och frekvens på grund av effektbehov så kan enheterna få enorma fördelar när det kommer till energibesparing. Dynamisk skalning av spänning och frekvens-tekniker (såkallad Dynamic Voltage and Frequency Scaling, DVFS) har visat sig vara användbara i detta sammanhang för en effektiv avvägning mellan energi och beteende. Hos Imec så använder sig bärbara enheter av den internt utvecklade MUSEIC v2 (Multi Sensor Integrated circuit version 2.0). Systemet är optimerat för effektiv och korrekt insamling, bearbetning och överföring av data från flera (hälso) sensorer. MUSEIC v2 har begränsad möjlighet att styra spänningen och frekvensen dynamiskt. I detta examensarbete undersöker vi hur traditionella DVFS-tekniker kan appliceras på MUSEIC v2. Experiment utfördes för att ta reda på de optimala effektlägena och för att effektivt kunna styra och även skala upp matningsspänningen och frekvensen. Eftersom att ”overhead” skapades vid växling av spänning och frekvens gjordes också en övergångsanalys. Realtidsoch icke-realtidskalkyler genomfördes baserat på dessa tekniker och resultaten sammanställdes och analyserades. I denna process granskades flera toppmoderna schemaläggningsalgoritmer och skalningstekniker för att hitta en lämplig teknik. Genom att använda vår föreslagna skalningsteknikimplementering har vi uppnått 86,95% effektreduktion i jämförelse med det konventionella sättet att MUSEIC v2-chipets processor arbetar med en fast spänning och frekvens. Tekniker som inkluderar lätt sömn och djupt sömnläge studerades och implementerades, vilket testade systemets förmåga att tillgodose DPM-tekniker (Dynamic Power Management) som kan uppnå ännu större fördelar. En ny metod för att genomföra den djupa sömnmekanismen föreslogs också och enligt erhållna resultat så kan den ge upp till 71,54% lägre energiförbrukning jämfört med det traditionella sättet att implementera djupt sömnläge.