Modélisation d'éléments finis pour les interactions fluide-structure

Cette thèse concerne la modélisation des interactions fluide-structure et les méthodes numériques qui s’y rattachent. De ce fait, la thèse est divisée en deux parties. La première partie concerne l’étude des interactions fluide-structure par la méthode des domaines fictifs. Dans cette contribution, le fluide est incompressible et laminaire et la structure est considérée rigide, qu’elle soit immobile ou en mouvement. Les outils que nous avons développés comportent la mise en oeuvre d’un algorithme fiable de résolution qui intégrera les deux domaines (fluide et solide) dans une formulation mixte. L’algorithme est basé sur des techniques de raffinement local adaptatif des maillages utilisés permettant de mieux séparer les éléments du milieu fluide de ceux du solide que ce soit en 2D ou en 3D. La seconde partie est l’étude des interactions mécaniques entre une structure flexible et un fluide incompressible. Dans cette contribution, nous proposons et analysons des méthodes numériques partitionnées pour la simulation de phénomènes d’interaction fluide-structure (IFS). Nous avons adopté à cet effet, la méthode dite «arbitrary Lagrangian-Eulerian» (ALE). La résolution fluide est effectuée itérativement à l’aide d’un schéma de type projection et la structure est modélisée par des modèles hyper élastiques en grandes déformations. Nous avons développé de nouvelles méthodes de mouvement de maillages pour aboutir à de grandes déformations de la structure. Enfin, une stratégie de complexification du problème d’IFS a été définie. La modélisation de la turbulence et des écoulements à surfaces libres ont été introduites et couplées à la résolution des équations de Navier-Stokes. Différentes simulations numériques sont présentées pour illustrer l’efficacité et la robustesse de l’algorithme. Les résultats numériques présentés attestent de la validité et l’efficacité des méthodes numériques développées.

Air Pollution and High Density Lipoprotein Structure and Function

Thesis (Ph.D.)--University of Washington, 2016-06

Dynamic fluid-structure interactions using finite volume unstructured mesh procedures

A three-dimensional finite volume, unstructured mesh (FV-UM) method for dynamic fluid–structure interaction (DFSI) is described. Fluid structure interaction, as applied to flexible structures, has wide application in diverse areas such as flutter in aircraft, wind response of buildings, flows in elastic pipes and blood vessels. It involves the coupling of fluid flow and structural mechanics, two fields that are conventionally modelled using two dissimilar methods, thus a single comprehensive computational model of both phenomena is a considerable challenge. Until recently work in this area focused on one phenomenon and represented the behaviour of the other more simply. More recently, strategies for solving the full coupling between the fluid and solid mechanics behaviour have been developed. A key contribution has been made by Farhat et al. [Int. J. Numer. Meth. Fluids 21 (1995) 807] employing FV-UM methods for solving the Euler flow equations and a conventional finite element method for the elastic solid mechanics and the spring based mesh procedure of Batina [AIAA paper 0115, 1989] for mesh movement. In this paper, we describe an approach which broadly exploits the three field strategy described by Farhat for fluid flow, structural dynamics and mesh movement but, in the context of DFSI, contains a number of novel features: a single mesh covering the entire domain, a Navier–Stokes flow, a single FV-UM discretisation approach for both the flow and solid mechanics procedures, an implicit predictor–corrector version of the Newmark algorithm, a single code embedding the whole strategy.

A bio-coke for anode production and the manufacturing method thereof

Dans l’industrie de l’aluminium, le coke de pétrole calciné est considéré comme étant le composant principal de l’anode. Une diminution dans la qualité du coke de pétrole a été observée suite à une augmentation de sa concentration en impuretés. Cela est très important pour les alumineries car ces impuretés, en plus d’avoir un effet réducteur sur la performance des anodes, contaminent le métal produit. Le coke de pétrole est aussi une source de carbone fossile et, durant sa consommation, lors du processus d’électrolyse, il y a production de CO2. Ce dernier est considéré comme un gaz à effet de serre et il est bien connu pour son rôle dans le réchauffement planétaire et aussi dans les changements climatiques. Le charbon de bois est disponible et est produit mondialement en grande quantité. Il pourrait être une alternative attrayante pour le coke de pétrole dans la fabrication des anodes de carbone utilisées dans les cuves d’électrolyse pour la production de l’aluminium. Toutefois, puisqu’il ne répond pas aux critères de fabrication des anodes, son utilisation représente donc un grand défi. En effet, ses principaux désavantages connus sont sa grande porosité, sa structure désordonnée et son haut taux de minéraux. De plus, sa densité et sa conductivité électrique ont été rapportées comme étant inférieures à celles du coke de pétrole. L’objectif de ce travail est d’explorer l’effet du traitement de chaleur sur les propriétés du charbon de bois et cela, dans le but de trouver celles qui s’approchent le plus des spécifications requises pour la production des anodes. L’évolution de la structure du charbon de bois calciné à haute température a été suivie à l’aide de différentes techniques. La réduction de son contenu en minéraux a été obtenue suite à des traitements avec de l’acide chlorhydrique utilisé à différentes concentrations. Finalement, différentes combinaisons de ces deux traitements, calcination et lixiviation, ont été essayées dans le but de trouver les meilleures conditions de traitement.

Simplified design method for energy dissipating devices in retrofitting of seismically isolated bridges

Abstract: Highway bridges have great values in a country because in case of any natural disaster they may serve as lines to save people’s lives. Being vulnerable under significant seismic loads, different methods can be considered to design resistant highway bridges and rehabilitate the existing ones. In this study, base isolation has been considered as one efficient method in this regards which in some cases reduces significantly the seismic load effects on the structure. By reducing the ductility demand on the structure without a notable increase of strength, the structure is designed to remain elastic under seismic loads. The problem associated with the isolated bridges, especially with elastomeric bearings, can be their excessive displacements under service and seismic loads. This can defy the purpose of using elastomeric bearings for small to medium span typical bridges where expansion joints and clearances may result in significant increase of initial and maintenance cost. Thus, supplementing the structure with dampers with some stiffness can serve as a solution which in turn, however, may increase the structure base shear. The main objective of this thesis is to provide a simplified method for the evaluation of optimal parameters for dampers in isolated bridges. Firstly, performing a parametric study, some directions are given for the use of simple isolation devices such as elastomeric bearings to rehabilitate existing bridges with high importance. Parameters like geometry of the bridge, code provisions and the type of soil on which the structure is constructed have been introduced to a typical two span bridge. It is concluded that the stiffness of the substructure, soil type and special provisions in the code can determine the employment of base isolation for retrofitting of bridges. Secondly, based on the elastic response coefficient of isolated bridges, a simplified design method of dampers for seismically isolated regular highway bridges has been presented in this study. By setting objectives for reduction of displacement and base shear variation, the required stiffness and damping of a hysteretic damper can be determined. By modelling a typical two span bridge, numerical analyses have followed to verify the effectiveness of the method. The method has been used to identify equivalent linear parameters and subsequently, nonlinear parameters of hysteretic damper for various designated scenarios of displacement and base shear requirements. Comparison of the results of the nonlinear numerical model without damper and with damper has shown that the method is sufficiently accurate. Finally, an innovative and simple hysteretic steel damper was designed. Five specimens were fabricated from two steel grades and were tested accompanying a real scale elastomeric isolator in the structural laboratory of the Université de Sherbrooke. The test procedure was to characterize the specimens by cyclic displacement controlled tests and subsequently to test them by real-time dynamic substructuring (RTDS) method. The test results were then used to establish a numerical model of the system which went through nonlinear time history analyses under several earthquakes. The outcome of the experimental and numerical showed an acceptable conformity with the simplified method.

Modelling of water removal during a paper vacuum dewatering process using a Level-Set method

Water removal in paper manufacturing is an energy-intensive process. The dewatering process generally consists of four stages of which the first three stages include mechanical water removal through gravity filtration, vacuum dewatering and wet pressing. In the fourth stage, water is removed thermally, which is the most expensive stage in terms of energy use. In order to analyse water removal during a vacuum dewatering process, a numerical model was created by using a Level-Set method. Various different 2D structures of the paper model were created in MATLAB code with randomly positioned circular fibres with identical orientation. The model considers the influence of the forming fabric which supports the paper sheet during the dewatering process, by using volume forces to represent flow resistance in the momentum equation. The models were used to estimate the dry content of the porous structure for various dwell times. The relation between dry content and dwell time was compared to laboratory data for paper sheets with basis weights of 20 and 50 g/m2 exposed to vacuum levels between 20 kPa and 60 kPa. The comparison showed reasonable results for dewatering and air flow rates. The random positioning of the fibres influences the dewatering rate slightly. In order to achieve more accurate comparisons, the random orientation of the fibres needs to be considered, as well as the deformation and displacement of the fibres during the dewatering process.

Are coach education programmes the most effective method for coach development?

The purpose of this study is to establish whether coaches from a multi-sport context develop most effectively through coach education programmes and whether formal learning is fostering coach effectiveness. A sample of eight qualified male multi-sports’ coaches participated with an age range of 24 to 52 years (M = 32.6, ± = 8.9) and 9 to 18 years coaching experience (M = 12.6, ± = 3.8). Qualitative semi structured interviews were employed, lasting approximately 30 to 60 minutes. The data then underwent a thematic analysis process reducing the data into six overarching themes: values of the coach; the coach’s role on athlete development; forms of learning; barriers regarding coach education; role of governing bodies; coaches career pathway. The findings of the study indicated coaches access a wide range of sources to enhance their practice, but informal learning was preferred (interacting with other coaches and learning by doing). This resulted from numerous barriers experienced surrounding the delivery, cost and access to coach education programmes preventing coaches from progressing through the pathway. However, coaches in the study feel coach education should be a mandatory process for every coach. The findings have implications for policymakers and sport organisations in developing their coach education structure.

Numerical analysis of the modal coupling at low resonances in a Colombian Andean Bandola in C using the finite element method

The work presented in this thesis is concerned with the dynamical behavior of a CBandola's acoustical box at low resonances -- Two models consisting of two and three coupled oscillators are proposed in order to analyse the response at the first two and three resonances, respectively -- These models describe the first resonances in a bandola as a combination of the lowest modes of vibration of enclosed air, top and back plates -- Physically, the coupling between these elements is caused by the fluid-structure interaction that gives rise to coupled modes of vibration for the assembled resonance box -- In this sense, the coupling in the models is expressed in terms of the ratio of effective areas and masses of the elements which is an useful parameter to control the coupling -- Numerical models are developed for the analysis of modal coupling which is performed using the Finite Element Method -- First, it is analysed the modal behavior of separate elements: enclosed air, top plate and back plate -- This step is important to identify participating modes in the coupling -- Then, a numerical model of the resonance box is used to compute the coupled modes -- The computation of normal modes of vibration was executed in the frequency range of 0-800Hz -- Although the introduced models of coupled oscillators only predict maximum the first three resonances, they also allow to study qualitatively the coupling between the rest of the computed modes in the range -- Considering that dynamic response of a structure can be described in terms of the modal parameters, this work represents, in a good approach, the basic behavior of a CBandola, although experimental measurements are suggested as further work to verify the obtained results and get more information about some characteristics of the coupled modes, for instance, the phase of vibration of the air mode and the radiation e ciency

Relating Innovative 2D Ideas into 3D Garments in Terms of Structure, Using ‘Sculptural Form Giving’ as an Intermediate Step in Creation.

This research focuses on finding a fashion design methodology to reliably translate innovative two-dimensional ideas on paper, via a structural design sculpture, into an intermediate model. The author, both as a fashion designer and a researcher, has witnessed the issues which arise, regarding the loss of some of the initial ideas and distortion during the two-dimensional creative sketch to three-dimensional garment transfer process. Therefore, this research is concerned with fashion designers engaged in transferring a two-dimensional sketch through the method ‘sculptural form giving’. This research method applies the ideal model of conceptual sculpture, in the fashion design process, akin to those used in the disciplines of architecture. These parallel design disciplines share similar processes for realizing design ideas. Moreover, this research investigates and formalizes the processes that utilize the measurable space between the garment and the body, to help transfer garment variation and scale. In summation, this research proposition focuses on helping fashion designers to produce a creative method that helps the designer transfer their imaginative concept through intermediate modeling.

Security and Trust in Distributed Computation

We propose three research problems to explore the relations between trust and security in the setting of distributed computation. In the first problem, we study trust-based adversary detection in distributed consensus computation. The adversaries we consider behave arbitrarily disobeying the consensus protocol. We propose a trust-based consensus algorithm with local and global trust evaluations. The algorithm can be abstracted using a two-layer structure with the top layer running a trust-based consensus algorithm and the bottom layer as a subroutine executing a global trust update scheme. We utilize a set of pre-trusted nodes, headers, to propagate local trust opinions throughout the network. This two-layer framework is flexible in that it can be easily extensible to contain more complicated decision rules, and global trust schemes. The first problem assumes that normal nodes are homogeneous, i.e. it is guaranteed that a normal node always behaves as it is programmed. In the second and third problems however, we assume that nodes are heterogeneous, i.e, given a task, the probability that a node generates a correct answer varies from node to node. The adversaries considered in these two problems are workers from the open crowd who are either investing little efforts in the tasks assigned to them or intentionally give wrong answers to questions. In the second part of the thesis, we consider a typical crowdsourcing task that aggregates input from multiple workers as a problem in information fusion. To cope with the issue of noisy and sometimes malicious input from workers, trust is used to model workers' expertise. In a multi-domain knowledge learning task, however, using scalar-valued trust to model a worker's performance is not sufficient to reflect the worker's trustworthiness in each of the domains. To address this issue, we propose a probabilistic model to jointly infer multi-dimensional trust of workers, multi-domain properties of questions, and true labels of questions. Our model is very flexible and extensible to incorporate metadata associated with questions. To show that, we further propose two extended models, one of which handles input tasks with real-valued features and the other handles tasks with text features by incorporating topic models. Our models can effectively recover trust vectors of workers, which can be very useful in task assignment adaptive to workers' trust in the future. These results can be applied for fusion of information from multiple data sources like sensors, human input, machine learning results, or a hybrid of them. In the second subproblem, we address crowdsourcing with adversaries under logical constraints. We observe that questions are often not independent in real life applications. Instead, there are logical relations between them. Similarly, workers that provide answers are not independent of each other either. Answers given by workers with similar attributes tend to be correlated. Therefore, we propose a novel unified graphical model consisting of two layers. The top layer encodes domain knowledge which allows users to express logical relations using first-order logic rules and the bottom layer encodes a traditional crowdsourcing graphical model. Our model can be seen as a generalized probabilistic soft logic framework that encodes both logical relations and probabilistic dependencies. To solve the collective inference problem efficiently, we have devised a scalable joint inference algorithm based on the alternating direction method of multipliers. The third part of the thesis considers the problem of optimal assignment under budget constraints when workers are unreliable and sometimes malicious. In a real crowdsourcing market, each answer obtained from a worker incurs cost. The cost is associated with both the level of trustworthiness of workers and the difficulty of tasks. Typically, access to expert-level (more trustworthy) workers is more expensive than to average crowd and completion of a challenging task is more costly than a click-away question. In this problem, we address the problem of optimal assignment of heterogeneous tasks to workers of varying trust levels with budget constraints. Specifically, we design a trust-aware task allocation algorithm that takes as inputs the estimated trust of workers and pre-set budget, and outputs the optimal assignment of tasks to workers. We derive the bound of total error probability that relates to budget, trustworthiness of crowds, and costs of obtaining labels from crowds naturally. Higher budget, more trustworthy crowds, and less costly jobs result in a lower theoretical bound. Our allocation scheme does not depend on the specific design of the trust evaluation component. Therefore, it can be combined with generic trust evaluation algorithms.

Capital structure of Finnish firms – Estimating the effects of firm characteristics on capital structure

One of the most disputable matters in the theory of finance has been the theory of capital structure. The seminal contributions of Modigliani and Miller (1958, 1963) gave rise to a multitude of studies and debates. Since the initial spark, the financial literature has offered two competing theories of financing decision: the trade-off theory and the pecking order theory. The trade-off theory suggests that firms have an optimal capital structure balancing the benefits and costs of debt. The pecking order theory approaches the firm capital structure from information asymmetry perspective and assumes a hierarchy of financing, with firms using first internal funds, followed by debt and as a last resort equity. This thesis analyses the trade-off and pecking order theories and their predictions on a panel data consisting 78 Finnish firms listed on the OMX Helsinki stock exchange. Estimations are performed for the period 2003–2012. The data is collected from Datastream system and consists of financial statement data. A number of capital structure characteristics are identified: firm size, profitability, firm growth opportunities, risk, asset tangibility and taxes, speed of adjustment and financial deficit. A regression analysis is used to examine the effects of the firm characteristics on capitals structure. The regression models were formed based on the relevant theories. The general capital structure model is estimated with fixed effects estimator. Additionally, dynamic models play an important role in several areas of corporate finance, but with the combination of fixed effects and lagged dependent variables the model estimation is more complicated. A dynamic partial adjustment model is estimated using Arellano and Bond (1991) first-differencing generalized method of moments, the ordinary least squares and fixed effects estimators. The results for Finnish listed firms show support for the predictions of profitability, firm size and non-debt tax shields. However, no conclusive support for the pecking-order theory is found. However, the effect of pecking order cannot be fully ignored and it is concluded that instead of being substitutes the trade-off and pecking order theory appear to complement each other. For the partial adjustment model the results show that Finnish listed firms adjust towards their target capital structure with a speed of 29% a year using book debt ratio.

Effect of soil structure on temporal and spatial dynamics of bacteria

Soil is a complex heterogeneous system comprising of highly variable and dynamic micro-habitats that have significant impacts on the growth and activity of resident microbiota. A question addressed in this research is how soil structure affects the temporal dynamics and spatial distribution of bacteria. Using repacked microcosms, the effect of bulk-density, aggregate sizes and water content on growth and distribution of introduced Pseudomonas fluorescens and Bacillus subtilis bacteria was determined. Soil bulk-density and aggregate sizes were altered to manipulate the characteristics of the pore volume where bacteria reside and through which distribution of solutes and nutrients is controlled. X-ray CT was used to characterise the pore geometry of repacked soil microcosms. Soil porosity, connectivity and soil-pore interface area declined with increasing bulk-density. In samples that differ in pore geometry, its effect on growth and extent of spread of introduced bacteria was investigated. The growth rate of bacteria reduced with increasing bulk-density, consistent with a significant difference in pore geometry. To measure the ability of bacteria to spread thorough soil, placement experiments were developed. Bacteria were capable of spreading several cm’s through soil. The extent of spread of bacteria was faster and further in soil with larger and better connected pore volumes. To study the spatial distribution in detail, a methodology was developed where a combination of X-ray microtopography, to characterize the soil structure, and fluorescence microscopy, to visualize and quantify bacteria in soil sections was used. The influence of pore characteristics on distribution of bacteria was analysed at macro- and microscales. Soil porosity, connectivity and soil-pore interface influenced bacterial distribution only at the macroscale. The method developed was applied to investigate the effect of soil pore characteristics on the extent of spread of bacteria introduced locally towards a C source in soil. Soil-pore interface influenced spread of bacteria and colonization, therefore higher bacterial densities were found in soil with higher pore volumes. Therefore the results in this showed that pore geometry affects the growth and spread of bacteria in soil. The method developed showed showed how thin sectioning technique can be combined with 3D X-ray CT to visualize bacterial colonization of a 3D pore volume. This novel combination of methods is a significant step towards a full mechanistic understanding of microbial dynamics in structured soils.

Essays on the term structure of interest rates

This PhD thesis contains three main chapters on macro finance, with a focus on the term structure of interest rates and the applications of state-of-the-art Bayesian econometrics. Except for Chapter 1 and Chapter 5, which set out the general introduction and conclusion, each of the chapters can be considered as a standalone piece of work. In Chapter 2, we model and predict the term structure of US interest rates in a data rich environment. We allow the model dimension and parameters to change over time, accounting for model uncertainty and sudden structural changes. The proposed timevarying parameter Nelson-Siegel Dynamic Model Averaging (DMA) predicts yields better than standard benchmarks. DMA performs better since it incorporates more macro-finance information during recessions. The proposed method allows us to estimate plausible realtime term premia, whose countercyclicality weakened during the financial crisis. Chapter 3 investigates global term structure dynamics using a Bayesian hierarchical factor model augmented with macroeconomic fundamentals. More than half of the variation in the bond yields of seven advanced economies is due to global co-movement. Our results suggest that global inflation is the most important factor among global macro fundamentals. Non-fundamental factors are essential in driving global co-movements, and are closely related to sentiment and economic uncertainty. Lastly, we analyze asymmetric spillovers in global bond markets connected to diverging monetary policies. Chapter 4 proposes a no-arbitrage framework of term structure modeling with learning and model uncertainty. The representative agent considers parameter instability, as well as the uncertainty in learning speed and model restrictions. The empirical evidence shows that apart from observational variance, parameter instability is the dominant source of predictive variance when compared with uncertainty in learning speed or model restrictions. When accounting for ambiguity aversion, the out-of-sample predictability of excess returns implied by the learning model can be translated into significant and consistent economic gains over the Expectations Hypothesis benchmark.

Comics as a literary-didactic method and their use for reducing gender differences in reading literacy at the primary level of education

The aim of the present study was to examine the effect of the systematic use of comics as a literary-didactic method to reduce gender differences in reading literacy and reading motivation at the primary level of education. It was assumed that the use of comics would have a positive effect on pupils’ reading literacy and reading motivation, while also reducing the aforementioned differences between boys and girls. The dimensions of reading literacy and reading motivation were examined in experimental and control groups, before and after the intervention, by means of questionnaires and tests for pupils. The sample consisted of 143 pupils from second to fifth grade from two Slovenian primary schools in a rural environment, of which 73 pupils participated in the experimental group and 70 pupils represented the control group. Effects of the use of comics as a literary-didactic method were not found: using comics as a literary-didactic method did not have a statistically significant effect on pupils’ reading literacy and reading motivation. However, when the four-way structure of the research (taking into account the age and gender of the pupils) was considered, some subgroups showed a statistically significant increase in reading interest and attitude towards reading. No reduction of gender differences in reading literacy and reading motivation was found. Based on the results, guidelines for further research are established and suggestions are offered for teachers’ work. (DIPF/Orig.)

Essays on the term structure of interest rates

This PhD thesis contains three main chapters on macro finance, with a focus on the term structure of interest rates and the applications of state-of-the-art Bayesian econometrics. Except for Chapter 1 and Chapter 5, which set out the general introduction and conclusion, each of the chapters can be considered as a standalone piece of work. In Chapter 2, we model and predict the term structure of US interest rates in a data rich environment. We allow the model dimension and parameters to change over time, accounting for model uncertainty and sudden structural changes. The proposed time-varying parameter Nelson-Siegel Dynamic Model Averaging (DMA) predicts yields better than standard benchmarks. DMA performs better since it incorporates more macro-finance information during recessions. The proposed method allows us to estimate plausible real-time term premia, whose countercyclicality weakened during the financial crisis. Chapter 3 investigates global term structure dynamics using a Bayesian hierarchical factor model augmented with macroeconomic fundamentals. More than half of the variation in the bond yields of seven advanced economies is due to global co-movement. Our results suggest that global inflation is the most important factor among global macro fundamentals. Non-fundamental factors are essential in driving global co-movements, and are closely related to sentiment and economic uncertainty. Lastly, we analyze asymmetric spillovers in global bond markets connected to diverging monetary policies. Chapter 4 proposes a no-arbitrage framework of term structure modeling with learning and model uncertainty. The representative agent considers parameter instability, as well as the uncertainty in learning speed and model restrictions. The empirical evidence shows that apart from observational variance, parameter instability is the dominant source of predictive variance when compared with uncertainty in learning speed or model restrictions. When accounting for ambiguity aversion, the out-of-sample predictability of excess returns implied by the learning model can be translated into significant and consistent economic gains over the Expectations Hypothesis benchmark.