Cette thèse comporte deux parties. Dans un premier temps, nous allons faire un lien entre des solutions stationnaires de problèmes d'évolutions de frontières par courbure moyenne avec des champs extérieurs et l'existence de minimiseur globaux d'un problème de minimisation de périmètre avec une énergie. Ces solutions stationnaires permettent en outre de fournir des bornes pour les solutions non stationnaires du problème. De plus, en modifiant l'énergie, on montre que les résolutions successives des problème de périmètre permettent de calculer l'évolution d'un ensemble par courbure moyenne. Enfin, on présentera un algorithme permettant de calculer les solutions de viscosité d'un problème de Dirichlet portant sur le Laplacien Infini grâce aux équations d'Aronsson.

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Historically turbulence modeling has been sharply divided into Reynolds averaged Navier-Stokes (RANS), in which all the turbulent scales of motion are modeled, and large-eddy simulation (LES), in which only a portion of the turbulent spectrum is modeled. In recent years there have been numerous attempts to couple these two approaches either by patching RANS and LES calculations together (zonal methods) or by blending the two sets of equations. In order to create a proper bridging model, that is, a single set of equations which captures both RANS and LES like behavior, it is necessary to place both RANS and LES in a more general framework.The goal of the current work is threefold: to provide such a framework, to demonstrate how the Flow Simulation Methodology (FSM) fits into this framework, and to evaluate the strengths and weaknesses of the current version of the FSM. To do this, first a set of filtered Navier-Stokes (FNS) equations are introduced in terms of an arbitrary generalized filter. Additional exact equations are given for the second order moments and the generalized subfilted dissipation rate tensor. This is followed by a discussion of the role of implicit and explicit filters in turbulence modeling.The FSM is then described with particular attention to its role as a bridging model. In order to evaluate the method a specific implementation of the FSM approach is proposed. Simulations are presented using this model for the case of separating flow over a "hump" with and without flow control. Careful attention is paid to error estimation, and, in particular, how using flow statistics and time series affects the error analysis. Both mean flow and Reynolds stress profiles are presented, as well as the phase averaged turbulent structures and wall pressure spectra. Using the phase averaged data it is possible to examine how the FSM partitions the energy between the coherent resolved scale motions, the random resolved scale fluctuations, and the subfilter quantities.The method proves to be qualitatively successful at reproducing large turbulent structures. However, like other hybrid methods, it has difficulty in the region where the model behavior transitions from RANS to LES> Consequently the phase averaged structures reproduce the experiments quite well, and the forcing does significantly reduce the length of the separated region. Nevertheless, the recirculation length is signficantly too large for all cases.Overall the current results demonstrate the promise of bridging models in general and the FSM in particular. However, current bridging techniques are still in their infancy. There is still important progress to be made and it is hoped that this work points out the more important avenues for exploration.

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L'objectif de cette thèse est d'étudier, sous divers aspects, le processus de formation d'amyloide à partir de la polymérisation de protéines. Ces phénomènes, aussi bien in vitro que in vivo, posent des questions de modélisation mathématique. Il s'agit ensuite de conduire une analyse des modèles obtenus. Dans la première partie nous présentons des travaux effectués en collaboration avec une équipe de biologistes. Deux modèles sont introduits, basés sur la théorie en vigueur du phénomène Prions, que nous ajustons aux conditions expérimentales. Ces modèles nous permettent d'analyser les données obtenues à partir d'expériences conduites en labora- toire. Cependant celles-ci soulèvent certains phénomènes encore inexpliqués par la théorie actuelle. Nous proposons donc un autre modèle qui corrobore les données et donne une nouvelle approche de la formation d'amyloide dans le cas du Prion. Nous terminons cette partie par l'analyse mathématique de ce système composé d'une infinité d'équations dif- férentielles. Ce dernier consiste en un couplage entre un système de type Becker-Döring et un système de polymérisation-fragmentation discrète. La seconde partie s'attache à l'analyse d'un nouveau modèle pour la polymérisation de protéines dont la fragmentation est sujette aux variations du fluide environnant. L'idée est de décrire au plus près les conditions expérimentales mais aussi d'introduire de nou- velles quantités macroscopiques mesurables pour l'étude de la polymérisation. Le premier chapitre de cette partie présente une description stochastique du problème. On y établit les équations du mouvement des polymères et des monomères (de type Langevin) ainsi que le formalisme pour l'étude du problème limite en grand nombre. Le deuxième chapitre pose le cadre fonctionnel et l'existence de solutions pour l'équation de Fokker-Planck- Smoluchowski décrivant la densité de configuration des polymères, elle-même couplée à une équation de diffusion pour les monomères. Le dernier chapitre propose une méthode numérique pour traiter ce problème. On s'intéresse dans la dernière partie à la modélisation de la maladie d'Alzheimer. On construit un modèle qui décrit d'une part la formation de plaque amyloide in vivo, et d'autre part les interactions entre les oligomères d'Aβet la protéine prion qui induiraient la perte de mémoire. On mène l'analyse mathématique de ce modèle dans un cas particulier puis dans un cas plus général où le taux de polymérisation est une loi de puissance.

The goal of this thesis is to develop more effective quantitative procedures to evaluate the stability of deteriorated metal culverts and a better understanding of the deteriorated culverts' behaviour through non-destructive testing, full scale experiments and numerical analyses. First, three design cases were examined using numerical analysis to study the effects of corrosion, burial depth and staged construction on the capacity of deteriorated steel culverts. Then, a method to measure the remaining wall thickness of two 1.8 m diameter corroded metal culverts using ultrasonic device was developed. Both culverts were then buried in the test pit at Queen's University and tested under nominal and working vehicle loads at 0.9m cover and 0.6m cover. The more heavily corroded structure (CSP1) was tested up to its ultimate limit state, inducing local bending across the crown, as well as local buckling of the remnants of the corrugated steel wall between perforations at the haunches. The results suggest that the single axle pads interact to influence the culvert's behaviour despite the shallow cover used in these experiments. CSP1 was able to carry the working load and did not fail until reaching 340 kN, which was equal to 90% of the fully factored load. The experiment suggests that less deteriorated metal culverts (as compared to CSP1) may have the required capacity. Two finite element packages, CANDE and ABAQUS, were used to perform the numerical investigation and the AASHTO and CHBDC approaches were then used to calculate the thrust force in the culverts. Although the numerical analysis produced conservative values for the thrust forces, it failed to capture the non-linear behaviour of both specimens in the experiments. Both the AASHTO and the CHBDC approaches produced unconservative thrust forces compared to experimental results while numerical analysis using Moore's spreading factor produced the most conservative results in terms of thrust. The analysis suggests that CANDE could be used to predict thrust forces in less deteriorated metal culverts. A procedure to assess the stability of deteriorated corrugated metal culverts based on quantitative data was developed using the numerical analysis and experimental results. / Thesis (Master, Civil Engineering) -- Queen's University, 2013-01-30 12:56:17.945

By considering Krylov subspace methods in nonstandard inner products, we develop in this thesis new methods for solving large sparse linear systems and examine the effectiveness of existing preconditioners. We focus on saddle point systems and systems with a nonsymmetric, diagonalizable coefficient matrix. For symmetric saddle point systems, we present a preconditioner that renders the preconditioned saddle point matrix nonsymmetric but self-adjoint with respect to an inner product and for which scaling is not required to apply a short-term recurrence method. The robustness and effectiveness of this preconditioner, when applied to a number of test problems, is demonstrated. We additionally utilize combination preconditioning (Stoll and Wathen. SIAM J. Matrix Anal. Appl. 2008; 30:582-608) to develop three new combination preconditioners. One of these is formed from two preconditioners for which only a MINRES-type method can be applied, and yet a conjugate-gradient type method can be applied to the combination preconditioned system. Numerical experiments show that application of these preconditioners can result in faster convergence. When the coefficient matrix is diagonalizable, but potentially nonsymmetric, we present conditions under which the pseudospectra of a preconditioner and coefficient matrix are identical and characterize the pseudospectra when this condition is not exactly fulfilled. We show that when the preconditioner and coefficient matrix are self-adjoint with respect to nearby symmetric bilinear forms the convergence of a particular minimum residual method is bounded by a term that depends on the spectrum of the preconditioned coefficient matrix and a constant that is small when the symmetric bilinear forms are close. An iteration-dependent bound for GMRES in the Euclidean inner product is presented that shows precisely why a standard bound can be pessimistic. We observe that for certain problems known, effective preconditioners are either self-adjoint with respect to the same symmetric bilinear form as the coefficient matrix or one that is nearby.

The aim of this work is to provide a mathematical and numerical analysis of the static quasicontinuum (QC) method. The QC method is, in essence, a finite element method for atomistic material models. By restricting the set of admissible deformations to linear splines with respect to a finite element mesh, the computational complexity of atomistic material models is reduced considerably. We begin with a general review of atomistic material models and the QC method and, most importantly, a thorough discussion of the correct concept of static equilibrium. For example, it is shown that, in contrast to global energy minimization, a ‘dynamic’ selection procedure based on gradient flows models the physically correct behaviour. Next, an atomistic model with long-range Lennard–Jones type interactions is analyzed in one dimension. A rigorous demonstration is given for the existence and stability of elastic as well as fractured steady states, and it is shown that they can be approximated by a QC method if the mesh is sufficiently well adapted to the exact solution; this can be measured by the interpolation error. While the a priori error analysis is an important theoretical step for understanding the approximation properties of the QC method, it is in general unclear how to compute the QC deformation whose existence is guaranteed by the a priori analysis. An a posteriori analysis is therefore performed as well. It is shown that, if a computed QC deformation is stable and has a sufficiently small residual, then there exists a nearby exact solution and the error is estimated. This a posteriori existence idea is also analyzed in an abstract setting. Finally, extensions of the ideas to higher dimensions are investigated in detail.

We present a unifying framework for the global optimization of functions which are expensive to evaluate. The framework is based on a Bayesian interpretation of radial basis function interpolation which incorporates existing methods such as Kriging, Gaussian process regression and neural networks. This viewpoint enables the application of Bayesian decision theory to derive a sequential global optimization algorithm which can be extended to include existing algorithms of this type in the literature. By posing the optimization problem as a sequence of sampling decisions, we optimize a general cost function at each stage of the algorithm. An extension to multi-stage decision processes is also discussed. The key idea of the framework is to replace the underlying expensive function by a cheap surrogate approximation. This enables the use of existing branch and bound techniques to globally optimize the cost function. We present a rigorous analysis of the canonical branch and bound algorithm in this setting as well as newly developed algorithms for other domains including convex sets. In particular, by making use of Lipschitz continuity of the surrogate approximation, we develop an entirely new algorithm based on overlapping balls. An application of the framework to the integration of expensive functions over rectangular domains and spherical surfaces in low dimensions is also considered. To assess performance of the framework, we apply it to canonical examples from the literature as well as an industrial model problem from oil reservoir simulation.

This thesis concerns the analytical and practical aspects of applying the Closest Point Method to solve elliptic partial differential equations (PDEs) on smooth surfaces and domains with smooth boundaries. A new numerical scheme is proposed to solve surface elliptic PDEs and a novel geometric multigrid solver is constructed to solve the resulting linear system. The method is also applied to coupled bulk-surface problems. A new embedding equation in a narrow band surrounding the surface is formulated so that it agrees with the original surface PDE on the surface and has a unique solution which is constant along the normals to the surface. The embedding equation is then discretized using standard finite difference scheme and barycentric Lagrange interpolation. The resulting scheme has 2nd-order accuracy in practice and is provably 2nd-order convergent for curves without boundary embedded in ℝ². To apply the method to solve elliptic equations on surfaces and domains with boundaries, the "ghost" point approach is adopted to handle Dirichlet, Neumann and Robin boundary conditions. A systematic method is proposed to represent values of ghost points by values of interior points according to boundary conditions. A novel geometric multigrid method based on the closest point representation of the surface is constructed to solve the resulting large sparse linear systems. Multigrid solvers are designed for surfaces with or without boundaries and domains with smooth boundaries. Numerical results indicate that the convergence rate of the multigrid solver stays roughly the same as we refine the mesh, as is desired of a multigrid algorithm. Finally the above methods are combined to solve coupled bulk-surface PDEs with some applications to biology.