The theory of deterministic chaos has generated a lot of interest and continues to be one of the much-focused research areas in the field of dynamics today. This is due to its prevalence in essential parts of human lives such as electrical circuits, chemical reactions, the flow of blood through the human system, the weather, etc. This thesis presents a study of the Lorenz equations, a famous example of chaotic systems. In particular, it presents the analysis of the Lorenz equations from stability to chaos and various bifurcation scenarios with numerical and graphical interpretations. It studies concepts of non-linear dynamical systems such as equilibrium points, stability, linearization, bifurcation, Lyapunov function, etc. Finally, it discusses how the Lorenz equations serve as a model for the waterwheel (in detail), and the convection roll for fluid.

Les équations de Navier-Stokes linéarisées sont utilisées dans l’industrie aéronautique pour l’optimisation de forme aérodynamique, l’aéroélasticité et l’aéroacoustique. Deux axes ont été suivis pour accélérer et rendre plus robuste la résolution de ces équations. Le premier est l’amélioration de la méthode itérative de résolution de systèmes linéaires utilisée, et le deuxième la formulation du schéma numérique conduisant à ce système linéaire. Dans cette première partie, l’extension de l’algorithme GMRES avec déflation spectrale à des systèmes à plusieurs seconds membres a été testée sur des cas tests industriels. L’amélioration du préconditionnement de la méthode GMRES par l’utilisation d'une méthode de Schwarz additive avec préconditionneur ILU(k) a permis une accélération du temps de résolution allant jusqu’à un facteur dix, ainsi que la convergence de cas jusqu’alors impossibles à résoudre. La deuxième partie présente d’abord un travail sur la stabilisation SUPG du schéma élément fini utilisé. La forme proposée de la matrice de stabilisation, dite complète, a donné des résultats encourageants en non-linéaire qui ne se sont pas transposés en linéarisé. Une étude sur les conditions aux limites de Dirichlet clôt cette partie. Une méthode algébrique d’imposition de conditions non homogènes sur des variables non triviales du calcul, qui a permis l’application industrielle à l’aéroacoustique, y est détaillée. De plus, la preuve est apportée que le caractère transparent d’une condition de Dirichlet homogène sur toutes les variables s’explique par le schéma SUPG. / The linearized Navier-Stokes equations are solved at Dassault Aviation within numerical simulations for aerodynamic shape optimisation, flutter calculations and aeroacoustics. In order to improve the robustness and efficiency of the Navier-Stokes solver, this thesis followed two complementary paths. The first is work on the iterative methods used to solve linear systems, and the second is the improvement of the numerical scheme underlying these linear systems. In the first part, the extension to multiple right-hand sides of the GMRES algorithm with spectral deflation was tested on industrial test cases. The use of the ILU(k) preconditioner within an additive Schwarz method led to a reduction of the time needed to solve the systems with GMRES by a factor ten. It also enabled the convergence of some numerically very difficult cases which could not be solved by the software available before this thesis. The second part of the manuscript begins with work on the SUPG method used to stabilise the finite element scheme. A new way of computing the stabilisation matrix gave promising results on non-linear cases, which were however not observed for linear cases. A study on Dirichlet boundary conditions concludes this part. An algebraic method to impose non homogeneous Dirichlet boundary conditions on non-trivial variables is introduced. It enables the use, in an industrial context, of linearized Navier-Stokes for aeroacoustics. Moreover, the transparent behaviour of a homogeneous Dirichlet boundary conditions on all variables is proved to be due to the SUPG stabilisation.

This thesis explores the connection between Crouzeix's conjecture and the convergence of the GMRES algorithm. GMRES is a popular iterative method for solving linear systems and is one of the many Krylov methods. Despite its popularity, the convergence of GMRES is not completely understood. While the spectrum can in some cases be a good indicator of convergence, it has been shown that in general, the spectrum does not provide sufficient information to fully explain the behavior of GMRES iterations. Other sets associated with a matrix that can also help predict convergence are the pseudospectrum and the numerical range. This work focuses on convergence bounds obtained by considering the latter. In particular, it focuses on the application of Crouzeix's conjecture, which relates the norm of a matrix polynomial to the size of that polynomial over the numerical range, to describing GMRES convergence.

No description available.

Yes / A dynamic response analysis model of a Class E2 converter for wireless power transfer applications is presented. The converter operates at 200 kHz and consists of an induction link with its primary coil driven by a class E inverter and the secondary coil with a voltage-driven class E synchronous rectifier. A seventh-order linear time invariant state-space model is used to obtain the eigenvalues of the system for the four modes resulting from the operation of the converter switches. A participation factor for the four modes is used to find the actual operating point dominant poles for the system response. A dynamic analysis is carried out to investigate the effect of changing the separation distance between the two coils, based on converter performance and the changes required of some circuit parameters to achieve optimum efficiency and stability. The results show good performance in terms of efficiency (90–98%) and maintenance of constant output voltage with dynamic change of capacitance in the inverter. An experiment with coils of the dimension of 53 × 43 × 6 mm3 operating at a resonance frequency of 200 kHz, was created to verify the proposed mathematical model and both were found to be in excellent agreement.

No description available.

Many methods used for estimation and detection consider only the mean and variance of the involved noise instead of the full noise descriptions. One reason for this is that the mathematics is often considerably simplified this way. However, the implications of the simplifications are seldom studied, and this thesis shows that if no approximations are made performance is gained. Furthermore, the gain is quantified in terms of the useful information in the noise distributions involved. The useful information is given by the intrinsic accuracy, and a method to compute the intrinsic accuracy for a given distribution, using Monte Carlo methods, is outlined. A lower bound for the covariance of the estimation error for any unbiased estimator s given by the Cramér-Rao lower bound (CRLB). At the same time, the Kalman filter is the best linear unbiased estimator (BLUE) for linear systems. It is in this thesis shown that the CRLB and the BLUE performance are given by the same expression, which is parameterized in the intrinsic accuracy of the noise. How the performance depends on the noise is then used to indicate when nonlinear filters, e.g., a particle filter, should be used instead of a Kalman filter. The CRLB results are shown, in simulations, to be a useful indication of when to use more powerful estimation methods. The simulations also show that other techniques should be used as a complement to the CRLB analysis to get conclusive performance results. For fault detection, the statistics of the asymptotic generalized likelihood ratio (GLR) test provides an upper bound on the obtainable detection performance. The performance is in this thesis shown to depend on the intrinsic accuracy of the involved noise. The asymptotic GLR performance can then be calculated for a test using the actual noise and for a test using the approximative Gaussian noise. Based on the difference in performance, it is possible to draw conclusions about the quality of the Gaussian approximation. Simulations show that when the difference in performance is large, an exact noise representation improves the detection. Simulations also show that it is difficult to predict the exact influence on the detection performance caused by substituting the system noise with Gaussian noise approximations. / Många metoder som används i estimerings- och detekteringssammanhang tar endast hänsyn till medelvärde och varians hos ingående brus istället för att använda en fullständig brusbeskrivning. En av anledningarna till detta är att den förenklade brusmodellen underlättar många beräkningar. Dock studeras sällan de effekter förenklingarna leder till. Denna avhandling visar att om inga förenklingar görs kan prestandan förbättras och det visas också hur förbättringen kan relateras till den intressanta informationen i det involverade bruset. Den intressanta informationen är den inneboende noggrannheten (eng. intrinsic accuracy) och ett sätt för att bestämma den inneboende noggrannheten hos en given fördelning med hjälp av Monte-Carlo-metoder presenteras. Ett mått på hur bra en estimator utan bias kan göras ges av Cramér-Raos undre gräns (CRLB). Samtidigt är det känt att kalmanfiltret är den bästa lineära biasfria estimatorn (BLUE) för lineära system. Det visas här att CRLB och BLUE-prestanda ges av samma matematiska uttryck där den inneboende noggrannheten ingår som en parameter. Kunskap om hur informationen påverkar prestandan kan sedan användas för att indikera när ett olineärt filter, t.ex. ett partikelfilter, bör användas istället för ett kalmanfilter. Med hjälp av simuleringar visas att CRLB är ett användbart mått för att indikera när mer avancerade metoder kan vara lönsamma. Simuleringarna visar dock också att CRLB-analysen bör kompletteras med andra tekniker för att det ska vara möjligt att dra definitiva slutsatser. I fallet feldetektion ger de asymptotiska egenskaperna hos den generaliserade sannolikhetskvoten (eng. generalized likelihood ratio, GLR) en övre gräns för hur bra detektorer som kan konstrueras. Det visas här hur den övre gränsen beror på den inneboende noggrannheten hos det aktuella bruset. Genom att beräkna asymptotisk GLR-testprestanda för det sanna bruset och för en gaussisk brusapproximation går det att dra slutsatser om huruvida den gaussiska approximationen är tillräckligt bra för att kunna användas. I simuleringar visas att det är lönsamt att använda sig av en exakt brusbeskrivning om skillnaden i prestanda är stor mellan de båda fallen. Simuleringarna indikerar också att det kan vara svårt att förutsäga den exakta effekten av en gaussisk brusapproximation. / <p>Report code: LiU-Tek-Lic-2005:54</p>

We relate linear constant coefficient systems of partial difference equations (a discretization of a system of linear partial differential equations) satisfying some collection of scalar polynomial equations to systems defined over the coordinate ring of an algebraic variety. This motivates the extension of behavioral systems theory (a generalization of classical systems theory where inputs and outputs are lumped together) to the setting where the ring of operators is an affine domain and the signal space is restricted to signals which satisfy the same scalar polynomial equations. By recognizing the role of the kernel representation's Gröbner basis in the Cauchy problem, we extend notions of controllability from the classical behavioral setting to accommodate this generalization. We then address the question as to when an autonomous behavior admits a Livšic-system state-space representation, where the state update equations are overdetermined leading to the requirement that the input and output signals satisfy their own compatibility difference equations. This leads to a frequency domain setting involving input and output holomorphic vector bundles and a transfer function given by a meromorphic bundle map. An analogue of the Hankel realization theorem developed by J. Ball and V. Vinnikov then leads to a Livšic-system state-space representation for an autonomous behavior satisfying some natural additional conditions. / Ph. D.

Sequences of matrices arise in many applications in science and engineering. In this thesis we consider matrices that are closely related (or closely related in groups), and we take advantage of the small differences between them to efficiently solve sequences of linear systems and eigenproblems. Recycling techniques, such as recycling preconditioners or subspaces, are popular approaches for reducing computational cost. In this thesis, we introduce two novel approaches for recycling previously computed information for a subsequent system or eigenproblem, and demonstrate good results for sequences arising in several applications. Preconditioners are often essential for fast convergence of iterative methods. However, computing a good preconditioner can be very expensive, and when solving a sequence of linear systems, we want to avoid computing a new preconditioner too often. Instead, we can recycle a previously computed preconditioner, for which we have good convergence behavior of the preconditioned system. We propose an update technique we call the sparse approximate map, or SAM update, that approximately maps one matrix to another matrix in our sequence. SAM updates are very cheap to compute and apply, preserve good convergence properties of a previously computed preconditioner, and help to amortize the cost of that preconditioner over many linear solves. When solving a sequence of eigenproblems, we can reduce the computational cost of constructing the Krylov space starting with a single vector by warm-starting the eigensolver with a subspace instead. We propose an algorithm to warm-start the Krylov-Schur method using a previously computed approximate invariant subspace. We first compute the approximate Krylov decomposition for a matrix with minimal residual, and use this space to warm-start the eigensolver. We account for the residual matrix when expanding, truncating, and deflating the decomposition and show that the norm of the residual monotonically decreases. This method is effective in reducing the total number of matrix-vector products, and computes an approximate invariant subspace that is as accurate as the one computed with standard Krylov-Schur. In applications where the matrix-vector products require an implicit linear solve, we incorporate Krylov subspace recycling. Finally, in many applications, sequences of matrices take the special form of the sum of the identity matrix, a very low-rank matrix, and a small-in-norm matrix. We consider convergence rates for GMRES applied to these matrices by identifying the sources of sensitivity. / Doctor of Philosophy / Problems in science and engineering often require the solution to many linear systems, or a sequence of systems, that model the behavior of physical phenomena. In order to construct highly accurate mathematical models to describe this behavior, the resulting matrices can be very large, and therefore the linear system can be very expensive to solve. To efficiently solve a sequence of large linear systems, we often use iterative methods, which can require preconditioning techniques to achieve fast convergence. The preconditioners themselves can be very expensive to compute. So, we propose a cheap update technique that approximately maps one matrix to another in the sequence for which we already have a good preconditioner. We then combine the preconditioner and the map and use the updated preconditioner for the current system. Sequences of eigenvalue problems also arise in many scientific applications, such as those modeling disk brake squeal in a motor vehicle. To accurately represent this physical system, large eigenvalue problems must be solved. The behavior of certain eigenvalues can reveal instability in the physical system but to identify these eigenvalues, we must solve a sequence of very large eigenproblems. The eigensolvers used to solve eigenproblems generally begin with a single vector, and instead, we propose starting the method with several vectors, or a subspace. This allows us to reduce the total number of iterations required by the eigensolver while still producing an accurate solution. We demonstrate good results for both of these approaches using sequences of linear systems and eigenvalue problems arising in several real-world applications. Finally, in many applications, sequences of matrices take the special form of the sum of the identity matrix, a very low-rank matrix, and a small-in-norm matrix. We examine the convergence behavior of the iterative method GMRES when solving such a sequence of matrices.

In science and engineering, many applications require the solution of a sequence of linear systems. There are many ways to solve linear systems and we always look for methods that are faster and/or require less storage. In this dissertation, we focus on solving these systems with Krylov subspace methods and how to obtain effective preconditioners inexpensively. We first present an application for electronic structure calculation. A sequence of slowly changing linear systems is produced in the simulation. The linear systems change by rank-one updates. Properties of the system matrix are analyzed. We use Krylov subspace methods to solve these linear systems. Krylov subspace methods need a preconditioner to be efficient and robust. This causes the problem of computing a sequence of preconditioners corresponding to the sequence of linear systems. We use recycling preconditioners, which is to update and reuse existing preconditioner. We investigate and analyze several preconditioners, such as ILU(0), ILUTP, domain decomposition preconditioners, and inexact matrix-vector products with inner-outer iterations. Recycling preconditioners produces cumulative updates to the preconditioner. To reduce the cost of applying the preconditioners, we propose approaches to truncate the cumulative preconditioner updates, which is a low-rank matrix. Two approaches are developed. The first one is to truncate the low-rank matrix using the best approximation given by the singular value decomposition (SVD). This is effective if many singular values are close to zero. If not, based on the ideas underlying GCROT and recycling, we use information from an Arnoldi recurrence to determine which directions to keep. We investigate and analyze their properties. We also prove that both truncation approaches work well under suitable conditions. We apply our truncation approaches on two applications. One is the Quantum Monte Carlo (QMC) method and the other is a nonlinear second order partial differential equation (PDE). For the QMC method, we test both truncation approaches and analyze their results. For the PDE problem, we discretize the equations with finite difference method, solve the nonlinear problem by Newton's method with a line-search, and utilize Krylov subspace methods to solve the linear system in every nonlinear iteration. The preconditioner is updated by Broyden-type rank-one updates, and we truncate the preconditioner updates by using the SVD finally. We demonstrate that the truncation is effective. In the last chapter, we develop a matrix reordering algorithm that improves the diagonal dominance of Slater matrices in the QMC method. If we reorder the entire Slater matrix, we call it global reordering and the cost is O(N^3), which is expensive. As the change is geometrically localized and impacts only one row and a modest number of columns, we propose a local reordering of a submatrix of the Slater matrix. The submatrix has small dimension, which is independent of the size of Slater matrix, and hence the local reordering has constant cost (with respect to the size of Slater matrix). / Ph. D.