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Motivated by the results concerning the regularity of solutions to the fractional Navier-Stokes system and questions about the influence of noise on the formation of singularities in hydrodynamic models, we have explored these two problems in the context of the fractional 1D Burgers equation. First, we performed highly accurate numerical computations to characterize the dependence of the blow-up time on the the fractional dissipation exponent in the supercritical regime. The problem was solved numerically using a pseudospectral method where integration in time was performed using a hybrid method combining the Crank-Nicolson and a three-step Runge-Kutta techniques. A highlight of this approach is automated resolution refinement. The blow-up time was estimated based on the time evolution of the enstrophy (H1 seminorm) and the width of the analyticity strip. The consistency of the obtained blow-up times was verified in the limiting cases. In the second part of the thesis we considered the fractional Burgers equation in the presence of suitably colored additive noise. This problem was solved using a stochastic Runge-Kutta method where the stochastic effects were approximated using a Monte-Carlo method. Statistic analysis of ensembles of stochastic solutions obtained for different noise magnitudes indicates that as the noise amplitude increases the distribution of blow-up times becomes non-Gaussian. In particular, while for increasing noise levels the mean blow-up time is reduced as compared to the deterministic case, solutions with increased existence time also become more likely. / Thesis / Master of Science (MSc)

Numerical models of PDE systems can involve very large matrix equations, but feedback controllers for these systems must be computable in real time to be implemented on physical systems. Classical control design methods produce controllers of the same order as the numerical models. Therefore, reduced order control design is vital for practical controllers. The main contribution of this research is a method of control order reduction that uses a newly developed low order basis. The low order basis is obtained by applying Proper Orthogonal Decomposition (POD) to a set of functional gains, and is referred to as the functional gain POD basis. Low order controllers resulting from the functional gain POD basis are compared with low order controllers resulting from more commonly used time snapshot POD bases, with the two dimensional heat equation as a test problem. The functional gain POD basis avoids subjective criteria associated with the time snapshot POD basis and provides an equally effective low order controller with larger stability radii. An efficient and effective methodology is introduced for using a low order basis in reduced order compensator design. This method combines "design-then-reduce" and "reduce-then-design" philosophies. The desirable qualities of the resulting reduced order compensator are verified by application to Burgers' equation in numerical experiments. / Ph. D.

In this paper we consider the problem of designing dynamic compensators to control a class of nonlinear parabolic distributed parameter systems. We concentrate on a system with unbounded input and output operators governed by Burgers’ equation. This equation provide a one dimensional model for certain convection—diffusion phenomena. A linearized model is used to compute a robust controller (MinMax), a LQG controller and a fixed-order-finite-dimensional control law (Optimal Projection) by minimizing various energy functionals. These control laws are then applied to the nonlinear model. Different approximation schemes are used to design suboptimal active feedback controllers. This approach provides important practical information. In particular, we show how functional gains can be used to locate new sensors. Numerical results are given to illustrate the basic ideas and to compare the various controllers. / Ph. D.

Data assimilation arises in a vast array of different topics: traditionally in meteorological and oceanographic modelling, wind tunnel or water tunnel experiments and recently from biomedical engineering. Data assimilation is a process for combine measured or observed data with a mathematical model, to obtain estimates of the expected data. The measured data usually contains inaccuracies and is given with low spatial and/or temporal resolution. In this thesis data assimilation for time dependent fluid flow is considered. The flow is assumed to satisfy a given partial differential equation, representing the mathematical model. The problem is to determine the initial state which leads to a flow field which satisfies the flow equation and is close to the given data. In the first part we consider one-dimensional flow governed by Burgers’ equation. We analyze two iterative methods for data assimilation problem for this equation. One of them so called adjoint optimization method, is based on minimization in L2-norm. We show that this minimization problem is ill-posed but the adjoint optimization iterative method is regularizing, and represents the well-known Landweber method in inverse problems. The second method is based on L2-minimization of the gradient. We prove that this problem always has a solution. We present numerical comparisons of these two methods. In the second part three-dimensional inviscid compressible flow represented by the Euler equations is considered. Adjoint technique is used to obtain an explicit formula for the gradient to the optimization problem. The gradient is used in combination with a quasi-Newton method to obtain a solution. The main focus regards the derivation of the adjoint equations with boundary conditions. An existing flow solver EDGE has been modified to solve the adjoint Euler equations and the gradient computations are validated numerically. The proposed iteration method are applied to a test problem where the initial pressure state is reconstructed, for exact data as well as when disturbances in data are present. The numerical convergence and the result are satisfying.

Orientador: Igor Leite Freire / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matemática, Estatística e Computação Científica / Made available in DSpace on 2018-08-19T07:51:21Z (GMT). No. of bitstreams: 1 Soares_JuniorCesarAlves_M.pdf: 448504 bytes, checksum: 3bdbb23b41bf8a05b373b9117cd9aa9b (MD5) Previous issue date: 2011 / Resumo: Neste trabalho, é estudada uma generalização da equação de Burgers do ponto de vista da teoria de simetrias de Lie / Abstract: In this work, a generalization of Burgers equation is studied from the point of view of Lie point symmetry theory / Mestrado / Matematica Aplicada / Mestre em Matemática Aplicada

The derivation of centered compact schemes at interior and boundary grid points is performed and an analysis of stability and computational efficiency is given. Compact schemes are high order implicit methods for numerical solutions of initial and/or boundary value problems modeled by differential equations. These schemes generally require smaller stencils than the traditional explicit finite difference counterparts. To avoid numerical instabilities at and near boundaries and in regions of mesh non-uniformity, a numerical filtering technique is employed. Experiments for non-stationary linear problems (convection, heat conduction) and also for nonlinear problems (Burgers' and KdV equations) were performed. The compact solvers were combined with Euler and fourth-order Runge-Kutta time differencing. In most cases, the order of convergence of the numerical solution to the exact solution was the same as the formal order of accuracy of the compact schemes employed.

The Eulerian description of dispersed two-phase flows results in a system of partial differential equations describing characteristics of the flow, namely volume fraction, density and velocity of the two phases, around any point in space over time. When pressure forces are neglected or a same pressure is considered for both phases, the resulting system is weakly hyperbolic and solutions may exhibit vacuum states (regions void of the dispersed phase) or localized unbounded singularities (delta shocks) that are not physically desirable. Therefore, it is crucial to find a physical way for preventing the formation of such undesirable solutions in weakly hyperbolic Eulerian two-phase flow models. This thesis focuses on the mathematical analysis of an Eulerian model for air- droplet flows, here called the Eulerian droplet model. This model can be seen as the sticky particle system with a source term and is successfully used for the prediction of droplet impingement and more recently for the prediction of particle flows in air- ways. However, this model includes only one-way momentum exchange coupling, and develops delta shocks and vacuum states. The main goal of this thesis is to improve this model, especially for the prevention of delta shocks and vacuum states, and the adjunction of two-way momentum exchange coupling. Using a characteristic analysis, the condition for loss of regularity of smooth solutions of the inviscid Burgers equation with a source term is established. The same condition applies to the droplet model. The Riemann problems associated, respectively, to the Burgers equation with a source term and the droplet model are solved. The characteristics are curves that tend asymptotically to straight lines. The existence of an entropic solution to the generalized Rankine-Hugoniot conditions is proven. Next, a way for preventing the formation of delta shocks and vacuum states in the model is identified and a new Eulerian droplet model is proposed. A new hierarchy of two-way coupling Eulerian models is derived. Each model is analyzed and numerical comparisons of the models are carried out. Finally, 2D computations of air-particle flows comparing the new Eulerian droplet model with the standard Eulerian droplet model are presented.

This paper develops a numerical algorithm that produces finite element solutions for a broad class of partial differential equations. The method is based on steepest descent methods in the Sobolev space H¹(Ω). Although the method may be applied in more general settings, we consider only differential equations that may be written as a first order quasi-linear system. The method is developed in a Hilbert space setting where strong convergence is established for part of the iteration. We also prove convergence for an inner iteration in the finite element setting. The method is demonstrated on Burger's equation and the Navier-Stokes equations as applied to the square cavity flow problem. Numerical evidence suggests that the accuracy of the method is second order,. A documented listing of the FORTRAN code for the Navier-Stokes equations is included.