Adaptive algorithms for two fluids flows with anisotropic finite elements and order two time discretizations

This thesis is devoted to the derivation of a posteriori error estimates for the numerical approximation of fluids flows separated by a free surface. Based on these estimates, error indicators are introduced and adaptive algorithms are proposed to solve the problem with accuracy and low computational costs. We focus on numerical methods that are combinations of anisotropic finite elements and second order methods to advance in time.

We split the technical difficulties in the derivation of the error estimates by first studying independent PDEs, and in a second time by gathering the different results to analyse the complete system of equations composed with these latter. The a posteriori error analysis for the approximation of these PDEs will be addressed in a particular and devoted chapter. The last chapter is dedicated to the study of the system describing two fluids flows.

In each chapter, we focus on two main objectives. The first is a theoretical analysis and the derivation of error estimates, the second is the description and the implementation of an algorithm to adapt meshes and time steps. Finally, numerical experiments are performed to demonstrate the efficiency of the procedure.

A posteriori error estimation for partial differential equations with random input data

Diane Sylvie Guignard

This thesis is devoted to the derivation of error estimates for partial differential equations with random input data, with a focus on a posteriori error estimates which are the basis for adaptive strategies. Such procedures aim at obtaining an approximation of the solution with a given precision while minimizing the computational costs. If several sources of error come into play, it is then necessary to balance them to avoid unnecessary work. We are first interested in problems that contain small uncertainties approximated by finite elements. The use of perturbation techniques is appropriate in this setting since only few terms in the power series expansion of the exact random solution with respect to a parameter characterizing the amount of randomness in the problem are required to obtain an accurate approximation. The goal is then to perform an error analysis for the finite element approximation of the expansion up to a certain order. First, an elliptic model problem with random diffusion coefficient with affine dependence on a vector of independent random variables is studied. We give both a priori and a posteriori error estimates for the first term in the expansion for various norms of the error. The results are then extended to higher order approximations and to other sources of uncertainty, such as boundary conditions or forcing term. Next, the analysis of nonlinear problems in random domains is proposed, considering the one-dimensional viscous Burgers' equation and the more involved incompressible steady-state Navier-Stokes equations. The domain mapping method is used to transform the equations in random domains into equations in a fixed reference domain with random coefficients. We give conditions on the mapping and the input data under which we can prove the well-posedness of the problems and give a posteriori error estimates for the finite element approximation of the first term in the expansion. Finally, we consider the heat equation with random Robin boundary conditions. For this parabolic problem, the time discretization brings an additional source of error that is accounted for in the error analysis. The second part of this work consists in the analysis of a random elliptic diffusion problem that is approximated in the physical space by the finite element method and in the stochastic space by the stochastic collocation method on a sparse grid. Considering a random diffusion coefficient with affine dependence on a vector of independent random variables, we derive a residual-based a posteriori error estimate that controls the two sources of error. The stochastic error estimator is then used to drive an adaptive sparse grid algorithm which aims at alleviating the so-called curse of dimensionality inherent to tensor grids. Several numerical examples are given to illustrate the performance of the adaptive procedure.

EPFL2016

Numerical approximation of the unsteady Navier-Stokes equations with an application to the flow past a square cylinder

Francesco Casalegno

The goal of this project is to numerically solve the Navier-Stokes equations by using different numerical methods with particular emphasis on solving the problem of the flow past a square cylinder. In particular, we use the finite element method based on P2/P1 elements for the velocity and pressure fields for the spatial approximation, while the backward Euler method (with semi-implicit treatment of the nonlinear term) for the time discretization. We firstly test the numerical schemes on a benchmark problem with known exact solution. Then, we discuss in detail the advantages, in terms of computational costs, in using the algebraic Chorin-Temam method with additional implementation improvements. We finally investigate the problem of the two-dimensional flow past a square cylinder, focusing our attention on the range 0.1-300 for the Reynolds number (Re). We describe the two different regimes associated to the steady and the unsteady flows and we remark as the latter is in fact due to a Hopf bifurcation of the system. We also discuss the relation between the Strouhal and Reynolds numbers concluding that the Strouhal number attains its maximum value in the range 169-170 for the Reynolds number. In particular, a cubic model is proposed, showing very good matching with observed data and a better fitting than other models available in literature.

2013

A Posteriori Error Estimation for the Stochastic Collocation Finite Element Method

Diane Sylvie Guignard, Fabio Nobile

In this work, we consider an elliptic partial differential equation (PDE) with a random coefficient solved with the stochastic collocation finite element method (SC-FEM). The random diffusion coefficient is assumed to depend in an affine way on independent random variables. We derive a residual-based a posteriori error estimate that is constituted of two parts controlling the SC error and the FE error, respectively. The SC error estimator is then used to drive an adaptive sparse grid algorithm. Several numerical examples are given to illustrate the efficiency of the error estimator and the performance of the adaptive algorithm.

2018

A posteriori error estimation for partial differential equations with random input data

Diane Sylvie Guignard

EPFL2016