Finite Difference and Discontinuous Galerkin Methods for Wave Equations
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Finite Difference Methods for Solving Differential
FINITE DIFFERENCE METHODS FOR SOLVING DIFFERENTIAL EQUATIONS I-Liang Chern Department of Mathematics National Taiwan University May 16, 2013 2 Contents 1 Introduction 3 1.1 Finite Difference Approximation . ........ 3 1.2 Basic Numerical Methods for Ordinary Differential Equations ........... 5 1.3 Runge-Kuttamethods..... ..... ...... ..... ...... ... ... 8 1.4 Multistepmethods ................................ 10 1.5 Linear difference equation . ...... 14 1.6 Stabilityanalysis ............................... 17 1.6.1 ZeroStability................................. 18 2 Finite Difference Methods for Linear Parabolic Equations 23 2.1 Finite Difference Methods for the Heat Equation . ........... 23 2.1.1 Some discretization methods . 23 2.1.2 Stability and Convergence for the Forward Euler method.......... 25 2.2 L2 Stability – von Neumann Analysis . 26 2.3 Energymethod .................................... 28 2.4 Stability Analysis for Montone Operators– Entropy Estimates ........... 29 2.5 Entropy estimate for backward Euler method . ......... 30 2.6 ExistenceTheory ................................. 32 2.6.1 Existence via forward Euler method . ..... 32 2.6.2 A Sharper Energy Estimate for backward Euler method . ......... 33 2.7 Relaxationoferrors.............................. 34 2.8 BoundaryConditions ..... ..... ...... ..... ...... ... 36 2.8.1 Dirichlet boundary condition . ..... 36 2.8.2 Neumann boundary condition . 37 2.9 The discrete Laplacian and its inversion . .......... 38 2.9.1 Dirichlet boundary condition . ..... 38 3 Finite Difference Methods for Linear elliptic Equations 41 3.1 Discrete Laplacian in two dimensions . ........ 41 3.1.1 Discretization methods . 41 3.1.2 The 9-point discrete Laplacian . ..... 42 3.2 Stability of the discrete Laplacian . ......... 43 3 4 CONTENTS 3.2.1 Fouriermethod ................................ 43 3.2.2 Energymethod ................................ 44 4 Finite Difference Theory For Linear Hyperbolic Equations 47 4.1 A review of smooth theory of linear hyperbolic equations ............. -
Exercises from Finite Difference Methods for Ordinary and Partial
Exercises from Finite Difference Methods for Ordinary and Partial Differential Equations by Randall J. LeVeque SIAM, Philadelphia, 2007 http://www.amath.washington.edu/ rjl/fdmbook ∼ Under construction | more to appear. Contents Chapter 1 4 Exercise 1.1 (derivation of finite difference formula) . 4 Exercise 1.2 (use of fdstencil) . 4 Chapter 2 5 Exercise 2.1 (inverse matrix and Green's functions) . 5 Exercise 2.2 (Green's function with Neumann boundary conditions) . 5 Exercise 2.3 (solvability condition for Neumann problem) . 5 Exercise 2.4 (boundary conditions in bvp codes) . 5 Exercise 2.5 (accuracy on nonuniform grids) . 6 Exercise 2.6 (ill-posed boundary value problem) . 6 Exercise 2.7 (nonlinear pendulum) . 7 Chapter 3 8 Exercise 3.1 (code for Poisson problem) . 8 Exercise 3.2 (9-point Laplacian) . 8 Chapter 4 9 Exercise 4.1 (Convergence of SOR) . 9 Exercise 4.2 (Forward vs. backward Gauss-Seidel) . 9 Chapter 5 11 Exercise 5.1 (Uniqueness for an ODE) . 11 Exercise 5.2 (Lipschitz constant for an ODE) . 11 Exercise 5.3 (Lipschitz constant for a system of ODEs) . 11 Exercise 5.4 (Duhamel's principle) . 11 Exercise 5.5 (matrix exponential form of solution) . 11 Exercise 5.6 (matrix exponential form of solution) . 12 Exercise 5.7 (matrix exponential for a defective matrix) . 12 Exercise 5.8 (Use of ode113 and ode45) . 12 Exercise 5.9 (truncation errors) . 13 Exercise 5.10 (Derivation of Adams-Moulton) . 13 Exercise 5.11 (Characteristic polynomials) . 14 Exercise 5.12 (predictor-corrector methods) . 14 Exercise 5.13 (Order of accuracy of Runge-Kutta methods) . -
Hp-Finite Element Methods for Hyperbolic Problems
¢¢¢¢¢¢¢¢¢¢¢ ¢¢¢¢¢¢¢¢¢¢¢ ¢¢¢¢¢¢¢¢¢¢¢ ¢¢¢¢¢¢¢¢¢¢¢ Eidgen¨ossische Ecole polytechnique f´ed´erale de Zurich ¢¢¢¢¢¢¢¢¢¢¢ ¢¢¢¢¢¢¢¢¢¢¢ ¢¢¢¢¢¢¢¢¢¢¢ ¢¢¢¢¢¢¢¢¢¢¢ Technische Hochschule Politecnico federale di Zurigo ¢¢¢¢¢¢¢¢¢¢¢ ¢¢¢¢¢¢¢¢¢¢¢ ¢¢¢¢¢¢¢¢¢¢¢ ¢¢¢¢¢¢¢¢¢¢¢ Z¨urich Swiss Federal Institute of Technology Zurich hp-Finite Element Methods for Hyperbolic Problems E. S¨uli†, P. Houston† and C. Schwab ∗ Research Report No. 99-14 July 1999 Seminar f¨urAngewandte Mathematik Eidgen¨ossische Technische Hochschule CH-8092 Z¨urich Switzerland †Oxford University Computing Laboratory, Wolfson Building, Parks Road, Oxford OX1 3QD, United Kingdom ∗E. S¨uliand P. Houston acknowledge the financial support of the EPSRC (Grant GR/K76221) hp-Finite Element Methods for Hyperbolic Problems E. S¨uli†, P. Houston† and C. Schwab ∗ Seminar f¨urAngewandte Mathematik Eidgen¨ossische Technische Hochschule CH-8092 Z¨urich Switzerland Research Report No. 99-14 July 1999 Abstract This paper is devoted to the a priori and a posteriori error analysis of the hp-version of the discontinuous Galerkin finite element method for partial differential equations of hyperbolic and nearly-hyperbolic character. We consider second-order partial dif- ferential equations with nonnegative characteristic form, a large class of equations which includes convection-dominated diffusion problems, degenerate elliptic equa- tions and second-order problems of mixed elliptic-hyperbolic-parabolic type. An a priori error bound is derived for the method in the so-called DG-norm which is optimal in terms of the mesh size h; the error bound is either 1 degree or 1/2 de- gree below optimal in terms of the polynomial degree p, depending on whether the problem is convection-dominated, or diffusion-dominated, respectively. In the case of a first-order hyperbolic equation the error bound is hp-optimal in the DG-norm. -
A Quasi-Static Particle-In-Cell Algorithm Based on an Azimuthal Fourier Decomposition for Highly Efficient Simulations of Plasma-Based Acceleration
A quasi-static particle-in-cell algorithm based on an azimuthal Fourier decomposition for highly efficient simulations of plasma-based acceleration: QPAD Fei Lia,c, Weiming And,∗, Viktor K. Decykb, Xinlu Xuc, Mark J. Hoganc, Warren B. Moria,b aDepartment of Electrical Engineering, University of California Los Angeles, Los Angeles, CA 90095, USA bDepartment of Physics and Astronomy, University of California Los Angeles, Los Angeles, CA 90095, USA cSLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA dDepartment of Astronomy, Beijing Normal University, Beijing 100875, China Abstract The three-dimensional (3D) quasi-static particle-in-cell (PIC) algorithm is a very effi- cient method for modeling short-pulse laser or relativistic charged particle beam-plasma interactions. In this algorithm, the plasma response, i.e., plasma wave wake, to a non- evolving laser or particle beam is calculated using a set of Maxwell's equations based on the quasi-static approximate equations that exclude radiation. The plasma fields are then used to advance the laser or beam forward using a large time step. The algorithm is many orders of magnitude faster than a 3D fully explicit relativistic electromagnetic PIC algorithm. It has been shown to be capable to accurately model the evolution of lasers and particle beams in a variety of scenarios. At the same time, an algorithm in which the fields, currents and Maxwell equations are decomposed into azimuthal harmonics has been shown to reduce the complexity of a 3D explicit PIC algorithm to that of a 2D algorithm when the expansion is truncated while maintaining accuracy for problems with near azimuthal symmetry. -
A Discontinuous Galerkin Method for Nonlinear Parabolic Equations and Gradient flow Problems with Interaction Potentials
A discontinuous Galerkin method for nonlinear parabolic equations and gradient flow problems with interaction potentials Zheng Sun1, Jos´eA. Carrillo2 and Chi-Wang Shu3 Abstract We consider a class of time dependent second order partial differential equations governed by a decaying entropy. The solution usually corresponds to a density distribution, hence positivity (non-negativity) is expected. This class of problems covers important cases such as Fokker-Planck type equations and aggregation models, which have been studied intensively in the past decades. In this paper, we design a high order discontinuous Galerkin method for such problems. If the interaction potential is not involved, or the interaction is defined by a smooth kernel, our semi-discrete scheme admits an entropy inequality on the discrete level. Furthermore, by applying the positivity-preserving limiter, our fully discretized scheme produces non-negative solutions for all cases under a time step constraint. Our method also applies to two dimensional problems on Cartesian meshes. Numerical examples are given to confirm the high order accuracy for smooth test cases and to demonstrate the effectiveness for preserving long time asymptotics. Keywords: discontinuous Galerkin method, positivity-preserving, entropy-entropy dissipation relationship, nonlinear parabolic equation, gradient flow 1Division of Applied Mathematics, Brown University, Providence, RI 02912, USA. E-mail: zheng [email protected] 2Department of Mathematics, Imperial College London, London SW7 2AZ, UK. E-mail: car- [email protected]. Research supported by the Royal Society via a Wolfson Research Merit Award and by EPSRC grant number EP/P031587/1. 3Division of Applied Mathematics, Brown University, Providence, RI 02912, USA. E-mail: [email protected]. -
A Numerical Method of Characteristics for Solving Hyperbolic Partial Differential Equations David Lenz Simpson Iowa State University
Iowa State University Capstones, Theses and Retrospective Theses and Dissertations Dissertations 1967 A numerical method of characteristics for solving hyperbolic partial differential equations David Lenz Simpson Iowa State University Follow this and additional works at: https://lib.dr.iastate.edu/rtd Part of the Mathematics Commons Recommended Citation Simpson, David Lenz, "A numerical method of characteristics for solving hyperbolic partial differential equations " (1967). Retrospective Theses and Dissertations. 3430. https://lib.dr.iastate.edu/rtd/3430 This Dissertation is brought to you for free and open access by the Iowa State University Capstones, Theses and Dissertations at Iowa State University Digital Repository. It has been accepted for inclusion in Retrospective Theses and Dissertations by an authorized administrator of Iowa State University Digital Repository. For more information, please contact [email protected]. This dissertation has been microfilmed exactly as received 68-2862 SIMPSON, David Lenz, 1938- A NUMERICAL METHOD OF CHARACTERISTICS FOR SOLVING HYPERBOLIC PARTIAL DIFFERENTIAL EQUATIONS. Iowa State University, PluD., 1967 Mathematics University Microfilms, Inc., Ann Arbor, Michigan A NUMERICAL METHOD OF CHARACTERISTICS FOR SOLVING HYPERBOLIC PARTIAL DIFFERENTIAL EQUATIONS by David Lenz Simpson A Dissertation Submitted to the Graduate Faculty in Partial Fulfillment of The Requirements for the Degree of DOCTOR OF PHILOSOPHY Major Subject: Mathematics Approved: Signature was redacted for privacy. In Cijarge of Major Work Signature was redacted for privacy. Headead of^ajorof Major DepartmentDepartmen Signature was redacted for privacy. Dean Graduate Co11^^ Iowa State University of Science and Technology Ames, Iowa 1967 ii TABLE OF CONTENTS Page I. INTRODUCTION 1 II. DISCUSSION OF THE ALGORITHM 3 III. DISCUSSION OF LOCAL ERROR, EXISTENCE AND UNIQUENESS THEOREMS 10 IV. -
Robust Adaptive Hp Discontinuous Galerkin Finite Element Methods For
Robust adaptive hp discontinuous Galerkin finite element methods for the Helmholtz equation∗ Scott Congrevey Joscha Gedickez Ilaria Perugiax Abstract This paper presents an hp a posteriori error analysis for the 2D Helmholtz equation that is robust in the polynomial degree p and the wave number k. For the discretization, we consider a discontinuous Galerkin formulation that is unconditionally well posed. The a posteriori error analysis is based on the technique of equilibrated fluxes applied to a shifted Poisson problem, with the error due to the nonconformity of the discretization controlled by a potential reconstruction. We prove that the error estimator is both reliable and efficient, under the condition that the initial mesh size and polynomial degree is chosen such that the discontinuous Galerkin formulation converges, i.e., it is out of the regime of pollution. We confirm the efficiency of an hp-adaptive refinement strategy based on the presented robust a posteriori error estimator via several numerical examples. Keywords a posteriori error analysis, hp discontinuous Galerkin finite element method, equilibrated fluxes, potential reconstruction, Helmholtz problem AMS subject classification 65N15, 65N30, 65N50 1 Introduction In this paper, we consider the following Helmholtz problem with impedance boundary condition: Find a (complex) solution u 2 H2(Ω) such that −∆u − k2u = f in Ω; (1.1) ru · n − iku = g on @Ω; where Ω ⊂ R2 is a bounded, Lipschitz domain, n denotes the outer unit normal on the boundary @Ω, f 2 L2(Ω), g 2 L2(@Ω), and k > 0 is the (constant) wavenumber. The problem (1.1) was shown to be well-posed in [18]. -
Analysis of Finite Difference Discretization Schemes For
Computers and Chemical Engineering 71 (2014) 241–252 Contents lists available at ScienceDirect Computers and Chemical Engineering j ournal homepage: www.elsevier.com/locate/compchemeng Analysis of finite difference discretization schemes for diffusion in spheres with variable diffusivity ∗ Ashlee N. Ford Versypt, Richard D. Braatz Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA a r t i c l e i n f o a b s t r a c t Article history: Two finite difference discretization schemes for approximating the spatial derivatives in the diffusion Received 28 April 2014 equation in spherical coordinates with variable diffusivity are presented and analyzed. The numerical Accepted 27 May 2014 solutions obtained by the discretization schemes are compared for five cases of the functional form Available online 27 August 2014 for the variable diffusivity: (I) constant diffusivity, (II) temporally dependent diffusivity, (III) spatially dependent diffusivity, (IV) concentration-dependent diffusivity, and (V) implicitly defined, temporally Keywords: and spatially dependent diffusivity. Although the schemes have similar agreement to known analytical Finite difference method or semi-analytical solutions in the first four cases, in the fifth case for the variable diffusivity, one scheme Variable coefficient Diffusion produces a stable, physically reasonable solution, while the other diverges. We recommend the adoption of the more accurate and stable of these finite difference discretization schemes to numerically approxi- Spherical geometry Method of lines mate the spatial derivatives of the diffusion equation in spherical coordinates for any functional form of variable diffusivity, especially cases where the diffusivity is a function of position. © 2014 Elsevier Ltd. -
Optimal Strong-Stability-Preserving Runge–Kutta Time Discretizations for Discontinuous Galerkin Methods
Journal of Scientific Computing manuscript No. (will be inserted by the editor) Optimal strong-stability-preserving Runge–Kutta time discretizations for discontinuous Galerkin methods Ethan J. Kubatko · Benjamin A. Yeager · David I. Ketcheson Received: date / Accepted: date Abstract Discontinuous Galerkin (DG) spatial discretizations are often used in a method-of-lines approach with explicit strong-stability-preserving (SSP) Runge– Kutta (RK) time steppers for the numerical solution of hyperbolic conservation laws. The time steps that are employed in this type of approach must satisfy Courant–Friedrichs–Lewy (CFL) stability constraints that are dependent on both the region of absolute stability and the SSP coefficient of the RK method. While existing SSPRK methods have been optimized with respect to the latter, it is in fact the former that gives rise to stricter constraints on the time step in the case of RKDG stability. Therefore, in this work, we present the development of new “DG-optimized” SSPRK methods with stability regions that have been specifically designed to maximize the stable time step size for RKDG methods of a given order in one space dimension. These new methods represent the best available RKDG methods in terms of computational efficiency, with significant improvements over methods using existing SSPRK time steppers that have been optimized with re- spect to SSP coefficients. Second-, third-, and fourth-order methods with up to eight stages are presented, and their stability properties are verified through ap- plication to numerical test cases. Keywords discontinuous Galerkin · Runge–Kutta · strong-stability-preserving E. Kubatko Department of Civil, Environmental, and Geodetic Engineering, The Ohio State University, 2070 Neil Avenue, Columbus, OH 43210, United States Tel.: 614-292-7176 E-mail: [email protected] B. -
A Bddc Algorithm for the Stokes Problem with Weak 2 Galerkin Discretizations
1 A BDDC ALGORITHM FOR THE STOKES PROBLEM WITH WEAK 2 GALERKIN DISCRETIZATIONS 3 XUEMIN TU∗ AND BIN WANGy 4 Abstract. The BDDC (balancing domain decomposition by constraints) methods have been 5 applied successfully to solve the large sparse linear algebraic systems arising from conforming finite 6 element discretizations of second order elliptic and Stokes problems. In this paper, the Stokes 7 equations are discretized using the weak Galerkin method, a newly developed nonconforming finite 8 element method. A BDDC algorithm is designed to solve the linear system such obtained. Edge/face 9 velocity interface average and mean subdomain pressure are selected for the coarse problem. The 10 condition number bounds of the BDDC preconditioned operator are analyzed, and the same rate 11 of convergence is obtained as for conforming finite element methods. Numerical experiments are 12 conducted to verify the theoretical results. 13 Key words. Discontinuous Galerkin, HDG, weak Galerkin, domain decomposition, BDDC, 14 Stokes, Saddle point problems, benign subspace 15 AMS subject classifications. 65F10, 65N30, 65N55 16 1. Introduction. Numerical solution of saddle point problems using non over- 17 lapping domain decomposition methods have long been an active area of research; see, 18 e.g., [28, 15, 11, 10, 18, 29, 30, 16, 33, 17, 34, 27]. The Balancing Domain Decomposi- 19 tion by Constraints (BDDC) algorithm is an advanced variant of the non-overlapping 20 domain decomposition technique. It was first introduced by Dohrmann [5], and the 21 theoretical analysis was later given by Mandel and Dohrmann [20]. In this theoretical 22 development, optimal condition number bound was obtained for the BBDC opera- 23 tors proposed for symmetric positive definite systems. -
Weighted Scheme and the Galerkin Method
mathematics Article On the Numerical Simulation of HPDEs Using q-Weighted Scheme and the Galerkin Method Haifa Bin Jebreen *,† and Fairouz Tchier † Department of Mathematics, College of Science, King Saud University, Riyadh 11 551, Saudi Arabia; [email protected] * Correspondence: [email protected] † These authors contributed equally to this work. Abstract: Herein, an efficient algorithm is proposed to solve a one-dimensional hyperbolic partial differential equation. To reach an approximate solution, we employ the q-weighted scheme to discretize the time interval into a finite number of time steps. In each step, we have a linear ordinary differential equation. Applying the Galerkin method based on interpolating scaling functions, we can solve this ODE. Therefore, in each time step, the solution can be found as a continuous function. Stability, consistency, and convergence of the proposed method are investigated. Several numerical examples are devoted to show the accuracy and efficiency of the method and guarantee the validity of the stability, consistency, and convergence analysis. Keywords: interpolating scaling functions; hyperbolic equation; Galerkin method 1. Introduction Partial differential equations (PDEs) are ubiquitous in mathematically scientific fields and play an important role in engineering and physics. They arise from many purely math- ematical considerations, such as the calculus of variations and differential geometry. One of Citation: Bin Jebreen, H.; Tchier, F. On the momentous subclasses of PDEs is the hyperbolic partial differential equations (HPDEs). the Numerical Simulation of HPDEs HPDEs are used to model many phenomena such as biology, industry, atomic physics, and Using q-Weighted Scheme and the aerospace [1–3]. Telegraph and wave equations are the most famous types of HPDEs that Galerkin Method. -
FETI-DP Domain Decomposition Method
ECOLE´ POLYTECHNIQUE FED´ ERALE´ DE LAUSANNE Master Project FETI-DP Domain Decomposition Method Supervisors: Author: Davide Forti Christoph Jaggli¨ Dr. Simone Deparis Prof. Alfio Quarteroni A thesis submitted in fulfilment of the requirements for the degree of Master in Mathematical Engineering in the Chair of Modelling and Scientific Computing Mathematics Section June 2014 Declaration of Authorship I, Christoph Jaggli¨ , declare that this thesis titled, 'FETI-DP Domain Decomposition Method' and the work presented in it are my own. I confirm that: This work was done wholly or mainly while in candidature for a research degree at this University. Where any part of this thesis has previously been submitted for a degree or any other qualification at this University or any other institution, this has been clearly stated. Where I have consulted the published work of others, this is always clearly at- tributed. Where I have quoted from the work of others, the source is always given. With the exception of such quotations, this thesis is entirely my own work. I have acknowledged all main sources of help. Where the thesis is based on work done by myself jointly with others, I have made clear exactly what was done by others and what I have contributed myself. Signed: Date: ii ECOLE´ POLYTECHNIQUE FED´ ERALE´ DE LAUSANNE Abstract School of Basic Science Mathematics Section Master in Mathematical Engineering FETI-DP Domain Decomposition Method by Christoph Jaggli¨ FETI-DP is a dual iterative, nonoverlapping domain decomposition method. By a Schur complement procedure, the solution of a boundary value problem is reduced to solving a symmetric and positive definite dual problem in which the variables are directly related to the continuity of the solution across the interface between the subdomains.