Discontinuous Galerkin and Finite Difference Methods for the Acoustic Equations with Smooth Coefficients. Mario Bencomo TRIP Review Meeting 2013

Similar documents
High Order Accurate Runge Kutta Nodal Discontinuous Galerkin Method for Numerical Solution of Linear Convection Equation

Weight-adjusted DG methods for elastic wave propagation in arbitrary heterogeneous media

Application of Nodal Discontinuous Glaerkin Methods in Acoustic Wave Modeling

arxiv: v2 [math.na] 20 Oct 2017

Fourier analysis for discontinuous Galerkin and related methods. Abstract

Lecture 9 Approximations of Laplace s Equation, Finite Element Method. Mathématiques appliquées (MATH0504-1) B. Dewals, C.

A SIMPLE TUTORIAL ON DISCONTINUOUS GALERKIN METHODS. Jennifer Proft CERMICS, ENPC. J. Proft CERMICS, ENPC

Conservation Laws & Applications

A staggered discontinuous Galerkin method for the simulation of. Rayleigh waves

Local discontinuous Galerkin methods for elliptic problems

A Space-Time Expansion Discontinuous Galerkin Scheme with Local Time-Stepping for the Ideal and Viscous MHD Equations

DISCONTINUOUS GALERKIN FINITE ELEMENT METHOD FOR THE WAVE EQUATION. SIAM J. Numer. Anal., Vol. 44, pp , 2006

SECOND ORDER TIME DISCONTINUOUS GALERKIN METHOD FOR NONLINEAR CONVECTION-DIFFUSION PROBLEMS

Pseudospectral and High-Order Time-Domain Forward Solvers

Performance comparison between hybridizable DG and classical DG methods for elastic waves simulation in harmonic domain

Interface Error Analysis for Numerical Wave Propagation

Scientific Computing WS 2017/2018. Lecture 18. Jürgen Fuhrmann Lecture 18 Slide 1

Discontinuous Galerkin Methods: Theory, Computation and Applications

Basic Principles of Weak Galerkin Finite Element Methods for PDEs

ON THE INTEGRATION OF EQUATIONS OF MOTION: FEM AND MOLECULAR DYNAMICS PROBLEMS

On Surface Meshes Induced by Level Set Functions

1 Exercise: Linear, incompressible Stokes flow with FE

THE COMPACT DISCONTINUOUS GALERKIN (CDG) METHOD FOR ELLIPTIC PROBLEMS

A note on accurate and efficient higher order Galerkin time stepping schemes for the nonstationary Stokes equations

ASM-BDDC Preconditioners with variable polynomial degree for CG- and DG-SEM

Fourier Type Error Analysis of the Direct Discontinuous Galerkin Method and Its Variations for Diffusion Equations

New class of finite element methods: weak Galerkin methods

First order BSSN formulation of Einstein s field equations

Overlapping Schwarz preconditioners for Fekete spectral elements

Energy norm a-posteriori error estimation for divergence-free discontinuous Galerkin approximations of the Navier-Stokes equations

On the Non-linear Stability of Flux Reconstruction Schemes

A space-time Trefftz method for the second order wave equation

arxiv: v2 [math.na] 8 Dec 2017

Hybridized Discontinuous Galerkin Methods

DG discretization of optimized Schwarz methods for Maxwell s equations

Discontinuous Galerkin Methods

arxiv: v1 [math.na] 29 Feb 2016

AN OPTIMALLY ACCURATE SPECTRAL VOLUME FORMULATION WITH SYMMETRY PRESERVATION

Weak Galerkin Finite Element Scheme and Its Applications

Lecture 10 Polynomial interpolation

Hybridized DG methods

Approximation SEM-DG pour les problèmes d ondes elasto-acoustiques

Well-balanced DG scheme for Euler equations with gravity

The CG1-DG2 method for conservation laws

Unified Hybridization Of Discontinuous Galerkin, Mixed, And Continuous Galerkin Methods For Second Order Elliptic Problems.

Math 660-Lecture 15: Finite element spaces (I)

A New Class of High-Order Energy Stable Flux Reconstruction Schemes for Triangular Elements

c 2006 Society for Industrial and Applied Mathematics BJÖRN ENGQUIST

The direct discontinuous Galerkin method with symmetric structure for diffusion problems

IMPACTS OF SIGMA COORDINATES ON THE EULER AND NAVIER-STOKES EQUATIONS USING CONTINUOUS/DISCONTINUOUS GALERKIN METHODS

Numerical resolution of discontinuous Galerkin methods for time dependent. wave equations 1. Abstract

A Robust CFL Condition for the Discontinuous Galerkin Method on Triangular Meshes

Numerical methods for PDEs FEM convergence, error estimates, piecewise polynomials

General linear methods for time-dependent PDEs

Finite Elements. Colin Cotter. January 18, Colin Cotter FEM

Wave propagation in discrete heterogeneous media

AProofoftheStabilityoftheSpectral Difference Method For All Orders of Accuracy

Discontinuous Galerkin method for hyperbolic equations with singularities

A note on discontinuous Galerkin divergence-free solutions of the Navier-Stokes equations

A space-time Trefftz method for the second order wave equation

High-Order Methods for Diffusion Equation with Energy Stable Flux Reconstruction Scheme

An efficient implementation of the divergence free constraint in a discontinuous Galerkin method for magnetohydrodynamics on unstructured meshes

On an Approximation Result for Piecewise Polynomial Functions. O. Karakashian

Efficient wave propagation on complex domains

A High-Order Discontinuous Galerkin Method for the Unsteady Incompressible Navier-Stokes Equations

LECTURE # 0 BASIC NOTATIONS AND CONCEPTS IN THE THEORY OF PARTIAL DIFFERENTIAL EQUATIONS (PDES)

Approximation of fluid-structure interaction problems with Lagrange multiplier

A recovery-assisted DG code for the compressible Navier-Stokes equations

The Discontinuous Galerkin Finite Element Method

Analysis of Hybrid Discontinuous Galerkin Methods for Incompressible Flow Problems

Trefftz-discontinuous Galerkin methods for time-harmonic wave problems

Partial Differential Equations and the Finite Element Method

AMS subject classifications. Primary, 65N15, 65N30, 76D07; Secondary, 35B45, 35J50

Runge-Kutta discontinuous Galerkin method with a simple and compact Hermite WENO limiter

A New Class of High-Order Energy Stable Flux Reconstruction Schemes

On the Comparison of the Finite Volume and Discontinuous Galerkin Methods

AMS 529: Finite Element Methods: Fundamentals, Applications, and New Trends

ON THE BENEFIT OF THE SUMMATION-BY-PARTS PROPERTY ON INTERIOR NODAL SETS

Graded Mesh Refinement and Error Estimates of Higher Order for DGFE-solutions of Elliptic Boundary Value Problems in Polygons

Méthodes de Galerkine Discontinues pour l imagerie sismique en domaine fréquentiel

Weighted Essentially Non-Oscillatory limiters for Runge-Kutta Discontinuous Galerkin Methods

A Discontinuous Galerkin Moving Mesh Method for Hamilton-Jacobi Equations

Numerical Solutions to Partial Differential Equations

Inverse Lax-Wendroff Procedure for Numerical Boundary Conditions of. Conservation Laws 1. Abstract

Well-balanced DG scheme for Euler equations with gravity

Numerical Methods of Applied Mathematics -- II Spring 2009

Scientific Computing WS 2018/2019. Lecture 15. Jürgen Fuhrmann Lecture 15 Slide 1

Operator Upscaling for the Wave Equation

AN EQUILIBRATED A POSTERIORI ERROR ESTIMATOR FOR THE INTERIOR PENALTY DISCONTINUOUS GALERKIN METHOD

arxiv: v1 [math.na] 11 Jul 2011

CIV-E1060 Engineering Computation and Simulation Examination, December 12, 2017 / Niiranen

Dispersion and dissipation error in high-order Runge-Kutta discontinuous Galerkin discretisations of the Maxwell equations

Seismic imaging and multiple removal via model order reduction

Block-Structured Adaptive Mesh Refinement

Arbitrary High-Order Discontinuous Galerkin Method for Solving Electromagnetic Field Problems in Time Domain

On the efficiency of the Peaceman-Rachford ADI-dG method for wave-type methods

[2] (a) Develop and describe the piecewise linear Galerkin finite element approximation of,

1. Introduction. We consider the model problem that seeks an unknown function u = u(x) satisfying

Space-time XFEM for two-phase mass transport

A Neumann series based method for photoacoustic tomography on irregular domains

Transcription:

About Me Mario Bencomo Currently 2 nd year graduate student in CAAM department at Rice University. B.S. in Physics and Applied Mathematics (Dec. 2010). Undergraduate University: University of Texas at El Paso (UTEP). 1

Discontinuous Galerkin and Finite Difference Methods for the Acoustic Equations with Smooth Coefficients Mario Bencomo TRIP Review Meeting 2013 2

Problem Statement Acoustic Equations (pressure-velocity form): ρ(x) v (x,t) + p(x,t) = 0 t (1a) 1 κ (x) p (x,t) + v(x,t) = f (x,t) t (1b) for x Ω and t [0,T ], where x = (x,z) and Ω = [0,1] 2. Boundary and initial conditions: p = 0, on Ω [0,T ] p(x,0) = p 0 (x) and v(x,0) = v 0 (x) Research focus: Analyze the computational efficiency of discontinuous Galerkin (DG) and finite difference (FD) methods in the context of the acoustic equations with smooth coefficients. 3

Problem Statement Acoustic Equations (pressure-velocity form): ρ(x) v (x,t) + p(x,t) = 0 t (1a) 1 κ (x) p (x,t) + v(x,t) = f (x,t) t (1b) for x Ω and t [0,T ], where x = (x,z) and Ω = [0,1] 2. Boundary and initial conditions: p = 0, on Ω [0,T ] p(x,0) = p 0 (x) and v(x,0) = v 0 (x) Research focus: Analyze the computational efficiency of discontinuous Galerkin (DG) and finite difference (FD) methods in the context of the acoustic equations with smooth coefficients. 3

Why Smooth Coefficients? Relevant for seismic applications: smooth trends in real data! Comparison has not been done before! Previous work with discontinuous coefficients (Wang, 2009). efficiency of DG over 2-4 FD (dome inclusion) DG resolves discontinuity interface errors 4

Focus of Talk 1 Discontinuous Galerkin (DG) Method derivation of scheme basis functions reference element 2 Locally conforming DG (LCDG) Method triangulation reference element basis functions 3 Summary and Future Work 5

Discontinuous Galerkin (DG) Method Why DG? (Cockburn, 2006; Brezzi et al., 2004; Wilcox et al., 2010) explicit time-stepping, after inverting block diagonal matrix can handle irregular meshes and complex geometries hp-adaptivity Idea: Approximate solution by piecewise polynomials on partitioned domain. Example: 1D piecewise linear approximation 6

Discontinuous Galerkin (DG) Method Why DG? (Cockburn, 2006; Brezzi et al., 2004; Wilcox et al., 2010) explicit time-stepping, after inverting block diagonal matrix can handle irregular meshes and complex geometries hp-adaptivity Idea: Approximate solution by piecewise polynomials on partitioned domain. Example: 1D piecewise linear approximation 6

Derivation of DG Semi-Discrete Scheme Let: Then, τ T, for some triangulation T on Ω test function w C (Ω) ρ v x t + p x τ = 0 = ρ v x t τ w dx + p x w dx = 0 I.B.P. and replace p with numerical flux p in boundary integral, τ ρ v x t τ w dx p w x τ dx + p w n x dσ = 0 IBP = ρ v x τ t τ w dx + p x τ w dx + (p p)w n x dσ = 0 (2) 7

Derivation of DG Semi-Discrete Scheme Let: Then, τ T, for some triangulation T on Ω test function w C (Ω) ρ v x t + p x τ = 0 = ρ v x t τ w dx + p x w dx = 0 I.B.P. and replace p with numerical flux p in boundary integral, τ ρ v x t τ w dx p w x τ dx + p w n x dσ = 0 IBP = ρ v x τ t τ w dx + p x τ w dx + (p p)w n x dσ = 0 (2) 7

Nodal Basis Functions Finite dimensional space W h : W h = {w : w τ P N (τ), τ T } Nodal DG: Use nodal basis, i.e., where P N (τ) = span{l τ j (x)}n j=1 τ T, Lagrange polynomials l τ j (xτ i ) = δ ij for given nodal set {x τ i }N i=1 τ N = 1 2 (N + 1)(N + 2), a.k.a., degrees of freedom per triangular element 8

Nodal Basis Functions Finite dimensional space W h : W h = {w : w τ P N (τ), τ T } Nodal DG: Use nodal basis, i.e., where P N (τ) = span{l τ j (x)}n j=1 τ T, Lagrange polynomials l τ j (xτ i ) = δ ij for given nodal set {x τ i }N i=1 τ N = 1 2 (N + 1)(N + 2), a.k.a., degrees of freedom per triangular element 8

Nodal Sets Example of nodal sets {x τ j }N j=1 : (a) N = 1,N = 3 (b) N = 2,N = 6 (c) N = 3,N = 10 9

DG Semi-Discrete Scheme Find v x,p W h such that τ ρ v x t τ w dx p x τ w dx + (p p)w n x dσ = 0 for all w W h and τ T. Note: v x W h = v x (x,t) τ = N j=1 v x (x τ j,t)lτ j (x), where {v x (x τ j,t)}n j=1 are unknowns. Same for v z and p, and numerical fluxes v x,v z,p. 10

Nodal Coefficient Vectors R N R N+1 v x (t) := [v x (x τ 1,t),v x(x τ 2,t),...,v x(x τ N,t)] T v z (t) := p(t) := v m n (t) := [v n (x τ m 1,t),v n (x τ m 2,t),...,v n (x τ m N+1,t)] T p m (t) := (v m n ) (t) := [vn(x τ m 1,t),vn(x τ m 2,t),...,vn(x τ m N+1,t)] T (p m ) (t) := where v n (x τ m j,t) = n m x v x (x τ m j,t) + n m z v z (x τ m j,t), and similar for v n(x τ m j,t). 11

DG Semi-Discrete Scheme From ρ v x τ t τ w dx + p x τ w dx + (p p)w n x dσ = 0, to where: MR d dt v x(t) + S x p(t) + 3 m=1 mass matrix (M) ij := mass matrix (M m ) ij := n m x M m ((p m ) p m )(t) = 0, τ eτ m l τ i τ l τ i lτ j dx, l τ i lτ m j dσ, in R N N in R N (N+1) stiffness matrix (S x ) ij := l τ j x dx, in RN N ρ-matrix (R) ij := δ ij ρ(x τ j ), in RN N 12

DG Semi-Discrete Scheme From ρ v x τ t τ w dx + p x τ w dx + (p p)w n x dσ = 0, to where: MR d dt v x(t) + S x p(t) + 3 m=1 mass matrix (M) ij := mass matrix (M m ) ij := n m x M m ((p m ) p m )(t) = 0, τ eτ m l τ i τ l τ i lτ j dx, l τ i lτ m j dσ, in R N N in R N (N+1) stiffness matrix (S x ) ij := l τ j x dx, in RN N ρ-matrix (R) ij := δ ij ρ(x τ j ), in RN N 12

DG Semi-Discrete Scheme System of ODE s: R d dt v x(t) = D x p(t) + for each τ T, where 3 m=1 n m x L m ((p m ) p m )(t) D x = M 1 S x, L m = M 1 M m. Similar result for other equations: R d dt v z(t) = D z p(t) + 3 m=1 K 1 d dt p(t) = f(t) Dx v x (t) D z v z (t) where n m z L m ((p m ) p m )(t) 3 m=1 L m ((v n ) v n )(t) (K ) ij = δ ij κ(x τ j ), (f) i = f l τ i dx, D z = M 1 S z. τ 13

DG Semi-Discrete Scheme System of ODE s: R d dt v x(t) = D x p(t) + for each τ T, where 3 m=1 n m x L m ((p m ) p m )(t) D x = M 1 S x, L m = M 1 M m. Similar result for other equations: R d dt v z(t) = D z p(t) + 3 m=1 K 1 d dt p(t) = f(t) Dx v x (t) D z v z (t) where n m z L m ((p m ) p m )(t) 3 m=1 L m ((v n ) v n )(t) (K ) ij = δ ij κ(x τ j ), (f) i = f l τ i dx, D z = M 1 S z. τ 13

DG Semi-Discrete Scheme Acoustic equations: DG scheme: R d dt v x(t) = D x p(t) + R d dt v z(t) = D z p(t) + ρ t v x = x p ρ t v z = z p 1 κ t p = f v 3 m=1 3 m=1 K 1 d dt p(t) = f(t) Dx v x (t) D z v z (t) n m x L m ((p m ) p m )(t) n m z L m ((p m ) p m )(t) 3 m=1 L m ((v n ) v n )(t) 14

Visualizing DG After time discretization (leapfrog): v n+1/2 x = v n 1/2 x tr [D 1 x p n + 3 m=1 n m x L m ((p m ) p m ) n ] t n+1/2 t n t n 1/2 15

Visualizing DG After time discretization (leapfrog): v n+1/2 x = v n 1/2 x tr 1 [D x p n + 3 m=1 n m x L m ((p m ) p m ) n ] t n+1/2 t n t n 1/2 16

Visualizing DG After time discretization (leapfrog): v n+1/2 x = v n 1/2 x tr 1 [D x p n + 3 m=1 n m x L m ((p m ) p m ) n ] t n+1/2 t n t n 1/2 17

Visualizing DG After time discretization (leapfrog): v n+1/2 x = v n 1/2 x tr 1 [D x p n + 3 m=1 n m x L m ((p m ) p m ) n ] t n+1/2 t n t n 1/2 18

Use of Reference Element in DG Reference triangle ˆτ: (-1,1) s r (-1,-1) (1,-1) Idea: Carry computations in ˆτ construct nodal set and basis functions in ˆτ: τ {l j } N j=1 s.t. l j(r i ) = δ ij for {r i } N i=1 ˆτ mass matrix computations l τ j lτ i dx = J(τ) l j l i dr = M = J(τ) ˆM ˆτ 19

Locally Conforming DG (LCDG) Method for Acoustics Why LCDG method? (Chung & Engquist, 2006, 2009) locally and globally energy conservative optimal convergence rate explicit time-step My contribution: nodal DG implementation of LCDG basis functions and nodal sets use of reference element 20

Locally Conforming DG (LCDG) Method for Acoustics Why LCDG method? (Chung & Engquist, 2006, 2009) locally and globally energy conservative optimal convergence rate explicit time-step My contribution: nodal DG implementation of LCDG basis functions and nodal sets use of reference element 20

Locally Conforming DG (LCDG) Method for Acoustics LCDG Idea: Enforce continuity of the pressure field and the normal component of the velocity field in a staggered manner. Example: 1D piecewise linear polynomial approximation 21

Triangulation Algorithm: unif_tri 1 given N sub, subdivide Ω into N sub N sub partition of squares 2 triangulate each square by adding a diagonal from top-left to bottom-right 3 Pick interior point at each triangle and re-triangulate Figure: Example of unif_tri for N sub = 2. 22

Triangulation Algorithm: unif_tri 1 given N sub, subdivide Ω into N sub N sub partition of squares 2 triangulate each square by adding a diagonal from top-left to bottom-right 3 Pick interior point at each triangle and re-triangulate Figure: Example of unif_tri for N sub = 2. 22

Triangulation Algorithm: unif_tri 1 given N sub, subdivide Ω into N sub N sub partition of squares 2 triangulate each square by adding a diagonal from top-left to bottom-right 3 Pick interior point at each triangle and re-triangulate Figure: Example of unif_tri for N sub = 2. 22

Triangulation Algorithm: unif_tri 1 given N sub, subdivide Ω into N sub N sub partition of squares 2 triangulate each square by adding a diagonal from top-left to bottom-right 3 Pick interior point at each triangle and re-triangulate Figure: Example of unif_tri for N sub = 2. 22

Triangulation Algorithm: unif_tri 1 given N sub, subdivide Ω into N sub N sub partition of squares 2 triangulate each square by adding a diagonal from top-left to bottom-right 3 Pick interior point at each triangle and re-triangulate Figure: Example of unif_tri for N sub = 2. 22

LCDG Approximation Spaces Pressure space P h : q P h if q τ P N (τ) q continuous on dashed edges q = 0 on Ω Velocity space V h : u V h if u τ P N (τ) 2 u n continuous dashed edges 23

LCDG Approximation Spaces Pressure space P h : q P h if q τ P N (τ) q continuous on dashed edges q = 0 on Ω Velocity space V h : u V h if u τ P N (τ) 2 u n continuous dashed edges 23

LCDG: Basis for P h Two types of pressure elements: boundary elements interior elements Reference elements: (-1,1) (-1,1) (1,1) s r (-1,-1) (1,-1) s r (-1,-1) (1,-1) 24

LCDG: Basis for P h Basis functions for boundary pressure elements (N = 1 example): 1 s Basis functions for pressure interior elements (N = 1 example): r 1 s 1 s 1 s 1 s r r r r 25

LCDG: Basis for V h Velocity elements and respective reference elements: (0, 2 3 3) velocity elements s r (0, 0) ( 1, 1 3 3) (1, 1 3 3) reference element 26

LCDG: Basis for V h Basis functions, N = 1 example: 27

LCDG Semi-Discrete Scheme For a triangulation T, find p P h and v V h such that Ω Ω ρ v t u dx B h (p,u) = 0 1 p κ t q dx + B h(v,q) = fq dx Ω for all q P h and u V h, where Bh (p,u) = p u dx + p [u ˆn] dσ Ω e Ep 0 e B h (v,q) = v q dx v ˆn[q] dσ. Ω e E e v 28

Summary standard DG motivation basis functions reference element LCDG triangulation spaces P h, V h reference elements and basis functions 29

Pending Work and Future Directions Pending work: implementation of LCDG absorbing BC (Chung & Engquist, 2009) time discretization (leapfrog and Runge-Kutta) error analysis Future directions: non-uniform triangulation 3D acoustics elasticity equations 30

References Brezzi, F., Marini, L., and Sïli, E. (2004). Discontinuous galerkin methods for first-order hyperbolic problems. Mathematical models and methods in applied sciences, 14(12):1893-1903. Chung, E. and Engquist, B. (2009). Optimal discontinuous galerkin methods for the acoustic wave equation in higher dimensions. SIAM Journal on Numerical Analysis, 47(5):3820-3848. Chung, E. T. and Engquist, B. (2006). Optimal discontinuous galerkin methods for wave propagation. SIAM Journal on Numerical Analysis, 44(5):2131-2158. Cockburn, B. (2003). Discontinuous Galerkin methods. ZAMM-Journal of Applied Mathematics and Mechanics/ Zeitschrift für Angewandte Mathematik und Mechanik, 83(11):731-754. Wilcox, Lucas C., et al. A high-order discontinuous Galerkin method for wave propagation through coupled elasticðacoustic media. Journal of Computational Physics 229.24 (2010): 9373-9396. Hesthaven, J. S. and Warburton, T. (2007). Nodal discontinuous Galerkin methods: algorithms, analysis, and applications, volume 54. Springer. Wang, X. (2009). Discontinuous Galerkin Time Domain Methods for Acoustics and Comparison with Finite Difference Time Domain Methods. Master s thesis, Rice University, Houston, Texas. Warburton, T. and Embree, M. (2006). The role of the penalty in the local discontinuous Galerkin method for Maxwell s eigenvalue problem. Computer methods in applied mechanics and engineering, 195(25):3205-3223. 31