Fast Multipole BEM for Structural Acoustics Simulation
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1 Fast Boundary Element Methods in Industrial Applications Fast Multipole BEM for Structural Acoustics Simulation Matthias Fischer and Lothar Gaul Institut A für Mechanik, Universität Stuttgart, Germany Contents Symmetric Galerkin BEM for acoustics Fast multilevel multipole algorithm Parameter study: accuracy vs. efficiency Preconditioning of the multipole BEM
2 Introduction Acoustic behaviour is a major concern in product development. Noise level influences buying decision, legal requirements must be fulfilled, acoustic instabilities can cause failure. = Efficient and reliable numerical simulation tools are required Boundary Element Method (BEM) is suitable. Excellent accuracy, easy mesh generation, infinite domains pose no difficulties, limited by fully populated matrices. = Reduction of numerical cost by Multipole BEM
3
4 9 / BEM for Acoustics Helmholtz equation 2 u(x) + k 2 u(x) = 0 in exterior domain Ω e with boundary conditions Fundamental solution in R 3 U (x, y) = 1 4π e ikr r u(x) = p(x), x Γ D, u(x) n Sommerfeld radiation condition u r iku c r 2 at r = q(x), x Γ N 9 A Representation formula u(y) = U (x, y) u(x) n ds x + Γ Γ U (x, y) n x u(x) ds x, y Ω e
5 Calderon Projector Limit on the smooth boundary, Ω e y Γ u(y) = 1 2 u(y) U (x, y) u(x) Γ n ds U (x, y) x + u(x) ds x, y Γ Γ n }{{}} x {{} (V u/ n)(y) (Ku)(y) Normal derivative on the smooth boundary, Ω e y Γ u(y) n = 1 u(y) 2 n U (x, y) u(x) Γ n y n ds 2 U (x, y) x + u(x) ds x Γ n x n y }{{}}{{} (K u/ n)(y) (Du)(y), y Γ Yielding the Calderon projector u = 1 2 I + K V 1 u/ n D 2 I K u u/ n
6 Symmetric Galerkin Formulation Hypersingular BIE for Neumann problem u/ n = q on Γ 2 U (x, y) u(x) ds x = 1 n x n y 2 q(y) + U (x, y) n y Γ Γ q(x) ds x Discretization linear shape functions for pressure constant shape functions for flux u h = N i=1 q h = M k=1 ϕ i (x) u i ψ k (x) q k Testing with linear test functions ϕ j (y) N { i=1 = Γ M { k=1 ϕ j (y) Γ Γ ϕ j (y) 2 U (x, y) n x n y ( 1 2 ψ k(y) + ϕ i (x) ds x ds y Γ U (x, y) n y } u i ψ k (x) ds x ) ds y } q k for j = 1.. N Matrix formulation D h [j, i] u i = ( 1 2 I h[j, k] + K h [j, k]) q k
7 Regularization of Hypersingular Operator Hypersingular operator D h [j, i] is not integrable 2 U (x, y) ϕ j (y) ϕ i (x) ds x ds y = k 2 n x n y ϕ i (x)ϕ j (y)u (x, y) ds x ds y Γ Γ n x n y Γ Γ ϕ j (y) ϕ i (x)n x [ x (n y y U (x, y))] ds x ds y Γ Applying Stoke s theorem twice on second integral D h [j, i] = k 2 n x n y ϕ i (x)ϕ j (y)u (x, y) ds x ds y Γ Γ ((n x x ϕ i (x)) (n y y ϕ j (y)) U (x, y) ds x ds y Γ Γ }{{} curl Γ (ϕ j ) constant on linear element Γ Hypersingular operator requires evaluation of single layer potential with constant and linear shape functions.
8 Multipole BEM System matrices are fully populated high memory requirements, high computational cost For iterative solution only the matrix-vector product must be evaluated Fast multipole method (FMM) [Greengard, Rokhlin 1987] Evaluation of v = V h w for the discrete single layer potential using the FMM v l = V h [l, k] w k + w k nearfield }{{ } farfield direct evaluation V h [l, k] w k + nearfield G l j=1 ω l,j l τ l farfield U (x, y) τ k }{{} smooth function multipole expansion G k w k i=1 ds x ds y ω k,i k U (x k,i, y l,j ) } {{ } q k,i U (x k,i,y l,j )
9 @ N, O Multipole Expansion Series expansion of fundamental solution e ik y x y x = eik D+d D + d = ik (2l + 1) j l (kd) h (1) l (kd) P l ( ˆD ˆd), D > d l=0 Orthonormality on unit sphere S 2 and plane wave expansion 4πi l j l (kd)p l ( ˆD ˆd) = eikd s P l (s ˆD)ds S 2 eik D+d D + d = ik 4π l=0 (2l + 1)i l h (1) l (kd) O N S 2 eikd s P l (s ˆD)ds Truncation of series expansion and definition of translation operators M L (s, D) = L l=0 (2l + 1)i l h (1) l (kd)p l (s ˆD)
10 , Translation Operators Advantages of series expansion separation of local distance d and translation D diagonal form, simple translation to new expansion center N N O O Task: For two well-separated sets of points, {x k } M k=1 and {y l} N l=1, compute potential Φ(y l ) = M k=1 q ku (x k, y l ) Direct evaluation: Numerical cost O(M N) Evaluation using translation operators Φ(y l ) = ik 4π S 2 eik(y l y 0 ) s M L (s, y 0 x 0 ) Numerical cost O(M) + O(N) M e ik(x 0 x k ) s q k k=1 }{{} farfield signature F (s) ds.
11 Multilevel FMM Further improvement of efficiency by multilevel scheme Hierarchical tree (levels l = 0,..., m) of element clusters (diameter d l ) Nearfield N: Distance D < c d l /2 Interaction list I: c d l /2 < D < c d l 1 /2 Algorithm: nearfield is evaluated directly by standard BEM farfield signature is transformed to the interaction list using translation operators M L and shifted to father cluster downward pass of entries in the interation lists, recovering of solution at integration points
12 Numerical Cost Numerical cost for kh = const constant error in engineering practice Required expansion length L l = kd l + p ln(kd l + π) Integration on unit sphere S 2 : 2L 2 l quadrature points 2L2 l coefficients for F (s) Application of translation operator M L Interpolation scheme for shift of farfield representation F (s) to father cluster Conversion of farfield representation F l (s) to multipole coefficients Algorithm based on FFT: L l FFTs of length 2L l 2L l 1D multipole expansions of length L l Adding zeros in original multipole expansion Conversion of multipole coefficients to farfield representation F l 1 (s) Translation of F l 1 (s) to new center Nearfield computation Nearfield size log 2 N for each cluster Numerical cost for total cluster tree O(N log 2 N)
13 Numerical Example: L-Shape L-Shape domain 1 m x 1 m x 1 m, monopole source applied inside, analytical Neumann data is boundary condition, exterior acoustic problem, 10 elements per wave length. Preconditioning with single layer potential. Distance factor c = 4, expansion factor p = 2. elements h k tnear iterations t / iteration Dir error m 5 m s s m 10 m s s m 20 m s s m 40 m s s 0.08
14 N log 2 N Complexity 10 2 time per iteration N log 2 N 10 1 time / sec 10 0 Predicted complexity N log 2 N 10 1 kh = = const elements
15 Error of Matrix-Vector Product error distance factor c Hypersingular operator evaluated on L-shape (k = 5 m 1 ) For cluster diameter d Distance factor c Nearfield: D < c d expansion factor p Expansion factor p L = kd + p ln(kd + π) Instability of multipole expansion no concern for engineering accuracies. Faster convergence of multipole expansion for larger near-field. Choice of distance and expansion factors determines efficiency of algorithm.
16 Efficiency of Multipole-BEM Computing time for L-shape, 2816 elements, k = 5 m 1 Parameters chosen to yield error < 10 3 compared to standard BEM, E I J =? A - N F = I E. =? J H * - $! #! " "! # " # ' & ' % # " & # $! $ & $ A = H B E 5 A J K F. 1 J A H 5 K J E # # # + F K J = J E = J E A I A?
17 Why Preconditioning? Fine meshes and high frequencies in Multipole-BEM simulations yield ill-conditioned systems of equations and high iteration counts. High iteration counts are expensive Multipole algorithm carried out for each iteration Good convergence of GMRES without restart, only, need to store each iterate Solve: P 1 Ax = P 1 b Requirements for preconditioner P Low condition number κ(p 1 A) optimum: κ(p 1 A) = const, independent of mesh size and frequency Application of P 1 with numerical cost of at most O(N log 2 N)
18 O / Approximate Inverse Preconditioner Basic idea Operator splitting A = A 0 + Ã with A 0 bounded. Eigenvalues of A 1 0 A = I + A 1 0 Ã cluster at 1. improved convergence of iterative solver. Construction of A 0 for hyper-singular operator 2 U (x, y) (Du)(y) = u(x) ds x Γ 0 n x n y Γ 2 U (x, y) n x n y u(x) ds x Two element layers is a reasonable choice for Γ 0. A 0 is a sparse matrix, but how to compute A 1 0? Approximate inverse preconditioner Minimize I A 0 P 1 P 1 A 1 0 Column-oriented algorithm, minimize e j A 0 (P 1 ) j separately /
19 Preconditioning using Boundary Integral Operators Based on spectral equivalence inequality which guarantees κ(p 1 A) < c 2 c 1. c 1 (P u, u) (Au, u) c 2 (P u, u) From the Calderon projector one finds the relations V D = 1 4 I K2, DV = 1 4 I (K ) 2, suggesting the single layer potential as an efficient preconditioner for the hyper singular operator. The preconditioner matrix is found as [Steinbach and Wendland 98] P 1 = M T V M 1. Preconditioner requires application of the single layer potential V and, twice, the inverse of the mass matrix M. Same complexity as matrix-vector product.
20 Preconditioning for L-shape no preconditioner approximate inverse single layer potential 400 no preconditioner approximate inverse single layer potential 60 k = 5 m kh = = const iterations 50 iterations h 1 / m h 1 / m 1 high iteration counts for fine meshes and high frequencies approximate inverse preconditioner reduces iteration count by a third, very cheap numerical cost preconditioning using single layer potential suppresses influence of mesh size, still strong influence of frequency
21 Sound Radiation from a Disk Brake Model of disk brake for FEM modal analysis, normal velocity of 12th eigenmode at 3720 Hz. Multipole-BEM simulation of acoustic field, 4422 triangular elements, computational time nearfield matrix 10 min. GMRES, residuum 10 3 preconditioner iterations time for solution without min approximate inverse min single layer potential min Structural mode shape provided by Robert Bosch GmbH, Zentralbereich Forschung und Vorausentwicklung, Angewandte Physik.
22 Conclusions Galerkin BEM for acoustic simulation. Fast multilevel multipole algorithm. Preconditioners for improved efficiency. Outlook Preconditioners that reduce influence of wave number. - Current Master thesis: Multilevel Preconditioners for the Multipole BEM in Acoustics. Use of fast multipole BEM for acoustic structure interaction. - Mortar coupling and efficient solution of the saddle point problem.
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