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A numerical investigation on the interplay amongst geometry, meshes, and linear algebra in the finite element solution of elliptic PDEs

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Abstract

In this paper, we study the effect of the choice of mesh quality metric, preconditioner, and sparse linear solver on the numerical solution of elliptic partial differential equations (PDEs). We smooth meshes on several geometric domains using various quality metrics and solve the associated elliptic PDEs using the finite element method. The resulting linear systems are solved using various combinations of preconditioners and sparse linear solvers. We use the inverse mean ratio and radius ratio metrics in addition to conditioning-based scale-invariant and interpolation-based size-and-shape metrics. We employ the Jacobi, SSOR, incomplete LU, and algebraic multigrid preconditioners and the conjugate gradient, minimum residual, generalized minimum residual, and bi-conjugate gradient stabilized solvers. We focus on determining the most efficient quality metric, preconditioner, and linear solver combination for the numerical solution of various elliptic PDEs with isotropic coefficients. We also investigate the effect of vertex perturbation and the effect of increasing the problem size on the number of iterations required to converge and on the solver time. In this paper, we consider Poisson’s equation, general second-order elliptic PDEs, and linear elasticity problems.

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References

  1. Babuska I, Suri M (1994) The p and h-p versions of the finite element method, basic principles, and properties. SIAM Rev 35:579–632

    MathSciNet  Google Scholar 

  2. Berzins M (1997) Solution-based mesh quality for triangular and tetrahedral meshes. In: Proceedings of the 6th International Meshing Roundtable, Sandia National Laboratories, New Mexico, pp 427–436

  3. Berzins M (1998) Mesh quality—geometry, error estimates, or both? In: Proceedings of the 7th International Meshing Roundtable, Sandia National Laboratories, New Mexico, pp 229–237

  4. Fried E (1972) Condition of finite element matrices generated from nonuniform meshes. AIAA J 10:219–221

    Article  MATH  Google Scholar 

  5. Shewchuk J (2002) What is a good linear element? Interpolation, conditioning, and quality measures. In: Proceedings of the 11th International Meshing Roundtable, Sandia National Laboratories, New Mexico, pp 115–126

  6. Du Q, Huang Z, Wang D (2005) Mesh and solver co-adaptation in finite element methods for anisotropic problems. Numer Meth Part D E 21:859–874

    Article  MathSciNet  MATH  Google Scholar 

  7. Du Q, Wang D, Zhu L (2009) On mesh geometry and stiffness matrix conditioning for general finite element spaces. SIAM J Numer Anal 47(2):1421–1444

    Article  MathSciNet  MATH  Google Scholar 

  8. Ramage A, Wathen A (1994) On preconditioning for finite element equations on irregular grids. SIAM J Matrix Anal Appl 15:909–921

    Article  MathSciNet  MATH  Google Scholar 

  9. Chatterjee A, Shontz SM, Raghavan P (2007) Relating mesh quality metrics to sparse linear solver performance. In: Proceedings of the SIAM Conference on Computational Science and Engineering, Costa Mesa

  10. Mavripilis D (2002) An assessment of linear versus nonlinear multigrid methods for unstructured mesh solvers. J Comput Phys 175:302–325

    Article  Google Scholar 

  11. Batdorf M, Freitag L, Ollivier-Gooch C (1997) Computational study of the effect of unstructured mesh quality on solution efficiency. In: Proceedings of the 13th CFD Conference, AIAA, Reston

  12. Freitag L, Ollivier-Gooch C (2000) A cost/benefit analysis of simplicial mesh improvement techniques as measured by solution efficiency. Int J Comput Geom Appl 10:361–382

    MathSciNet  MATH  Google Scholar 

  13. Bhowmick S, Raghavan P, McInnes LC, Norris B (2004) Faster PDE-based simulations using robust composite linear solvers. Futur Gener Comput Syst 20(3):373–387

    Google Scholar 

  14. Brewer M, Freitag Diachin L, Knupp P, Leurent T, Melander D (2003) The Mesquite Mesh Quality Improvement Toolkit. In: Proceedings of the 12th International Meshing Roundtable, Sandia National Laboratories, New Mexico, pp 239–250

  15. Balay S, Brown J, Buschelman K, Dalchin L, Eijkhout V, Gropp W, Karpeev D, Kaushik D, Knepley M, McInnes LC, Minden V, Abhyankar S, Smith B, Zhang H (2011) PETSc Webpage. http://www.mcs.anl.gov/petsc

  16. Munson T (2007) Mesh shape-quality optimization using the inverse mean-ratio metric. Math Program 110:561–590

    Article  MathSciNet  MATH  Google Scholar 

  17. Barrett R, Berry M, Chan TF, Demmel J, Donato J, Dongarra J, Eijkhout V, Pozo R, Romine C, Van der Vorst H (1994) Templates for the solution of linear systems: building blocks for iterative methods, 2nd edn. SIAM, Bangkok

  18. Brandt A, McCormick S, Ruge J (1985) Algebraic multigrid for sparse matrix equations. Cambridge University Press, Cambridge

    Google Scholar 

  19. Hestenes MR, Stiefel E (1952) Methods of conjugate gradients for solving linear systems. J Res Natl Bur Stand 49:409–436

    Article  MathSciNet  MATH  Google Scholar 

  20. Paige CC, Saunders MA (1975) Solution of sparse indefinite systems of linear equations. SIAM J Numer Anal 12:617–629

    Article  MathSciNet  MATH  Google Scholar 

  21. Saad Y, Schultz M (1986) GMRES: a generalized minimal residual algorithm for solving nonsymmetric linear systems. SIAM J Sci Comput 7(3):856–869

    Article  MathSciNet  MATH  Google Scholar 

  22. van der Vorst HA (1992) Bi-CGSTAB: A fast and smoothly converging variant of Bi-CG for the solution of nonsymmetric linear systems. SIAM J Sci Comput 13:631–644

    Article  MATH  Google Scholar 

  23. Kim J, Sastry SP, Shontz SM (2010) Efficient solution of elliptic partial differential equations via effective combination of mesh quality metrics, preconditioners, and sparse linear solvers. In: Proceedings of the 19th international meshing roundtable, Sandia National Laboratories, New Mexico, pp 103–120

  24. Becker EB, Carey GF, Oden JT (1981) Finite elements: an introduction. Prentice-Hall. Englewood Cliffs

    MATH  Google Scholar 

  25. Kwon Y, Bang H (2000) The finite element method using Matlab, 2nd edn. CRC Press, Boca Raton

    Google Scholar 

  26. Oh S, Yim J (2005) Optimal finite element mesh for elliptic equation of divergence form. Appl Math Comput 162:969–989

    Article  MathSciNet  MATH  Google Scholar 

  27. Diachin L, Knupp P, Munson T, Shontz S (2006) A comparison of two optimization methods for mesh quality improvement. Eng Comput 22(2):61–74

    Article  Google Scholar 

  28. Nocedal J, Wright S (2006) Numerical optimization, 2nd edn. Springer, Berlin

    MATH  Google Scholar 

  29. Brewer M (2008) Obtaining smooth mesh transitions using vertex optimization. Int J Numer Methods Eng 75:555–576

    Article  MathSciNet  MATH  Google Scholar 

  30. Johnson C (1987) Numerical solution of partial differential equations by the finite element method. Cambridge University Press, Cambridge

    MATH  Google Scholar 

  31. Baker AH, Jessup ER, Kolev TzV (2009) simple strategy for varying the restart parameter in GMRES(m). J Comput Appl Math 230:751–761

    Article  MathSciNet  MATH  Google Scholar 

  32. Saad Y (2003) Iterative methods for sparse linear systems, 2nd edn. Society for Industrial and Applied Mathematics, Philadelphia

    Book  MATH  Google Scholar 

  33. Cyberstar webpage. http://www.ics.psu.edu/research/cyberstar/index.html

  34. Shewchuk JR (1996) Triangle: engineering a 2D quality mesh generator and Delaunay Triangulator. Lect Notes Comput Sci 2(1148):203–222

    Article  Google Scholar 

  35. Norris B, McInnes L, Bhowmick S, Li L (2007) Adaptive numerical components for PDE-based simulations. In: ICIAM Proceedings of Applied Mathematics and Mechanics

  36. Shontz Suzanne M, Knupp Patrick (2008) The effect of vertex reordering on 2D local mesh optimization efficiency. In: Proceedings of the 17th International Meshing Roundtable, pp 107–124

  37. Vaněk P, Mandel J, Brezina M (1996) Algebraic multigrid by smoothed aggregation for second and fourth order elliptic problems. Computing 56(3):179–196

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgments

The authors would like to thank Anirban Chatterjee and Padma Raghavan for interesting the third author in this area of research and Nicholas Voshell for helpful discussions. This work was funded in part by NSF Grant CNS 0720749 and an Institute for Cyberscience grant from The Pennsylvania State University. This work was supported in part through instrumentation funded by the National Science Foundation through Grant OCI-0821527. They also wish to thank the two anonymous referees for their careful reading of the paper and for their helpful suggestions which strengthened it.

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Correspondence to Suzanne M. Shontz.

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Kim, J., Sastry, S.P. & Shontz, S.M. A numerical investigation on the interplay amongst geometry, meshes, and linear algebra in the finite element solution of elliptic PDEs. Engineering with Computers 28, 431–450 (2012). https://doi.org/10.1007/s00366-011-0231-0

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