Skip to main content

Efficient Solution of Elliptic Partial Differential Equations via Effective Combination of Mesh Quality Metrics, Preconditioners, and Sparse Linear Solvers

  • Conference paper
Proceedings of the 19th International Meshing Roundtable

Abstract

In this paper, we study the effect the choice of mesh quality metric, preconditioner, and sparse linear solver have on the numerical solution of elliptic partial differential equations (PDEs). We smoothe 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 vertex condition number metrics in addition to interpolation-based, scale-variant and scale-invariant metrics. We employ the Jacobi, incomplete LU, and SSOR 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/linear solver combination for the numerical solution of various elliptic PDEs.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

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

    MathSciNet  Google Scholar 

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

    Google Scholar 

  3. Berzins, M.: Mesh quality - Geometry, error estimates, or both? In: Proc. of the 7th International Meshing Roundtable, Sandia National Laboratories, pp. 229–237 (1998)

    Google Scholar 

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

    Article  MATH  Google Scholar 

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

    Google Scholar 

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

    Article  MATH  MathSciNet  Google Scholar 

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

    Article  MATH  MathSciNet  Google Scholar 

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

    Article  MATH  MathSciNet  Google Scholar 

  9. Chatterjee, A., Shontz, S.M., Raghavan, P.: Relating Mesh Quality Metrics to Sparse Linear Solver Performance. In: SIAM Computational Science and Engineering, Costa Mesa, CA (2007)

    Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

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

    MATH  MathSciNet  Google Scholar 

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

    Google Scholar 

  14. Balay, S., Buschelman, K., Gropp, W.D., Kaushik, D., Knepley, M.G., McInnes, L.C., Smith, B.F., Zhang, H.: PETSc Webpage (2009), http://www.mcs.anl.gov/petsc

  15. Becker, E.B., Carey, G.F., Oden, J.T.: Finite Elements: An Introduction. Prentice-Hall, Englewood Cliffs (1981)

    MATH  Google Scholar 

  16. Munson, T.: Mesh Shape-Quality Optimization Using the Inverse Mean-Ratio Metric. Mathematical Programming 110, 561–590 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  17. Knupp, P.: Achieving finite element mesh quality via optimization of the Jacobian matrix norm and associated quantities, Part II - A framework for volume mesh optimization and the condition number of the Jacobian matrix. Internat. J. Numer. Methods Engrg. 48, 1165–1185 (2000)

    Article  MATH  Google Scholar 

  18. Nocedal, J., Wright, S.: Numerical Optimization, 2nd edn. Springer, Heidelberg (2006)

    MATH  Google Scholar 

  19. Baker, A.H., Jessup, E.R., Kolev, T.V.: A simple strategy for varying the restart parameter in GMRES(m). J. Comput. Appl. Math. 230, 751–761 (2009)

    Article  MATH  MathSciNet  Google Scholar 

  20. Barrett, R., Berry, M., Chan, T.F., Demmel, J., Donato, J., Dongarra, J., Eijkhout, V., Pozo, R., Romine, C., Van der Vorst, H.: Templates for the Solution of Linear Systems: Building Blocks for Iterative Methods, 2nd edn. SIAM, Philadelphia (1994)

    Google Scholar 

  21. Saad, Y.: Iterative Methods for Sparse Linear Systems, 2nd edn. Society for Industrial and Applied Mathematics (2003)

    Google Scholar 

  22. Cyberstar Webpage, http://www.ics.psu.edu/research/cyberstar/index.html

  23. Shewchuk, J.R.: Triangle: Engineering a 2D Quality Mesh Generator and Delaunay Triangulator. In: Lin, M.C., Manocha, D. (eds.) FCRC-WS 1996 and WACG 1996. LNCS, vol. 1148, pp. 203–222. Springer, Heidelberg (1996)

    Chapter  Google Scholar 

  24. Si, H.: TetGen - A Quality Tetrahedral Mesh Generator and Three-Dimensional Delaunay Triangulator, http://tetgen.berlios.de/

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Kim, J., Sastry, S.P., Shontz, S.M. (2010). Efficient Solution of Elliptic Partial Differential Equations via Effective Combination of Mesh Quality Metrics, Preconditioners, and Sparse Linear Solvers. In: Shontz, S. (eds) Proceedings of the 19th International Meshing Roundtable. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15414-0_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-15414-0_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15413-3

  • Online ISBN: 978-3-642-15414-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics