Advertisement

Automatic optimal block decomposition for structured mesh generation using genetic algorithm

  • Lu Zhao
  • Yong Liu
  • Chi Zhang
  • Xiang Zhang
Technical Paper

Abstract

This paper presents a novel system of evaluating the block quality in order to obtain the optimal block. Two algorithms for an automatic optimal block decomposition for complex geometries with arbitrary profiles based on genetic algorithm theory are proposed. One is serial optimization, which decomposes the domain according to a certain sequence. The other is global optimization, performed by optimizing all blocks simultaneously. The results will be demonstrated with an example of the basin of a turbine blade to indicate that two proposed optimization algorithms are capable of decomposing complex domains into a series of optimal structured blocks automatically in a matter of seconds. Compared to some commercial programs, the two optimization approaches not only significantly alleviate the difficulties of decomposing those domains, but also ensure the generated meshes with high quality. The quality among the blocks divided by serial optimization algorithm has a wider range. However, the global optimization algorithm can more efficiently give a decomposition scheme, in which the blocks have more even qualities.

Keywords

Structured mesh Genetic algorithm Block decomposition Optimization 

References

  1. 1.
    Park S, Lee K (1998) Automatic multiblock decomposition using hypercube ++ for grid generation. Comput Fluids 27(4):509–528MathSciNetzbMATHCrossRefGoogle Scholar
  2. 2.
    Zhang Y, Jia Y, Wang SSY, Altinakar M (2013) Composite structured mesh generation with automatic domain decomposition in complex geometries. Eng Appl Comput Fluid Mech 7(1):90–102Google Scholar
  3. 3.
    Vachal P, Garimella RV, Shashkov MJ (2004) Untangling of 2D meshes in ALE simulations. J Comput Phys 196(2):627–644zbMATHCrossRefGoogle Scholar
  4. 4.
    Li TS, Wong SM, Hon YC et al (2000) Smoothing by optimization for a quadrilateral mesh with invalid elements. Finite Elem Anal Des 34(1):37–60zbMATHCrossRefGoogle Scholar
  5. 5.
    Chen ZJ, Tristano JR, Kwok W (2004) Construction of an objective function for optimization-based smoothing. Eng Comput 20(3):184–192CrossRefGoogle Scholar
  6. 6.
    Knupp P (2012) Introducing the target-matrix paradigm for mesh optimization via node-movement. Eng Comput 28(4):419–429CrossRefGoogle Scholar
  7. 7.
    Knupp P, Margolin L, Shashkov MJ (2002) Reference Jacobian optimization-based rezone strategies for arbitrary Lagrangian Eulerian methods. J Comput Phys 176:93–128zbMATHCrossRefGoogle Scholar
  8. 8.
    Freitag LA, Plassmann P (2000) Local optimization-based simplicial mesh untangling and improvement. Int J Numer Methods Eng 49(1/2):109–125zbMATHCrossRefGoogle Scholar
  9. 9.
    Knupp PM (2001) Hexahedral and tetrahedral mesh untangling. Eng Comput 17(3):261–268zbMATHCrossRefGoogle Scholar
  10. 10.
    Charakhch’yan AA, Ivanenko SA (1997) A variational form of the Winslow grid generator. J Comput Phys 136(2):385–398MathSciNetzbMATHCrossRefGoogle Scholar
  11. 11.
    Yao Y (2007) Research on grid optimization for computational fluid dynamics. China Academy of Engineering Physics, MianyangGoogle Scholar
  12. 12.
    Mitchell SA, Tautges TJ (1995) Pillowing doublets: refining a mesh to ensure that faces share at most one edge. In: Proceedings of the international meshing roundtable, pp 231–240Google Scholar
  13. 13.
    Bommes D, Lempfer T, Kobbelt L (2011) Global structure optimization of quadrilateral meshes. Comput Graph Forum 30(2):375–384CrossRefGoogle Scholar
  14. 14.
    Peng CH, Zhang E, Kobayashi Y, Wonka P (2011) Connectivity editing for quadrilateral meshes. In: Siggraph Asia conference, vol 30, no 6, p 141Google Scholar
  15. 15.
    Dittmer JP, Jensen CG, Gottschalk M, Almy T (2006) Mesh optimization using a genetic algorithm to control mesh creation parameters. Comput Aided Des Appl 3(6):731–740CrossRefGoogle Scholar
  16. 16.
    Koljonen J, Nordling TEM, Alander JT (2008) A review of genetic algorithms in near infrared spectroscopy and chemometrics: past and future. J Near Infrared Spectrosc 16(3):189CrossRefGoogle Scholar
  17. 17.
    Kolsek Tomaz, Subelj Matjaz, Duhovnik Joze (2003) Generation of block-structured grids in complex computational domains using templates. Finite Elem Anal Des 39(12):1139–1154CrossRefGoogle Scholar
  18. 18.
    Forest S (1993) Genetic algorithms: principles of natural selection applied to computation. Science 261(5123):872–878CrossRefGoogle Scholar
  19. 19.
    Zavattieri PD, Dari EA, Buscaglia GC (1996) Optimization strategies in unstructured mesh generation. Int J Numer Methods Eng 39:2055–2071zbMATHCrossRefGoogle Scholar
  20. 20.
    Kennon SR, Dulikravich GS (2015) Generation of computational girds using optimization. AIAA J 24(7):1069–1073zbMATHCrossRefGoogle Scholar

Copyright information

© The Brazilian Society of Mechanical Sciences and Engineering 2018

Authors and Affiliations

  1. 1.College of Energy and Power EngineeringNanjing University of Aeronautics and AstronauticsNanjingPeople’s Republic of China
  2. 2.College of Materials Science and EngineeringNanjing Forestry UniversityNanjingPeople’s Republic of China

Personalised recommendations