Convergence Improvement Method for Computational Fluid Dynamics Using Building-Cube Method

  • Takahiro Fukushige
  • Toshihiro Kamatsuchi
  • Toshiyuki Arima
  • Seiji Fujino
Conference paper
Part of the Lecture Notes in Computational Science and Engineering book series (LNCSE, volume 74)


Computational Fluid Dynamics (CFD) has become an important tool for aerodynamics by the improvements of computer performance and CFD algorithm itself. However, the computational time of CFD continues to increase, while progress of computer has been made. One of the reasons is considered that application of CFD has become more complex. For example, CFD is employed to estimate aerodynamics performance for a complex shaped object such as formula one car (Fig. 1) Concerning complex shape, however, the problem of grid generation still remains. It requires so much time and labor. To overcome the problem in meshing for complex-shaped object, we have already proposed an algorithm [3]. The algorithm consists of two approaches. One is Immersed Boundary method [6], and the other is Building-Cube Method (BCM) [4]. The basic idea of Immersed Boundary method is applied to cells in the vicinity of solid boundary, and Cartesian grid method is performed for other cells. These approaches have several advantages except for solution convergence. In this paper, Implicit Residual Smoothing (IRS) [2] is proposed for improvements of solution convergence.


Computational Fluid Dynamics Computational Grid AIAA Journal Immerse Boundary Method Convergence History 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    P. H. Cook, M. A. McDonald, and M. C. P. Firmin. Airfoil rae2822-pressure distributions, and boundary layer and wake measurements. Experimental Data Base for Computer Program Assessment, AGARD-AR-138, 1979.Google Scholar
  2. 2.
    A. Jameson. The evolution of computational methods in aerodynamics. Journal of Applied Mechanics Review, 50(2):1052–1070, 1983.zbMATHCrossRefMathSciNetGoogle Scholar
  3. 3.
    T. Kamatsuchi. Turbulent flow simulation around complex geometries with cartesian grid method. SIAM J. Sci. Stat. Comput, 13(2):631–644, 2007.Google Scholar
  4. 4.
    K. Nakahashi and L. S. Kim. Building-cube method for large-scale, high resolution flow computations. AIAA Paper, 2004-0423, 2004.Google Scholar
  5. 5.
    S. Obayashi and G. P. Guruswamy. Convergence acceleration of a navier-stokes solver for efficient static aeroelastic computations. AIAA Journal, 33(6):1134–1141, 1995.zbMATHCrossRefGoogle Scholar
  6. 6.
    C. S. Peskin. Flow patterns around heart valves: A numerical method. Journal of Computational Physics, 10(2):252–271, 1972.zbMATHCrossRefGoogle Scholar
  7. 7.
    P. R. Spalart and S. R. Allmaras. A one-equation turbulence model for aerodynamic flows. AIAA Paper, 92-0439, 1992.Google Scholar
  8. 8.
    J. M. Weiss and W. A.Smith. Preconditioning applied to variable and constant density time-accurate flows on unstructured meshes. AIAA Journal, 33(11):2050–2057, 1995.zbMATHCrossRefGoogle Scholar
  9. 9.
    S. Yamamoto and H. Daiguji. Higher-order-accurate upwind schemes for solving the compressible euler and navier-stokes equations. Computer and Fluids, 22(2/3):259–270, 1993.zbMATHCrossRefMathSciNetGoogle Scholar
  10. 10.
    S. Yoon and A. Jameson. Lower-upper symmetric-gauss-seidel method for euler and navier-stokes equations. AIAA Journal, 26(9):1025–1026, 1998.CrossRefGoogle Scholar

Copyright information

© Springer Berlin Heidelberg 2010

Authors and Affiliations

  • Takahiro Fukushige
    • 1
  • Toshihiro Kamatsuchi
    • 1
  • Toshiyuki Arima
    • 1
  • Seiji Fujino
    • 2
  1. 1.Fundamental Technology Research CenterHonda R&D Co.,Ltd.SaitamaJapan
  2. 2.Kyushu UniversityFukuoka-shiJapan

Personalised recommendations