Hostname: page-component-74656f9884-2dmjx Total loading time: 0 Render date: 2026-11-10T04:16:19.947Z Has data issue: false hasContentIssue false
  • English
  • Français

Intelligent automated grid generation for numerical simulations

Published online by Cambridge University Press:  27 February 2009

Ke-Thia Yao  and
Andrew Gelsey
Ke-Thia Yao
Affiliation:
Computer Science Department, Rutgers University, New Brunswick, NJ 08903, U.S.A.
Andrew Gelsey
Affiliation:
Computer Science Department, Rutgers University, New Brunswick, NJ 08903, U.S.A.
Get access Rights & Permissions [Opens in a new window]

Abstract

Numerical simulation of partial differential equations (PDEs) plays a crucial role in predicting the behavior-of physical systems and in modern engineering design. However, to produce reliable results with a PDE simulator, a human expert must typically expend considerable time and effort in setting up the simulation. Most of this effort is spent in generating the grid, the discretization of the spatial domain that the PDE simulator requires as input. To properly design a grid, the gridder must not only consider the characteristics of the spatial domain, but also the physics of the situation and the peculiarities of the numerical simulator. This article describes an intelligent gridder that is capable of analyzing the topology of the spatial domain and of predicting approximate physical behaviors based on the geometry of the spatial domain to automatically generate grids for computational fluid dynamics simulators. Typically, gridding programs are given a partitioning of the spatial domain to assist the gridder. Our gridder is capable of performing this partitioning. This enables the gridder to automatically grid spatial domains with a wide range of configurations.

Keywords

GriddingQualitative ReasoningSpatial ReasoningSimulation

Information

Type
Articles
Information
AI EDAM , Volume 10 , Issue 3 , June 1996 , pp. 215 - 234
Copyright
Copyright © Cambridge University Press 1996

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

REFERENCES

Andrews, A.E. (1987). Progress and challenges in the application of artificial intelligence to computational fluid dynamics. AIAA J. 26, 40–46.CrossRefGoogle Scholar
Barnhill, R.E., Farin, G., Jordan, M., & Piper, B.R. (1987). Surface/surface intersection. Comput. Aided Geomet. Design 4, 3–16.CrossRefGoogle Scholar
Chao, Y.C., & Liu, S.S. (1991). Streamline adaptive grid method for complex flow computation. Numer. Heat Transfer, Pt. B 20, 145–168.CrossRefGoogle Scholar
Chung, S.G., Kuwahara, K., & Richmond, O. (1993). Streamline-coordinate finite-difference method for hot metal deformations.J. Comput. Phys. 108, 1–7.CrossRefGoogle Scholar
Dannenhoffer, J.F. (1992). Intelligent scientific computation (No. FS–92–01) AAAI Technical Reports. AAAI Press.Google Scholar
Ellman, T., Keane, J., & Schwabacher, M. (1992). The Rutgers CAP Project Design Associate. (Technical Report CAP-TR-7). New Brunswick, NJ: Rutgers University.Google Scholar
Gelsey, A. (1995). Intelligent automated quality control for computational simulation. AI EDAM 9, 387–400.Google Scholar
Globus, A., Levit, C., & Lasinski, T. (1991). A tool for visualizing the topology of three-dimensional vector fields. IEEE Conf. Visualization, pp. 33–40. IEEE Computer Society Press,Los Alamitos, CA.Google Scholar
Helman, J.L., & Hesselink, L. (1990). Surface representations of two-and three-dimensional fluid flow topology. IEEE Conf. Visualization, pp. 6–13. IEEE Computer Society Press, Los Alamitos, CA.Google Scholar
Hess, J.L. (1990). Panel methods in computational fluid dynamics. Annu. Rev. Fluid Mechanics 22, 255–274.CrossRefGoogle Scholar
Hoffmann, C.M. (1989). Geometric and solid modeling. Morgan Kaufmann, San Mateo, CA.Google Scholar
Houghton, E.G., & Emnett, R.F. (1985). Implementation of a divide-and-conquer method for intersection of parametric surfaces. Comput. Aided Geometric Design 2, 173–183.CrossRefGoogle Scholar
Kao, T.J., & Su, T.Y. (1992). An interactive multi-block grid generation system. In Software Systemsfor Surface Modeling and Grid Generations (Smith, R.E., Ed.), pp. 333–345. No. 3143 in NASA Conference Publication, Washington, DC.Google Scholar
Katz, J., & Plotkin, A. (1991). Low-speed aerodynamics: From wing theory to panel methods. McGraw-Hill, New York.Google Scholar
Knupp, P.M., & Steinberg, S. (1993). The fundamentals of grid generation. CRC Press, Boca Raton, FL.Google Scholar
Ling, S.R., Steinberg, L., & Jaluria, Y. (1993). MSG: A computer system for automated modeling of heat transfer. AI EDAM 7 (4), 287–300.Google Scholar
Müllenheim, G. (1991). On determining start points for a surface/surface intersection algorithm. Comput. Aided Geometric Design 8, 401–408.CrossRefGoogle Scholar
Narayanan, N.H. (1993). Imagery: Computational and cognitive perspectives. Computat. Intell. 9 (4), 303–307.CrossRefGoogle Scholar
Natarajan, B.K. (1990). On computing the intersection of b-splines. Proc. 6th Ann. ACM Symp. Computat. Geometry, pp. 157–167.Google Scholar
Perry, A.E., & Chong, M.S. (1987). A description of eddying motions and flow patterns using critical-point concepts. Annu. Rev. Fluid Mechanics 19, 125–155.CrossRefGoogle Scholar
Remotique, M.G., Hart, E.T., & Stokes, M.L. (1992). EAGLEView: A surface and grid generation program and its data management. In Software Systems for Surface Modeling and Grid Generation (Smith, R.E., Ed.), pp. 243–251. No. 3143 in NASA Conference Publication, Washington, DC.Google Scholar
Requicha, A.G. (1980). Representations for rigid solids: Theory, methods, and systems. Comput. Surveys 12 (4), 437–464.CrossRefGoogle Scholar
Risenfeld, R.F. (1981). Using the oslo algorithm as a basis for CAD/CAM geometric modeling. Proc. NCGA National Conf., pp. 345–356.Google Scholar
Sacks, E.P. (1991). Automatic analysis of one-parameter planar ordinary differential equations by intelligent numeric simulation. Artif. Intell. 48, 27–56.CrossRefGoogle Scholar
Santhanam, T., Browne, J.C., Kallinderis, J., & Miranker, D. (1992). A knowledge based approach to mesh optimization in CFD domain: ID Euler code example. In Intelligent Scientific Computation (No FS–91–01) AAAI Technical Reports. AAAI Press, Menlo Park, CA.Google Scholar
Schuster, D.M. (1992). Batch mode grid generation: An endangered species? In Software Systemsfor Surface Modeling and Grid Generation (Smith, R.E., Ed.), pp. 487–500. No. 3143 in NASA Conference Publication, Washington, DC.Google Scholar
Sinha, P. (1992). Mixed dimensional objects in geometric modeling. New Technologies in CAD/CAM (Autofact '92) Society of Manufacturing Engineers. Dearborn, MI.Google Scholar
Thompson, J.F., Warsi, Z.U.A., & Mastin, C.W. (1985). Numerical grid generation: Foundations and applications. North-Holland, Amsterdam.Google Scholar
Tobak, M., & Peake, D. J. (1982). Topology of three-dimensional separated flows. Annu. Rev. Fluid Mechanics 14, 61–85.CrossRefGoogle Scholar
Vogel, A.A. (1990). Automated domain decomposition for computational fluid dynamics. Comput. Fluids 18 (4), 329–346.CrossRefGoogle Scholar
Yao, K.-T., & Gelsey, A. (1994). Intelligent automated grid generation for numerical simulations. Proc. Twelfth Nat. Conf. Artif. Intell., pp. 1224–1230.Google Scholar
Yao, K.-T. (1996). Intelligent Automated Grid Generation for Numer ical Simulations. Ph.D. thesis, Rutgers University, New Brunswick, NJ.Google Scholar
Yip, K.M.K. (1991). Understanding complex dynamics by visual and symbolic reasoning. Artif. Intell. 51, 179–221.CrossRefGoogle Scholar
Zhao, F. (1991). Extracting and representing qualitative behaviors of complex systems in phase spaces. Proc. 12th Int. Joint Conf. Artif. Intell.CrossRefGoogle Scholar