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

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