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Automated Edge Grid Generation Based on Arc-Length Optimization

McLaurin, David and Suzanne M. Shontz

22nd International Meshing Roundtable, Springer-Verlag, pp.385-403, October 13-16 2013

IMR
PROCEEDINGS

22nd International Meshing Roundtable
Orlando, FL
October 13-16,2013

High Performance Computing Collaboratory, Center for Advanced Vehicular Systems, Graduate Program in Computational Engineering, Mississippi State University, Mississippi State, MS, U.S.A.
Department of Mathematics and Statistics, Department of Computer Science and Engineering, Center for Computational Sciences, Graduate Program in Computational Engineering, Mississippi State University, Mississippi State, MS, U.S.A.
Email: d.mclaurin@msstate.edu, sshontz@math.msstate.edu

Summary
Computational design and analysis has become a fundamental part of industry and academia for use in research, development, and manufacturing. In general, the accuracy of a computational analysis depends heavily on the fidelity of the computational representation of a real-world object or phenomenon. Most mesh generation strategies focus on element quality–with the justification being that downstream applications require high quality geometries in order to achieve a desired level of accuracy. However, element quality should be secondary to accurately representing the underlying physical object or phenomenon. This work seeks to improve the process of creating a computational model of an object of interest by accelerating the process of mesh generation by reducing the need for (often) manual intervention. This acceleration will be accomplished by automatically generating optimal discretizations of curves by minimizing the arc-length deficit. We propose a method for generating optimal discretizations through local optimization of the arc length. Our results demonstrate the robustness and accuracy of our optimal discretization technique.We also discuss how to incorporate our edge grid generator into existing mesh generation software.

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