Streaming tetrahedral mesh optimization

Tian Xia, Eric Shaffer

Research output: Contribution to conferencePaperpeer-review

Abstract

Improving the quality of tetrahedral meshes is an important operation in many scientific computing applications. Meshes with badly shaped elements impact both the accuracy and convergence of scientific applications. State-of-the-art mesh improvement techniques rely on sophisticated numerical optimization methods such as feasible Newton or conjugate gradient. Unfortunately, these methods cannot be practically applied to very large meshes due to their global nature. Our contribution in this paper is to describe a streaming framework for tetrahedral mesh optimization. This framework enables the optimization of meshes an order of magnitude larger than previously feasible, effectively optimizing meshes too large to fit in memory. Our results show that streaming is typically faster than global optimization and results in comparable mesh quality. This leads us to conclude that streaming extends mesh optimization to a new class of mesh sizes without compromising the quality of the optimized mesh.

Original languageEnglish (US)
Pages281-286
Number of pages6
DOIs
StatePublished - Dec 15 2008
Event2008 ACM Symposium on Solid and Physical Modeling 2008, SPM'08 - Stony Brook, NY, United States
Duration: Jun 2 2008Jun 4 2008

Conference

Conference2008 ACM Symposium on Solid and Physical Modeling 2008, SPM'08
CountryUnited States
CityStony Brook, NY
Period6/2/086/4/08

Keywords

  • Computational geometry and object modeling
  • Large meshes
  • Mesh smoothing
  • Out-of-core algorithms
  • Streaming algorithms
  • Tetrahedral meshes

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Graphics and Computer-Aided Design
  • Software

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