Title
Fun sheet matching: towards automatic block decomposition for hexahedral meshes
Abstract
Depending upon the numerical approximation method that may be implemented, hexahedral meshes are frequently preferred to tetrahedral meshes. Because of the layered structure of hexahedral meshes, the automatic generation of hexahedral meshes for arbitrary geometries is still an open problem. This layered structure usually requires topological modifications to propagate globally, thus preventing the general development of meshing algorithms such as Delaunay’s algorithm for tetrahedral meshes or the advancing-front algorithm based on local decisions. To automatically produce an acceptable hexahedral mesh, we claim that both global geometric and global topological information must be taken into account in the mesh generation process. In this work, we propose a theoretical classification of the layers or sheets participating in the geometry capture procedure. These sheets are called fundamental, or fun-sheets for short, and make the connection between the global layered structure of hexahedral meshes and the geometric surfaces that are captured during the meshing process. Moreover, we propose a first generation algorithm based on fun-sheets to deal with 3D geometries having 3- and 4-valent vertices.
Year
DOI
Venue
2012
10.1007/s00366-010-0207-5
Eng. Comput. (Lond.)
Keywords
Field
DocType
meshing algorithm,automatic block decomposition,layered structure,generation algorithm,global layered structure,automatic generation,advancing-front algorithm,hexahedral mesh,mesh generation process,acceptable hexahedral mesh,fun sheet matching,global topological information,hexahedral meshblock-structured mesh � dual meshfundamental meshmesh generation
Hexahedron,Open problem,Polygon mesh,Vertex (geometry),Volume mesh,Theoretical computer science,Computational science,Geometry,Mathematics,Mesh generation,Static mesh,Delaunay triangulation
Journal
Volume
Issue
ISSN
28
3
1435-5663
Citations 
PageRank 
References 
8
0.65
18
Authors
4
Name
Order
Citations
PageRank
Nicolas Kowalski1232.29
Franck Ledoux2608.55
Matthew L. Staten321937.46
Steve J. Owen480.65