Fully anisotropic split-tree adaptive refinement mesh generation using tetrahedral mesh stitching

Due to the myriad of geometric topologies that modern computational fluid dynamicists desire to mesh and run solutions on, the need for a robust Cartesian Mesh Generation algorithm is paramount. Not only do Cartesian meshes require less elements and often help resolve flow features but they also allow the grid generator to have a great deal of control in so far as element aspect ratio, size, and gradation. Fully Anisotropic Split-Tree Adaptive Refinement (FASTAR) is a code that allows the user to exert a great deal of control and ultimately generate a valid, geometry conforming mesh. Due to the split-tree nature and the use of volumetric pixels (voxels), non-unit aspect ratio meshing is easily achieved. Nodes are not generated until the end which mitigates tolerance issues. The tree is retained coherently, and viscous layers may be inserted in the space between the geometry and the Cartesian mesh before it is tetrahedralized. FASTAR uses tree traversal to determine neighbors robustly, and with the tetrahedralization of only a small amount of space around the geometry, sliver cells and inverted elements are avoided. The code uses Riemannian Metric Tensors to generate geometry-appropriate spacing and is capable of adaptive meshing from a spacing field generated either by the user or from solution data. FASTAR is a robust, general mesh generator that allows maximum flexibility with minimal post-processing.

Grantor dc:publisher

University of Tennessee at Chattanooga

Author dc:creator

• Betro, Vincent Charles

Contributors dc:contributor

• Karman, Steve L., Jr.
• Anderson, W. Kyle; Wilson, Robert V.; Matthews, John V., III
• College of Engineering and Computer Science

• Numerical grid generation (Numerical analysis)
• Computer algorithms

Licence

• https://rightsstatements.org/page/InC/1.0/?language=en

Language dc:language

English, eng

Repository record dc:identifier

https://scholar.utc.edu/theses/197

OAI identifier oai:identifier

oai:scholar.utc.edu:theses-1337