SMS:Adaptive Tesselation: Difference between revisions
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Adaptive tessellation is a mesh generation technique used to fill the interior of a polygon. The method is based on overlaying a quad tree on the polygon, and recursively splitting the quads until the size approaches the desired spacing. SMS derives the desired spacing based on either the spacing of the original polygon, or based on a user specified spatially varying scalar dataset (for a scattered dataset). A polygon is automatically processed with adaptive tessellation and is filled with the ''Map to 2D Mesh'' command. | Adaptive tessellation is a mesh generation technique used to fill the interior of a polygon. The method is based on overlaying a quad tree on the polygon, and recursively splitting the quads until the size approaches the desired spacing. SMS derives the desired spacing based on either the spacing of the original polygon, or based on a user specified spatially varying scalar dataset (for a scattered dataset). A polygon is automatically processed with adaptive tessellation and is filled with the '''Map to 2D Mesh''' command. | ||
The adaptive tessellation technique is robust and relatively quick, however, it often results in discrete increments in resolution as the overlying quad tree grid transitions from one resolution level to another. For this reason, the Advancing Front Triangulation method is preferred. | The adaptive tessellation technique is robust and relatively quick, however, it often results in discrete increments in resolution as the overlying quad tree grid transitions from one resolution level to another. For this reason, the Advancing Front Triangulation method is preferred. | ||
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==Scalar Adaptive Tessellation== | ==Scalar Adaptive Tessellation== | ||
SMS supports the option to control the local target size of elements using a | SMS supports the option to control the local target size of elements using a size dataset. This is the scalar adaptive tessellation method and requires selecting a spatial dataset that is everywhere positive to define the local spacing of the desired mesh. This may come from a variety of sources. | ||
See the tutorials on mesh generation for [[SMS:CGWAVE|CGWAVE]] and [[SMS:ADCIRC|ADCIRC]] for more information. | |||
==Related Topics== | ==Related Topics== | ||
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[[Category:SMS 2D Mesh|A]] | [[Category:SMS 2D Mesh|A]] | ||
Latest revision as of 16:04, 26 August 2016
Adaptive tessellation is a mesh generation technique used to fill the interior of a polygon. The method is based on overlaying a quad tree on the polygon, and recursively splitting the quads until the size approaches the desired spacing. SMS derives the desired spacing based on either the spacing of the original polygon, or based on a user specified spatially varying scalar dataset (for a scattered dataset). A polygon is automatically processed with adaptive tessellation and is filled with the Map to 2D Mesh command.
The adaptive tessellation technique is robust and relatively quick, however, it often results in discrete increments in resolution as the overlying quad tree grid transitions from one resolution level to another. For this reason, the Advancing Front Triangulation method is preferred.
Boundary Spaced Adaptive Tessellation
Adaptive tessellation uses the existing spacing on the polygons to determine the element sizes on the interior. Any interior arcs and refine points are forced into the new mesh. If the input polygon has varying node densities along its perimeter, SMS attempts to create a smooth element size transition between these areas of differing densities. Altering the size bias indicates whether SMS should favor the creation of large or small elements. Decreasing the bias will result in smaller elements; increasing the bias will result in larger elements. In either case, the elements in the interior of the mesh will honor the arc edges and the element sizes specified at nodes. The bias simply controls the element sizes in the transition region.
Scalar Adaptive Tessellation
SMS supports the option to control the local target size of elements using a size dataset. This is the scalar adaptive tessellation method and requires selecting a spatial dataset that is everywhere positive to define the local spacing of the desired mesh. This may come from a variety of sources.
See the tutorials on mesh generation for CGWAVE and ADCIRC for more information.
Related Topics
SMS – Surface-water Modeling System | ||
|---|---|---|
| Modules: | 1D Grid • Cartesian Grid • Curvilinear Grid • GIS • Map • Mesh • Particle • Quadtree • Raster • Scatter • UGrid |  |
| General Models: | 3D Structure • FVCOM • Generic • PTM | |
| Coastal Models: | ADCIRC • BOUSS-2D • CGWAVE • CMS-Flow • CMS-Wave • GenCade • STWAVE • WAM | |
| Riverine/Estuarine Models: | AdH • HEC-RAS • HYDRO AS-2D • RMA2 • RMA4 • SRH-2D • TUFLOW • TUFLOW FV | |
| Aquaveo • SMS Tutorials • SMS Workflows | ||
