Abstract:
In numerical simulation where the underlying function is strongly
directional, it is desirable to use a mesh that is adaptive both in size
and in shape. In such simulation, a metric tensor is used to quantify
the ideal size and direction locally at each point in the domain,
which in turn defines the local stretching and size of the triangles
or quadrilaterals of the mesh. Given a metric tensor, the
anisotropic meshing problem is to construct a good quality mesh
satisfying the metric tensor. We present a new anisotropic
meshing method which is called the ellipse biting method. Our
algorithm uses the framework of advancing front to generate a
close to optimal packing of ellipses. We then use the p-Delaunay
triangulation of the vertex set to generate the final mesh. Because
it generates an ellipse packing that respects the underlying control
spacing, this new method produce a high quality mesh whose
element size and directionality conform well locally to the given
input. As part of this work, we introduces a set of operations
includrng scaling, intersection, and union on ten.sor metrics. Then
operations are used to formally define distance among metrics and
to extend Lipschitz condition and the notion of well-shaped
meshes from isotropic metrics to anisotropic metrics.