PNNL

Abstract

Mesh generation algorithms must consider the computational physics schemes to be adopted insomuch as tessellation should attempt to minimize discretization error metrics a priori, while placing elements judiciously yet economically. Basing local element size and shape on local geometric feature size is a promising approach, as the underlying physics may either be scale-invariant or may vary with scale in a predictable way. We present a boundary-fitted scale-invariant unstructured tetrahedral mesh generation algorithm that enables registration of element size to local geometric scale, given a triangulated mesh surface. The resulting tetrahedra are well-shaped and nearly orthogonal to the boundary. Unlike previous feature-based approaches, our algorithm does not require a background mesh, nor does it rely on the medial-axis. In contrast, as a first step, our algorithm produces a gradientlimited feature-size field over the input surface based on efficient ray casting. We illustrate how this field can be used to produce quality grids for computational fluid dynamics based simulations of challenging, topologically complex surfaces derived from magnetic resonance images. The algorithm is implemented in the Pacific Northwest National Laboratory (PNNL) version of the Los Alamos grid toolbox LaGriT[6].