Smooth Gradation of Anisotropic Meshes Using Log–Euclidean Metrics
Zhoufang Xiao, Carl F. Ollivier Gooch · AIAA Journal · 2021
The anisotropic mesh-size function represented by metric tensors includes two features: mesh sizes and mesh orientation. Such metrics are widely used in scientific computing for adaptation, but the solution metrics are not smooth, which adversely affects adaptation. A novel algorithm is proposed in this paper to smooth the metric as a whole and to improve anisotropic mesh-size gradation. First, the concept of Log–Euclidean metrics is used to convert the metric tensors from the Riemannian space into a Euclidean space. Then, the variations of metric tensors are limited by constraining the gradients of metric tensors in this space over the region of each background mesh element. Finally, a convex nonlinear optimization problem is formulated to smooth the metric tensors over the entire meshing domain. Theoretical analysis reveals the existence of a globally optimal smoothed sizing function. Numerical experiments on anisotropic meshes in both analytical and real laminar and turbulent flow cases are presented to demonstrate the effectiveness of the proposed method.