hr-adaptivity for nonconforming high-order meshes with the target matrix optimization paradigm
Veselin Dobrev, Patrick Knupp, Tzanio Kolev, Ketan Mittal, Vladimir, Tomov

TL;DR
This paper introduces an $hr$-adaptivity framework combining mesh refinement and optimization via the Target-Matrix Optimization Paradigm to improve high-order mesh quality and accuracy efficiently.
Contribution
It extends existing $r$-adaptivity methods by integrating nonconforming mesh refinement, enabling better geometric target satisfaction and accuracy in high-order mesh optimization.
Findings
$hr$-adaptivity achieves similar accuracy with fewer degrees of freedom.
It effectively satisfies geometric targets where $r$-adaptivity fails.
The method supports both 2D and 3D meshes of various element types.
Abstract
We present an -adaptivity framework for optimization of high-order meshes. This work extends the -adaptivity method for mesh optimization by Dobrev et al., where we utilized the Target-Matrix Optimization Paradigm (TMOP) to minimize a functional that depends on each element's current and target geometric parameters: element aspect-ratio, size, skew, and orientation. Since fixed mesh topology limits the ability to achieve the target size and aspect-ratio at each position, in this paper we augment the -adaptivity framework with nonconforming adaptive mesh refinement to further reduce the error with respect to the target geometric parameters. The proposed formulation, referred to as -adaptivity, introduces TMOP-based quality estimators to satisfy the aspect-ratio-target via anisotropic refinements and size-target via isotropic refinements in each element of the mesh. The…
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