Cell-vertex WENO schemes with shock-capturing quadrature for high-order finite element discretizations of hyperbolic problems
Joshua Vedral, Dmitri Kuzmin

TL;DR
This paper introduces a novel cell-vertex WENO scheme with shock-capturing quadrature for high-order finite element methods, improving accuracy and robustness in solving hyperbolic conservation laws with shocks.
Contribution
The paper presents a new cell-vertex WENO averaging method and a quadrature-driven artificial viscosity distribution, enhancing shock resolution and reducing mesh imprinting in high-order discretizations.
Findings
Significant accuracy improvements in 1D and 2D tests.
Enhanced shock-capturing capabilities without subcell decomposition.
Reduced mesh imprinting and improved robustness.
Abstract
We propose a new kind of localized shock capturing for continuous (CG) and discontinuous Galerkin (DG) discretizations of hyperbolic conservation laws. The underlying framework of dissipation-based weighted essentially nonoscillatory (WENO) stabilization for high-order CG and DG approximations was introduced in our previous work. In this general framework, Hermite WENO (HWENO) reconstructions are used to calculate local smoothness sensors that determine the appropriate amount of artificial viscosity for each cell. In the original version, candidate polynomials for WENO averaging are constructed using the derivative data from von Neumann neighbors. We upgrade this standard `cell-cell' reconstruction procedure by using WENO polynomials associated with mesh vertices as candidate polynomials for cell-based WENO averaging. The Hermite data of individual cells is sent to vertices of those…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Numerical Methods in Computational Mathematics · Computational Fluid Dynamics and Aerodynamics · Model Reduction and Neural Networks
