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arxiv: 2303.00374 · v1 · pith:FK3XIHP4 · submitted 2023-03-01 · math.NA · cs.NA

Monolithic Convex Limiting for Legendre-Gauss-Lobatto Discontinuous Galerkin Spectral Element Methods

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classification math.NA cs.NA
keywords limitingconvexdgsemdiscontinuousfluxgalerkinspectralelement
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We extend the monolithic convex limiting (MCL) methodology to nodal discontinuous Galerkin spectral element methods (DGSEM). The use of Legendre-Gauss-Lobatto (LGL) quadrature endows collocated DGSEM space discretizations of nonlinear hyperbolic problems with properties that greatly simplify the design of invariant domain preserving high-resolution schemes. Compared to many other continuous and discontinuous Galerkin method variants, a particular advantage of the LGL spectral operator is the availability of a natural decomposition into a compatible subcell flux discretization. Representing a high-order spatial semi-discretization in terms of intermediate states, we perform flux limiting in a manner that keeps these states and the results of Runge-Kutta stages in convex invariant domains. Additionally, local bounds may be imposed on scalar quantities of interest. In contrast to limiting approaches based on predictor-corrector algorithms, our MCL procedure for LGL-DGSEM yields nonlinear flux approximations that are independent of the time-step size and can be further modified to enforce entropy stability. To demonstrate the robustness of MCL/DGSEM schemes for the compressible Euler equations, we run simulations for challenging setups featuring strong shocks, steep density gradients and vortex dominated flows.

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Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Well-Balanced Subcell Limiting for Discontinuous Galerkin Discretizations of the Shallow-Water Equations

    math.NA 2026-05 unverdicted novelty 7.0

    A reformulation of the shallow water equations enables staggered DG fluxes whose non-conservative terms vanish at equilibrium, allowing node-wise subcell limiting that remains exactly well-balanced.