A least-squares formulation of the Moving Discontinuous Galerkin Finite Element Method with Interface Condition Enforcement. (1st August 2021)

• Record Type: Journal Article
• Authors: Kercher, Andrew D.; Corrigan, Andrew
• Is Part Of: Computers & mathematics with applications. Volume 95(2021)
• Page Start: 143
• Page End: 171
• Publication Date: 2021-08-01
• Subjects: MDG-ICE -- FOSLS -- DPG -- Anisotropic curvilinear r-adaptivity; Electronic data processing -- Periodicals; Mathematics -- Data processing -- Periodicals; 510.28541
• DOI: 10.1016/j.camwa.2020.09.012
• Languages: English
• ISSNs: 0898-1221

Abstract

Abstract: A least-squares formulation of the Moving Discontinuous Galerkin Finite Element Method with Interface Condition Enforcement (LS-MDG-ICE) is presented. This method combines MDG-ICE, which uses a weak formulation that separately enforces a conservation law and the corresponding interface condition and treats the discrete geometry as a variable, with the Discontinuous Petrov–Galerkin (DPG) methodology of Demkowicz and Gopalakrishnan to systematically generate optimal test functions from the trial spaces of both the discrete flow field and discrete geometry. For inviscid flows, LS-MDG-ICE detects and fits a priori unknown interfaces, including shocks. For convection-dominated diffusion, LS-MDG-ICE resolves internal layers, e.g., viscous shocks, and boundary layers using anisotropic curvilinear r -adaptivity in which high-order shape representations are anisotropically adapted to accurately resolve the flow field. As such, LS-MDG-ICE solutions are oscillation-free, regardless of the grid resolution and polynomial degree. Finally, for both linear and nonlinear problems in one dimension, LS-MDG-ICE is shown to achieve optimal-order convergence of the L 2 solution error with respect to the exact solution when the discrete geometry is fixed and super-optimal convergence when the discrete geometry is treated as a variable. Highlights: LS-MDG-ICE, a least-squares formulation of MDG-ICE is presented.. Test functions are generated from the trial space of the discrete geometry. The method achieves optimal-order convergence for a static discrete geometry. The method achieves super-optimal convergence for a variable discrete geometry. Internal and boundary layers are resolved via anisotropic curvilinear r -adaptivity.