4.7 Article

Matrix-free subcell residual distribution for Bernstein finite element discretizations of linear advection equations

出版社

ELSEVIER SCIENCE SA
DOI: 10.1016/j.cma.2019.112658

关键词

Advection equation; Discrete maximum principles; Bernstein finite elements; Matrix-free methods; Residual distribution; Flux-corrected transport

资金

  1. German Research Association (DFG), Germany [KU 1530/23-1]
  2. U.S. Department of Energy, USA [DE-AC52-07NA27344, LLNL-JRNL-768125]
  3. SNF, Switzerland [200020 175784]
  4. Swiss National Science Foundation (SNF) [200020_175784] Funding Source: Swiss National Science Foundation (SNF)

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In this work, we introduce a new residual distribution (RD) framework for the design of bound-preserving high-resolution finite element schemes. The continuous and discontinuous Galerkin discretizations of the linear advection equation are modified to construct local extremum diminishing (LED) approximations. To that end, we perform mass lumping and redistribute the element residuals in a manner which guarantees the LED property. The hierarchical correction procedure for high-order Bernstein finite element discretizations involves localization to subcells and definition of bound-preserving weights for subcell contributions. Using strong stability preserving (SSP) Runge-Kutta methods for time integration, we prove the validity of discrete maximum principles under CFL-like time step restrictions. The low-order version of our method has roughly the same accuracy as the one derived from a piecewise (multi)-linear approximation on a submesh with the same nodal points. In high-order extensions, we use an element-based flux-corrected transport (FCT) algorithm which can be interpreted as a nonlinear RD scheme. The proposed LED corrections are tailor-made for matrix-free implementations which avoid the rapidly growing cost of matrix assembly for high-order Bernstein elements. The results for 1D, 2D, and 3D test problems compare favorably to those obtained with the best matrix-based approaches. (C) 2019 Elsevier B.V. All rights reserved.

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