A Hybridizable Discontinuous Galerkin Method for the Miscible Displacement Problem under Minimal Regularity

A numerical method based on the hybridizable discontinuous Galerkin method in space and backward Euler in time is formulated and analyzed for solving the miscible displacement problem. Under low regularity assumptions, convergence is established by proving that, up to a subsequence, the discrete pressure, velocity and concentration converge to a weak solution as the mesh size and time step tend to zero. The analysis is based on several key features: an H(div) reconstruction of the velocity, the skew-symmetrization of the concentration equation, the introduction of an auxiliary variable and the definition of a new numerical flux. Numerical examples demonstrate optimal rates of convergence for smooth solutions, and convergence for problems of low regularity.

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Publication Details

Journal
SIAM Journal on Numerical Analysis
Published
2026-06-19
DOI
https://doi.org/10.1137/25m1759239
Primary Topic
Advanced Numerical Methods in Computational Mathematics
Type
article
Field-Weighted Citation Impact
0.00

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article

A Hybridizable Discontinuous Galerkin Method for the Miscible Displacement Problem under Minimal Regularity

Béatrice Rivière, Keegan L. A. Kirk
SIAM Journal on Numerical Analysis
Advanced Numerical Methods in Computational Mathematics
article

A Hybridizable Discontinuous Galerkin Method for the Miscible Displacement Problem under Minimal Regularity

Béatrice Rivière, Keegan L. A. Kirk
article en

Abstract

A numerical method based on the hybridizable discontinuous Galerkin method in space and backward Euler in time is formulated and analyzed for solving the miscible displacement problem. Under low regularity assumptions, convergence is established by proving that, up to a subsequence, the discrete pressure, velocity and concentration converge to a weak solution as the mesh size and time step tend to zero. The analysis is based on several key features: an H(div) reconstruction of the velocity, the skew-symmetrization of the concentration equation, the introduction of an auxiliary variable and the definition of a new numerical flux. Numerical examples demonstrate optimal rates of convergence for smooth solutions, and convergence for problems of low regularity.

SIAM Journal on Numerical AnalysisVol. 64(3)
George Mason University (US), Kennedy Center (DK), Rice University (US)
National Science Foundation, Natural Sciences and Engineering Research Council of Canada, Office of Naval Research, Air Force Office of Scientific Research
Openalex Percentile: Top 98%
Advanced Numerical Methods in Computational Mathematics
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