An efficient invariant-region-preserving central scheme for hyperbolic conservation laws
Ruifang Yan,
Wei Tong and
Guoxian Chen
Applied Mathematics and Computation, 2023, vol. 436, issue C
Abstract:
Due to the Riemann solver free and avoiding characteristic decomposition, the central scheme is a simple and efficient tool for numerical solution of hyperbolic conservation laws (Nessyahu and Tadmor, J. Comput. Phys., 87(2):314-329,1990). But the theoretical Courant number CFL in order to preserve the invariant region of the numerical solution is very small, and there is lack of the stability proof for nonlinear systems. By adding a limiter on the reconstructed slope without requiring clipping condition, we enlarge the value of the CFL to admit larger time step. Then a widely applicable stability proof, which is suitable for general hyperbolic conservation laws, is given by writing the evolved solution as convex combinations in terms of the Lax-Friedrichs scheme. Some numerical experiments are carried out to verify the robustness.
Keywords: Hyperbolic conservation laws; Unstaggered-central scheme; Invariant-region-preserving principle; Minimum-maximum-preserving principle; Positivity-preserving principle; Forward-backward splitting (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0096300322005744
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:436:y:2023:i:c:s0096300322005744
DOI: 10.1016/j.amc.2022.127500
Access Statistics for this article
Applied Mathematics and Computation is currently edited by Theodore Simos
More articles in Applied Mathematics and Computation from Elsevier
Bibliographic data for series maintained by Catherine Liu ().