TY - GEN
T1 - Time Filtered Finite Difference Schemes for Linear Hyperbolic Problems
AU - Boatman, K.
AU - Davis, L.
AU - Drapaca, C.
AU - Pahlevani, Faranak
AU - Rajan, T.
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025
Y1 - 2025
N2 - The focus of this paper is to investigate the effects of combining a time filter with three finite difference methods for numerically solving a linear hyperbolic equation. The examples in this paper show that, in some cases, the filter technique can be successfully combined with an explicit scheme. The three examples demonstrate different possible outcomes when one attempts to apply filtering. Von Neumann analysis proves to be a useful tool for examining the stability properties of filtered versions of the upwind and leapfrog methods as it allows one to deal with the filter parameter systematically. In each case, the analysis of the filtered scheme is done with insight into that of the original un-filtered method. With a careful choice of the filter parameter, the filtered upwind scheme is shown to be more accurate than its upwind counterpart, but the analysis also shows that a new CFL condition must be used in order to obtain accurate, stable results. This paper also demonstrates that the type of filter considered here cannot remedy certain kinds of stability properties. The filtered leapfrog scheme is shown to inherit the same type of spurious mode that its original un-filtered counterpart is known to possess. We also show that combining the filter with the Crank-Nicolson method leads to an implicit method where no value of the filter parameter provides for a consistent filtered version of the method. We conclude with numerical computations that support the theoretical results for the improved accuracy of the filtered upwind scheme.
AB - The focus of this paper is to investigate the effects of combining a time filter with three finite difference methods for numerically solving a linear hyperbolic equation. The examples in this paper show that, in some cases, the filter technique can be successfully combined with an explicit scheme. The three examples demonstrate different possible outcomes when one attempts to apply filtering. Von Neumann analysis proves to be a useful tool for examining the stability properties of filtered versions of the upwind and leapfrog methods as it allows one to deal with the filter parameter systematically. In each case, the analysis of the filtered scheme is done with insight into that of the original un-filtered method. With a careful choice of the filter parameter, the filtered upwind scheme is shown to be more accurate than its upwind counterpart, but the analysis also shows that a new CFL condition must be used in order to obtain accurate, stable results. This paper also demonstrates that the type of filter considered here cannot remedy certain kinds of stability properties. The filtered leapfrog scheme is shown to inherit the same type of spurious mode that its original un-filtered counterpart is known to possess. We also show that combining the filter with the Crank-Nicolson method leads to an implicit method where no value of the filter parameter provides for a consistent filtered version of the method. We conclude with numerical computations that support the theoretical results for the improved accuracy of the filtered upwind scheme.
UR - https://www.scopus.com/pages/publications/105014492246
UR - https://www.scopus.com/pages/publications/105014492246#tab=citedBy
U2 - 10.1007/978-3-031-84869-8_36
DO - 10.1007/978-3-031-84869-8_36
M3 - Conference contribution
AN - SCOPUS:105014492246
SN - 9783031848681
T3 - Springer Proceedings in Mathematics and Statistics
SP - 425
EP - 436
BT - Addressing Modern Challenges in the Mathematical, Statistical, and Computational Sciences - The 6th AMMCS International Conference
A2 - Kilgour, D. Marc
A2 - Makarov, Roman N.
A2 - Melnik, Roderick
A2 - Wang, Xu
A2 - Kunze, Herb
PB - Springer
T2 - 6th International Conference on Applied Mathematics, Modeling and Computational Science, AMMCS 2023
Y2 - 14 August 2023 through 18 August 2023
ER -