Spectral Volume from a DG perspective: Oscillation Elimination, Stability, and Optimal Error Estimates
<|reference_start|>Spectral Volume from a DG perspective: Oscillation Elimination, Stability, and Optimal Error Estimates: The discontinuous Galerkin (DG) method and the spectral volume (SV) method are two widely-used numerical methodologies for solving hyperbolic conservation laws. In this paper, we demonstrate that under specific subdivision assumptions, the SV method can be represented in a DG form with a different inner product. Building on this insight, we extend the oscillation-eliminating (OE) technique, recently proposed in [M. Peng, Z. Sun, and K. Wu, {\it Mathematics of Computation}, https://doi.org/10.1090/mcom/3998], to develop a new fully-discrete OESV method. The OE technique is non-intrusive, efficient, and straightforward to implement, acting as a simple post-processing filter to effectively suppress spurious oscillations. From a DG perspective, we present a comprehensive framework to theoretically analyze the stability and accuracy of both general Runge-Kutta SV (RKSV) schemes and the novel OESV method. For the linear advection equation, we conduct an energy analysis of the fully-discrete RKSV method, identifying an upwind condition crucial for stability. Furthermore, we establish optimal error estimates for the OESV schemes, overcoming nonlinear challenges through error decomposition and treating the OE procedure as additional source terms in the RKSV schemes. Extensive numerical experiments validate our theoretical findings and demonstrate the effectiveness and robustness of the proposed OESV method. This work enhances the theoretical understanding and practical application of SV schemes for hyperbolic conservation laws, making the OESV method a promising approach for high-resolution simulations.<|reference_end|>
@article{li2024spectral,
    title={Spectral Volume from a DG perspective: Oscillation Elimination,
  Stability, and Optimal Error Estimates},
    author={Zhuoyun Li, Kailiang Wu},
    journal={arXiv preprint arXiv:2409.10871},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.10871},
    primaryClass={math.NA cs.NA}
}