A Ghost-Point Smoothing Strategy for Geometric Multigrid on Curved Boundaries
SSRN, ISSN: 1556-5068
2022
- 346Usage
- 1Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
We present a Boundary Local Fourier Analysis (BLFA) to optimize the relaxation parameters of boundary conditions in a multigrid framework.The method is implemented to solve elliptic equations on curved domains embedded in a uniform Cartesian mesh,although it is designed to be extended for general PDEs in curved domains, wherever a multigrid technique can be implemented.The boundary is implicitly defined by a level-set function and a ghost-point technique is employed to treat the boundary conditions.Existing strategies in literature adopt a constant relaxation parameter on the whole boundary. In this paper, the relaxation parameters are optimized in terms of the distance between ghost points and boundary, with the goal of smoothing the residual along the tangential direction.Theoretical results are confirmed by several numerical tests in 1D, 2D and 3D, showing that the convergence factor associated with the smoothing on internal equations is not degraded by boundary effects.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85178528491&origin=inward; http://dx.doi.org/10.2139/ssrn.4196491; https://www.ssrn.com/abstract=4196491; https://dx.doi.org/10.2139/ssrn.4196491; https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4196491; https://ssrn.com/abstract=4196491
Elsevier BV
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