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High Order Semi-Lagrangian Particle Methods

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Spectral and High Order Methods for Partial Differential Equations ICOSAHOM 2016

Part of the book series: Lecture Notes in Computational Science and Engineering ((LNCSE,volume 119))

Abstract

Semi-lagrangian (or remeshed) particle methods are conservative particle methods where the particles are remeshed at each time-step. The numerical analysis of these methods show that their accuracy is governed by the regularity and moment properties of the remeshing kernel and that their stability is guaranteed by a lagrangian condition which does not rely on the grid size. Turbulent transport and more generally advection dominated flows are applications where these features make them appealing tools. The adaptivity of the method and its ability to capture fine scales at minimal cost can be further reinforced by remeshing particles on adapted grids, in particular through wavelet-based multi-resolution analysis.

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Author information

Authors and Affiliations

  1. University Grenoble Alpes and Institut Universitaire de France, Grenoble, France

    Georges-Henri Cottet

  2. ETH Zurich, Zurich, Switzerland

    Petros Koumoutsakos

Authors
  1. Georges-Henri Cottet
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  2. Petros Koumoutsakos
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Corresponding author

Correspondence to Georges-Henri Cottet .

Editor information

Editors and Affiliations

  1. School of Mechanical Engineering, University of Campinas, Campinas, Brazil

    Marco L. Bittencourt

  2. Department of Civil Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, Brazil

    Ney A. Dumont

  3. EPFL-SB-MATHICSE-MCSS, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    Jan S. Hesthaven

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Cottet, GH., Koumoutsakos, P. (2017). High Order Semi-Lagrangian Particle Methods. In: Bittencourt, M., Dumont, N., Hesthaven, J. (eds) Spectral and High Order Methods for Partial Differential Equations ICOSAHOM 2016. Lecture Notes in Computational Science and Engineering, vol 119. Springer, Cham. https://doi.org/10.1007/978-3-319-65870-4_6

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