A stochastic particle-mesh scheme for uncertainty propagation in vortical flows

Olivier Le Maitre*, Omar Knio

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

A new mesh-particle scheme is constructed for uncertainty propagation in vortical flow. The scheme is based on the incorporation of polynomial chaos (PC) expansions into a Lagrangian particle approximation of the Navier-Stokes equations. The main idea of the method is to use a unique set of particles to transport the stochastic modes of the solution. The particles are transported by the mean velocity field, while their stochastic strengths are updated to account for diffusive and convective effects induced by the coupling between stochastic modes. An integral treatment is used for the evaluation of the coupled stochastic terms, following the framework of the particle strength exchange (PSE) methods, which yields a conservative algorithm. It is also shown that it is possible to apply solution algorithms used in deterministic setting, including particle-mesh techniques and particle remeshing. Thus, the method combines the advantages of particles discretizations with the efficiency of PC representations. Validation of the method on uncertain diffusion and convection problems is first performed. An example is then presented of natural convection of a hot patch of fluid in infinite domain, and the computations are used to illustrate the effectiveness of the approach for both large number of particles and high-order PC expansions.

Original languageEnglish (US)
Pages (from-to)645-671
Number of pages27
JournalJournal of Computational Physics
Volume226
Issue number1
DOIs
StatePublished - Sep 10 2007

Keywords

  • Fluid flow
  • Particle method
  • Spectral method
  • Stochastic polynomials
  • Uncertainty

ASJC Scopus subject areas

  • Numerical Analysis
  • Modeling and Simulation
  • Physics and Astronomy (miscellaneous)
  • Physics and Astronomy(all)
  • Computer Science Applications
  • Computational Mathematics
  • Applied Mathematics

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