Conditional statistics for a passive scalar with a mean gradient and intermittency

A. Bourlioux, A. J. Majda, O. Volkov

Research output: Contribution to journalArticle

Abstract

The conditional dissipation and diffusion for a passive scalar with an imposed mean gradient are studied here. The results are obtained for an elementary model consisting of a random shear flow with a simple time-periodic transverse sweep. As the Peclet number is increased, scalar intermittency is observed; the scalar probability density function departs strongly from a Gaussian law. As a result, the conditional dissipation undergoes a transition from a quadratic behavior for the near-Gaussian probability distribution case at low Peclet number to a more complex shape at large Peclet. The conditional diffusion also undergoes a transition, this time from a linear to a nonlinear dependence, for cases with sufficient intermittency as well as a significant contribution from multiple spatial modes. The present analysis sheds some light on similar behaviors observed recently in numerical simulations of more complex models. The statistics in the present study are obtained by exact processing of one-dimensional quadrature results so that all sampling errors are eliminated, including in the tails of the distribution. This allows for a quantification of typical sampling errors when the conditional statistics are processed from numerical databases. The robustness of models based on polynomial fits for the conditional statistics is also assessed.

Original languageEnglish (US)
Article number104102
JournalPhysics of Fluids
Volume18
Issue number10
DOIs
StatePublished - Oct 2006

Keywords

  • Flow instability
  • Flow simulation
  • Gaussian distribution
  • Random processes
  • Shear turbulence
  • Turbulent diffusion

ASJC Scopus subject areas

  • Computational Mechanics
  • Condensed Matter Physics
  • Mechanics of Materials
  • Mechanical Engineering
  • Fluid Flow and Transfer Processes

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