Probability distribution function for self-organization of shear flows

Eun Jin Kim, Han Li Liu, Johan Anderson

Research output: Contribution to journalArticle

24 Citations (Scopus)

Abstract

The first prediction of the probability distribution function (PDF) of self-organized shear flows is presented in a nonlinear diffusion model where shear flows are generated by a stochastic forcing while diffused by a nonlinear eddy diffusivity. A novel nonperturbative method based on a coherent structure is utilized for the prediction of the strongly intermittent exponential PDF tails of the gradient of shear flows. Numerical simulations using Gaussian forcing not only confirm these predictions but also reveal the significant contribution from the PDF tails with a large population of supercritical gradients. The validity of the nonlinear diffusion model is then examined using a threshold model where eddy diffusivity is given by discontinuous values, elucidating an important role of relative time scales of relaxation and disturbance in the determination of the PDFs.

Original languageEnglish
Article number052304
JournalPhysics of Plasmas
Volume16
Issue number5
DOIs
Publication statusPublished - 10 Jun 2009
Externally publishedYes

ASJC Scopus subject areas

  • Condensed Matter Physics

Fingerprint Dive into the research topics of 'Probability distribution function for self-organization of shear flows'. Together they form a unique fingerprint.

  • Cite this