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

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probability distribution functions
shear flow
diffusivity
predictions
vortices
gradients
disturbances
thresholds
simulation

ASJC Scopus subject areas

  • Condensed Matter Physics

Cite this

Probability distribution function for self-organization of shear flows. / Kim, Eun Jin; Liu, Han Li; Anderson, Johan.

In: Physics of Plasmas, Vol. 16, No. 5, 052304, 10.06.2009.

Research output: Contribution to journalArticle

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