60 research outputs found
Conditional Lagrangian acceleration statistics in turbulent flows with Gaussian distributed velocities
The random intensity of noise approach to one-dimensional
Laval-Dubrulle-Nazarenko type model having deductive support from the
three-dimensional Navier-Stokes equation is used to describe Lagrangian
acceleration statistics of a fluid particle in developed turbulent flows.
Intensity of additive noise and cross correlation between multiplicative and
additive noises entering a nonlinear Langevin equation are assumed to depend on
random velocity fluctuations in an exponential way. We use exact analytic
result for the acceleration probability density function obtained as a
stationary solution of the associated Fokker-Planck equation. We give a
complete quantitative description of the available experimental data on
conditional and unconditional acceleration statistics within the framework of a
single model with a single set of fit parameters. The acceleration distribution
and variance conditioned on Lagrangian velocity fluctuations, and the marginal
distribution calculated by using independent Gaussian velocity statistics are
found to be in a good agreement with the recent high-Reynolds-number Lagrangian
experimental data. The fitted conditional mean acceleration is very small, that
is in agreement with DNS, and increases for higher velocities but it departs
from the experimental data, which exhibit anisotropy of the studied flow.Comment: RevTeX4, twocolumn, 9 pages, 7 figures; revised version, to appear in
Phys. Rev.
- …