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310. Turbulence. Statistical approach 1

APPLIED — By Dmitry Podolsky on March 17, 2009 at 11:00 pm
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Dmitry Podolsky has got his PhD from Landau Institute for Theoretical Physics. He currently works as postdoc at Case Western Reserve University. He is also one of the editors of NEQNET.

Let me get back again to one of my most favourite topics in physics, that is, to developed turbulence. Last time (oh my, mid February) I have tried to explain what I consider the most important (and probably hard-to-solve) open problems in physics of turbulence. Now let me list quickly several (not too promising 310. Turbulence. Statistical approach 1 ) approaches to those problems we were able to develop during last hundred years or so.

It would be most natural to start by discussing statistical approach to developed turbulence.

310. Turbulence. Statistical approach 1

On the Fig: 2D turbulence highlighted by laser beam. Image by I. Afanassiev (U. of Newfoundland)

1. Reynolds averaging. Correlation functions of Eulerian velocities

The first person who started thinking about turbulence as stochastic phenomenon was probably Reynolds (back in 1880s he studied transition from laminar, regular, motion of water in a pipe to turbulent motion). Since the turbulent flow is essentially stochastic, he said, one needs to deal with averaged quantities.

What do we mean by averaging? It actually depends on a particular problem we are dealing with or a question we want to answer. For example, one can average over fast pulsations of the flow or over small linear scales.

Anyway, Reynolds has managed to write down a set of equations

310. Turbulence. Statistical approach 1,

where 310. Turbulence. Statistical approach 1 is averaged (in the sense above) velocity of the flow, 310. Turbulence. Statistical approach 1 (310. Turbulence. Statistical approach 1 is viscosity) and 310. Turbulence. Statistical approach 1 is so called Reynolds tensor (here 310. Turbulence. Statistical approach 1 is velocity of the flow before averaging). The latter satisfies the equations

310. Turbulence. Statistical approach 1.

Reynolds averaging is not self-consistent, since the number of variables is larger than the number of equations. For example, if you want to find the equation for the Reynolds tensor 310. Turbulence. Statistical approach 1, you may try to multiply eq. for 310. Turbulence. Statistical approach 1 by 310. Turbulence. Statistical approach 1 and average subsequently, but this will just introduce higher order tensor 310. Turbulence. Statistical approach 1, so you’ll need to find a equation for that one etc. etc. In overall, the procedure reminds the one we perform deriving BBGKY hierarchy of equations in kinetics, with only difference that it is absolutely unclear where to truncate hierarchy in the case of developed turbulence. Still, Reynolds equations turn out to be useful when we want to study, say, averaged characteristics of the flow in pipes.


2. Transport properties of turbulent flow. Correlation functions of Lagrangian velocities

It seems that one of the most important properties of turbulence is the ability of turbulent flow to transfer matter density, energy and momentum fast compared to a laminar flow.

Initially, to describe transport properties of the turbulent flow, people used simplified models based on analogies with slow laminar flows. For example, Boussinesq has related Reynolds tensor 310. Turbulence. Statistical approach 1 with averaged velocity of the flow 310. Turbulence. Statistical approach 1 in the case when fluctuations of velocity are present only along a direction 310. Turbulence. Statistical approach 1 perpendicular to the vector 310. Turbulence. Statistical approach 1 itself. Roughly, in this case

310. Turbulence. Statistical approach 1.

The quantity 310. Turbulence. Statistical approach 1 is called turbulent viscosity (here 310. Turbulence. Statistical approach 1 is characteristic linear scale of the flow). Usually, 310. Turbulence. Statistical approach 1 by orders of magnitude in turbulent flows.

Talking about transport properties of turbulent flows, I cannot help mentioning work by Taylor, who has studied transport of Lagrangian markers in turbulent flow and found that

310. Turbulence. Statistical approach 1,

where 310. Turbulence. Statistical approach 1 is a Lagrangian marker (this fancy term just means that you pick a liquid particle, which is located at the position 310. Turbulence. Statistical approach 1 at 310. Turbulence. Statistical approach 1 and follow its motion with time) and the diffusion coefficient 310. Turbulence. Statistical approach 1 is given by

310. Turbulence. Statistical approach 1,

i.e., by the correlation function of Lagrange velocity. This expression basically shows how, similar to description in terms of correlation functions of Eulerian velocities introduced by Reynolds, one can describe physics of turbulence in terms of correlation functions of Lagrangian velocities. Of course, this description is also incomplete and we again get a hierarchy of equations for correlation functions, which is unclear how to close in the case of developed turbulence.

That is it for now, folks… Next time I am going to return to the discussion of Kolmogorov spectra (probably, the most beautiful result found by applying statistical approach) and cover briefly the issue of intermittency.

Further reading

1. L. Landau, E. Lifshitz, Fluid mechanics
2. Monin, Yaglom, Statistical fluid mechanics (both Vol. 1 and 2)
3. Frost (Ed.). Handbook of turbulence. Fundamentals and applications

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