experiments on turbulent dispersion p tabeling, m c jullien, p castiglione ens, 24 rue lhomond,...
TRANSCRIPT
Experiments on turbulent dispersion
P Tabeling, M C Jullien, P Castiglione
ENS, 24 rue Lhomond, 75231 Paris (France)
Outline
• 1 - Dispersion in a smooth field (Batchelor regime)
• 2 - Dispersion in a rough field (the inverse cascade)
Important theoretical results have been obtained in the fifties, sixties, (KOC theory, Batchelor regime,.. )
In the last ten years, theory has made important progress for the case of rough velocity fields essentially after the Kraichnan model (1968) was rigorously solved (in 1995 by two groups)
In the meantime, the case of smooth velocity fields, called the Batchelor regime, has been solved analytically.
Experiments on turbulent dispersion have been performed since 1950, leading to important observations such as scalar spectra, scalar fronts,...
- However, up to recent years no detailed :- Investigation of lagrangian properties, pair or multipoint statistics- Reliable measurement of high order statistics
In the last years, much progress has been done
IB
Magnet
Principle of the experiment
2 fluid layers, salt and Clear water
U
The experimental set-up
15 cm
5- 8 mm
Is the flow we produce this way two-dimensional ?
- Stratification accurately suppresses the vertical component (measured as less than 3 percents of the horizontal component)
- The velocity profile across the layer is parabolic at all times and quickly returns to this state if perturbed (the time constant has been measured to be on the order of 0.2s)
- Under these circumstances, the equations governing the flow are 2D Navier Stokes equations + a linear friction term
- Systematic comparison with 2D DNS brings evidence the system behaves as a two-dimensional system
Part 1 : DISPERSION IN A SMOOTH VELOCITY FIELD
FORCING USED FOR A SMOOTH VELOCITY FIELD
A typical (instantaneous) velocity field
Velocity profile for two components, along a line
U smooth- U can be expanded in Taylor series everywhere (almost)- The statistical statement is :
(called structure function of order 2)This situation gives rise to the Batchelor regime
U roughStructure functions behave as
<(U(x+r) −U(x))2 >~r2
<(U(x+r) −U(x))2 >~ra with a<2
A way to know whether a velocity field is smooth or rough, is to inspect the energy spectrum E(k)
E(k) ~k−β
If < 3 then the field is roughIf > 3 then the field is smooth
This is equivalent to examining the velocity structure function,
<u(x+r) −u(x) >~ra
For which the boundary between rough and smooth is a=1
CHARACTERISTICS OF THE VELOCITY FIELD(GIVING RISE TO BATCHELOR REGIME)
10-4
10-3
10-2
10-1
100
101
1 10k (cm -1 )
-3
kF k
l
k
Energy Spectrum 2D Energy spectrum
RELEASING THE TRACER
Drop of a mixture of fluoresceindelicately released on the free surface
Evolution of a drop after it has been released
CHARACTERIZING THE BATCHELOR REGIME
There exists a range of time in whichstatistical properties are stationary
Turbulence deals with dissipation : something is injected at large scales and ‘ burned ’ at small scales; in between there is a self similar range of scales called « cascade »
The rule holds for tracers : the dissipation is
χ=D< ∇C∫2dxdy>
In a steady state, is a constant
DISSIPATION AS A FUNCTION OF TIME
0
1
2
3
4
5
6
0 5 10 15 20 25 30 35 40t (s)
TWO WORDS ON SPECTRA...
The spectrum Ec(k) is related to the Fourier decomposition of the field
Its physical meaning can be viewed through the relation
<(C(x,y)−<C>)2 >= EC(k)dk0
∞
∫
They are a bit old-fashioned but still very useful
0.01
0.1
1
10
100
1000
1 10
Eθ
( )k
(k cm-1)
110100
1 10
*k Eθ( )k
SPECTRUM OF THE CONCENTRATION FIELD
2D Spectrum
Does the k -1 spectrum contain much information ?
C=1
r
C=0
< ΔCr( )p
>=<C(x+r)−C(x)( )p>~r0
E(k) ~k−1
GOING FURTHER…. HIGHER ORDER MOMENTS
In turbulence, the statistics is not determined by the second order moment only (even if, from the practical viewpoint, this may be often sufficient)
Higher moments are worth being considered, to test theories, and to better characterize the phenomenon.
A central quantity :Probability distribution function (PDF) of the increments
r
C1C2
ΔCr =C1 −C2
The pdf of Cr is called :P(Cr)
Taking the increment across a distance r amounts to apply a pass-band filter, centered on r.
r
Cr
10-5
0.0001
0.001
0.01
0.1
1
-10 -5 0 5 10
C
PDF for r = 0.9 cm
10-6
10-5
0.0001
0.001
0.01
0.1
1
-6 -4 -2 0 2 4 6
C
PDF for r = 11 cm
Two pdfs, at small and large scale
10-5
0.0001
0.001
0.01
0.1
1
-4 0 4 8 12Θ/σ
10-50.00010.0010.010.1
-15-10-5051015
PDF OF THE INCREMENTS OF CONCENTRATIONIN THE SELF SIMILAR RANGE
Structure functions
Sp =<(C(x+r)−C(x))p >=<(ΔCr)p >
= (ΔCr)pp(ΔCr)d(ΔCr )∫
The structure function of order p is the pth-moment of the pdf of the increment
10
100
1000
104
105
106
0.01 0.1 1 10
Sp(r)
r (cm)
p=4
p=2
p=6
p=8
0.1 1S2(r)
r (cm)
STRUCTURE FUNCTIONS OF THE CONCENTRATION INCREMENTS
TO UNDERSTAND = SHOW THE OBSERVATIONS CAN BE INFERRED
FROM THE DIFFUSION ADVECTION EQUATIONS
The answer is essentially YES, after the work byChertkov, Falkovitch, Kolokolov, Lebedev, Phys Rev E54,5609 (1995)
- k-1 Spectrum- Exponential tails for the pdfs- Logarithmic like behaviour for the structure functions
DO WE UNDERSTAND THESE OBSERVATIONS ?
CONCLUSION : THEORY AGREES WITH EXPERIMENT
A PIECE OF UNDERSTANDING, CONFIRMED BY THE EXPERIMENT, IS OBTAINED
HOWEVER, THE STORY IS NOT FINISHED
0
2
4
6
8
10
0.0 2.0 4.0 6.0 8.0 10
Isolated pair
x (cm)
0.1
1
10
0.0 2.0 4.0 6.0 8.0 10
t(s)
The life of a pair of particles released in the system
How two particles separate ? exponentially, according to the theory
6.5
7
7.5
8
8.5
9
6.5 7.0 7.5 8.0 8.5 9.0
Isolated pair
x (cm)
Blow-up of the previous figure : the first four seconds
0
5
10
15
20
0.0 2.0 4.0 6.0 8.0 10 12
time
0.01
0.1
1
10
100
0.0 2.0 4.0 6.0 8.0 10 12
time
Separation (squared) for 100000 pairs
LINEAR LINEAR LOG-LINEAR
C Jullien (2001)
Part 2 :DISPERSION IN THE INVERSE CASCADE
Reminding...
• We are dealing with a diffusion advection given by :
DCDt
=∂C∂t
+(u∇)C=DΔC
Two cases : u(x,t) smooth u(x,t) rough
ARRANGEMENT USED FOR THE INVERSE CASCADE
A typical instantaneous velocity field
l
2l
4l
Cartoon of the inverse cascade in 2D
vorticity
streamfunction
Energy spectrum for the inverse cascade
0.1
1
10
0.1 1
E ( k , t )
k / 2π (cm-1
)
( a )
Slope -5/3
injectiondissipation
2D spectrum
Evolution of a drop released in the inverse cascade
Evolution of a drop in the Batchelor regime
How do two particles separate ?
6
8
10
12
14
16
6 8 10 12 14 16x (cm)
Averaged squared separation with timein the inverse cascade
Slope 3
0.001
0.01
0.1
1
10
100
0. 1 10 100( )time s
Why the pairs do not simply diffuse ?
li
X(N) = Xii=1
N
∑
Central limit theorem : the squared separation grows as t2
0
0.2
0.4
0.6
0.8
1
-1 -0.8 -0.6 -0.4 -0.2 0τ/t
R(t,τ
)/R(t,0)
(b)
Pairs remember about 60% of their past common life
10-5
10-4
10-3
10-2
10-1
100
101
0 2 4 6 8 10r (cm)
(a)
Lagrangian distributions of the separations
t=1s
t=10 s
10-5
10-4
10-3
10-2
10-1
100
101
0 5 10 15 20s=r/σ
( )b
The same, but renormalized using the r.m.s
Pair separation of particles in turbulence
- An old problem…. starting with Richardson in 1926- Tackled by him, Batchelor, Obukhov, Kraichnan,…- Several predictions for pair distributions- Essentially no reliable data for a long time
- Accurate data obtained only a few years ago- Surprisingly close to Kraichnan model prediction
How do triangles evolve in the turbulent field ?
9
10
11
12
13
14
15
10 11 12 13 14 15 16
P Castiglione (2001)
How to characterize triangles ?
r1
r2 r3
e1 =r1 −r2
2;e2 =
2r3 −r1 −r26
Introduce
Define the area :R=e1
2 +e22
Introduce shape parameters :
w=2e1 ×e2
R2
χ=12Arctg
2e1e2
e12 −e2
2
⎡
⎣ ⎢
⎤
⎦ ⎥
0 /12 /6
w
0
1
Different configurations mapped by w,
Distribution of the shape parameters of 100000 triangles released in the flow
P Castiglione
Coming back to the dispersion of a blob
0.5
1
1.5
2
2.5
3
0 5 10 15 20 25 30 35
t (s)
A «QUASI-STEADY » STATE CAN BE DEFINED
Spectrum of the concentration field
0.01
0.1
1
10
100
1000
0.1 1 10/2k (cm-1)
Slope -5/3
2D SpectrumSpectrum
Is the k-5/3 spectrum a big surprise ?
It was given in the fifties by Kolmogorov Corrsin Obukhov, giving rise to the KOC theory
E(k) ~k−5/3
It has been observed by a number of investigators, during the last four decades, in 3D
PDF of the concentration increments in the inertial range
r=7cm
r=1cm
10
100
1000
104
105
106
0.1 1 10 100
r (cm)
n=2
n=4
n=6
n=8
n=10
Structure functions of the concentration field
Sn(r)
0
0.5
1
1.5
2
2.5
3
0 2 4 6 8 10 12 14
ξn
n
K 41
Exponents of the structure functions
The exponents tend to saturate
About the saturation
- Remarkable phenomenon, discovered a few years ago- One conjectures it is a universal phenomenon- It is linked to the presence of fronts- It is linked to the clusterization of particles
C=1 C=0
r
<(C(x+r)-C(x))p>=Nr0
DO WE UNDERSTAND THE OBSERVATIONS ?
Kraichnan model provides a framework for interpreting most of the observations, i.e :
- Existence of deviations from KOC theory- Saturation of the exponents- Presence of fronts- Clusterization of triads- Form of the distributions of pairs
CONCLUSION
Progress has been made, leading to new theories whose relevance to the « real world » has been shown
These theories explain a set of properties which constrain the concentration field
High order moments, multipoint statistics is no more a terra incognita, and we might encourage investigators to more systematically consider these quantities so as to better characterize their system.