pattern formation in spatially extended systems lecture 4mcc/minicourse/ta_lecture4_slides.pdf ·...
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Tel Aviv, January, 2006:Pattern Formation in Spatially Extended Systems - Lecture 4 1
Pattern Formation in Spatially Extended Systems
Lecture 4
Chaos
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Tel Aviv, January, 2006:Pattern Formation in Spatially Extended Systems - Lecture 4 2
Outline
• Introduction to chaos
• Pattern chaos
• Spatiotemporal chaos
� Definition and characterization
� Transitions to spatiotemporal chaos and between different
chaotic states
� Coarse grained descriptions
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Tel Aviv, January, 2006:Pattern Formation in Spatially Extended Systems - Lecture 4 3
Equilibrium
Uniform state
Patterns
R
Rc
Driv
ing
Spatiotemporalchaos
Turbulence
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Tel Aviv, January, 2006:Pattern Formation in Spatially Extended Systems - Lecture 4 4
qqc
R
Rc
E
O
E=EckhausZ=ZigZagSV=Skew VaricoseO=OscillatorySV
EZZ
stableband
qS+qS- qN+qN-
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Tel Aviv, January, 2006:Pattern Formation in Spatially Extended Systems - Lecture 4 5
Some Theoretical Highlights
Landau (1944) Turbulence develops by infinite sequence of transitions
adding additional temporal modes and spatial complexity
Lorenz (1963) Discovered chaos in simple model of convection
Ruelle and Takens (1971)Suggested the onset of aperiodic dynamics
from a low dimensional torus (quasiperiodic motion with a small
numberN frequencies)
Feigenbaum (1978)Quantitative universality for period doubling route
to chaos
…
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Tel Aviv, January, 2006:Pattern Formation in Spatially Extended Systems - Lecture 4 6
Lorenz Model
X = −σ(X − Y )Y = rX − Y −XZZ = b (XY − Z)
“Classic” parameter values areb = 8/3, σ = 10
andr = 27.
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Tel Aviv, January, 2006:Pattern Formation in Spatially Extended Systems - Lecture 4 7
-10-5
05
10
X
-10
0
10
Y
0
10
20
30
40
Z
-50
510
X
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Tel Aviv, January, 2006:Pattern Formation in Spatially Extended Systems - Lecture 4 8
Lyapunov Exponents and Eigenvectors
Quantifying the sensitive dependence on initial conditions
t0
t1 t2
t3
tf
δu0
δufX
Y
Z
Sλ(t) = ln∣∣∣ δutδu0
∣∣∣ ; λ = lim tf→∞ 1tf−t0Sλ(tf ); δut → eigenvector
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Tel Aviv, January, 2006:Pattern Formation in Spatially Extended Systems - Lecture 4 9
Dimension of the Attractor
• The fractal dimension of the attractor quantifies the number of
chaotic degrees of freedom.
• There are many possible definitions. Most are inaccessible to
experiment and numerics for high dimensional attractors.
• I will discuss theLyapunov dimensionwhich is conjectured to be
the same as theinformation dimension
Line lengths→ eλ1t , Areas→ e(λ1+λ2)t , Volumes→ e(λ1+λ2+λ3)t , . . .
Lyapunov Dimension:
DL = ν + 1
|λν+1|ν∑i=1
λi
whereν is the largest index such that the sum is positive.
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Tel Aviv, January, 2006:Pattern Formation in Spatially Extended Systems - Lecture 4 10
Lyapunov dimension
Defineµ(n) =∑ni=1 λi (λ1 ≥ λ2 · · · ) with λi theith Lyapunov
exponent.
DL is the interpolated value ofn givingµ = 0 (the dimension of the
volume that neither grows nor shrinks under the evolution)
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Tel Aviv, January, 2006:Pattern Formation in Spatially Extended Systems - Lecture 4 11
Small system chaos: some experimental highlights
Ahlers (1974) Transition from time independent flow to aperiodic flow at
R/Rc ∼ 2 (aspect ratio 5)
Gollub and Swinney (1975)Onset of aperiodic flow from time-periodic
flow in Taylor-Couette
Maurer and Libchaber, Ahlers and Behringer (1978) Transition from
quasiperiodic flow to aperiodic flow in small aspect ratio convection
Lichaber, Laroche, and Fauve (1982)Quantitative demonstration of the
Fiegenbaum period doubling route to chaos
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Tel Aviv, January, 2006:Pattern Formation in Spatially Extended Systems - Lecture 4 12
Pattern chaos
• First experiments (1974):0 = 5.27, cryogenic (normal) liquidHe4 as fluid. High
precision heat flow measurements (no flow visualization).
• Onset of aperiodic time dependence in low Reynolds number flow: relevance of
chaos to “real” (continuum) systems.
• Broad power spectrum with power law decrease at largef : P(f ) ∼ f−4
• Aspect ratio dependence of the onset of time dependence (1978)
0 2 5 57
Rt 10Rc 2Rc 1.1Rc
• Flow visualization (Croquette et al. 1986):0 = 7.66, Argon
• Simulation of Generalized Swift-Hohenberg equation (Greenside, MCC, Coughran
1985)
• Simulation of full fluid equations (Paul, MCC, Fischer, and Greenside 2001)
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Tel Aviv, January, 2006:Pattern Formation in Spatially Extended Systems - Lecture 4 13
Aperiodic time series
[from Ahlers 1974]
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Tel Aviv, January, 2006:Pattern Formation in Spatially Extended Systems - Lecture 4 14
Broad power spectrum
[from Ahlers and Behringer 1978]
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Tel Aviv, January, 2006:Pattern Formation in Spatially Extended Systems - Lecture 4 15
Aspect ratio dependence
[from Gao and Behringer 1984]
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Tel Aviv, January, 2006:Pattern Formation in Spatially Extended Systems - Lecture 4 16
Generalized Swift-Hohenberg simulations
[from Greenside, MCC, Coughran 1985]
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Tel Aviv, January, 2006:Pattern Formation in Spatially Extended Systems - Lecture 4 17
Fluid simulations
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Tel Aviv, January, 2006:Pattern Formation in Spatially Extended Systems - Lecture 4 18
R = 3127 R = 6949
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Tel Aviv, January, 2006:Pattern Formation in Spatially Extended Systems - Lecture 4 19
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Tel Aviv, January, 2006:Pattern Formation in Spatially Extended Systems - Lecture 4 20
Lyapunov Exponent
10 20 30 40 500
10
20
30
t
log
|Nor
m|
dataλ = 0.6
R = 6949 [Scheel and MCC, 2006]
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Tel Aviv, January, 2006:Pattern Formation in Spatially Extended Systems - Lecture 4 21
Spatiotemporal chaos
Chaos in large aspect ratio (formally0→∞) spatially homogeneous
system:
• Break down of pattern to time dependent state
• Collective effect of many coupled chaotic degrees of freedom
Many natural examples of chaotic systems are closer to this idealization
than to low dimensional chaos.
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Tel Aviv, January, 2006:Pattern Formation in Spatially Extended Systems - Lecture 4 22
Systems
• Coupled Maps
x(n+1)i = f (x(n)i )+D × 1
n
∑δ=n.n.
[f (x(n)i+δ)− f (x(n)i )]
with e.g.f (x) = ax(1− x)• PDE simulations
� Kuramoto-Sivashinsky equation
∂tu = −∂2xu− ∂4
xu− u∂xu
� Amplitude equations, e.g. Complex Ginzburg-LandauEquation
∂tA = A+ (1+ ic1)∇2A− (1− ic3) |A|2A
• Physical systems ( experiment and numerics )
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Tel Aviv, January, 2006:Pattern Formation in Spatially Extended Systems - Lecture 4 23
Challenges
• System-specific questions
• Definition and characterization
• Transitions to spatiotemporal chaos and between different chaotic
states
• Coarse grained descriptions
• Control
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Tel Aviv, January, 2006:Pattern Formation in Spatially Extended Systems - Lecture 4 23
Challenges
• System-specific questions
• Definition and characterization
• Transitions to spatiotemporal chaos and between different chaotic
states
• Coarse grained descriptions
• Control
Ideas and methods from dynamical systems, statistical mechanics, phase
transition theory …
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Tel Aviv, January, 2006:Pattern Formation in Spatially Extended Systems - Lecture 4 24
Definition and characterization
• Narrow the phenomena
• Decide if theory, simulation, and experiment match
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Tel Aviv, January, 2006:Pattern Formation in Spatially Extended Systems - Lecture 4 25
Characterizing spatiotemporal chaos
Methods from statistical physics: Correlation lengths and times, etc.
• Easy to measure, but perhaps not very insightful
Methods from dynamical systems:Lyapunov exponents and attractor
dimensions.
• Inaccessible in experiment, but can be measured in simulations
• Ruelle suggested that Lyapunov exponents should beintensive,
and the dimension should beextensive∝ Ld
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Tel Aviv, January, 2006:Pattern Formation in Spatially Extended Systems - Lecture 4 26
Lyapunov spectrum and dimension for spiral defect chaos
(Egolf et al. 2000)
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Tel Aviv, January, 2006:Pattern Formation in Spatially Extended Systems - Lecture 4 27
Microextensivity for the 1d Kuramoto-Sivashinsky equation
78 83 88 93L
15
16
17
18
Lyap
unov
Dim
ensi
on
(from Tajima and Greenside 2000)
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Tel Aviv, January, 2006:Pattern Formation in Spatially Extended Systems - Lecture 4 28
Spatial aspects of sensitivity to initial conditions
Lyapunov vector for spiral defect chaos (Chiam, 2003, after Egolf et al.)
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Tel Aviv, January, 2006:Pattern Formation in Spatially Extended Systems - Lecture 4 29
Challenges
• System-specific questions
• Definition and characterization
• Transitions to spatiotemporal chaos and between different chaotic
states
• Coarse grained descriptions
• Control
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Tel Aviv, January, 2006:Pattern Formation in Spatially Extended Systems - Lecture 4 30
Transitions
In thermodynamic equilibrium systems the behavior may be simpler near
phase transitions.
Is there universal behavior near transitions in spatiotemporal chaos
(transition to STC, transitions within STC)?
If so, is the universality the same as in corresponding equilibrium systems?
Examples:
• Chaotic Ising map
• Rotating convection
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Tel Aviv, January, 2006:Pattern Formation in Spatially Extended Systems - Lecture 4 31
Chaotic Ising Map
J. Miller and D. Huse [Phys. Rev.E48, 2528 (1993)]
D. Egolf [Science287, 101 (2000)]
x(n+1)i = f (x(n)i )+ g∑δ(x
(n)i+δ − x(n)i )
x
f(x)
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Tel Aviv, January, 2006:Pattern Formation in Spatially Extended Systems - Lecture 4 32
Universality
Universality apparently the same as for thermodynamic Ising system:
[Miller and Huse, Phys. Rev.E48, 2528 (1993)]
Also RNG treatment suggests non-equilibrium correction terms are
irrelevant [Bennett and Grinstein, Phys. Rev. Lett.55, 657 (1985)]
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Tel Aviv, January, 2006:Pattern Formation in Spatially Extended Systems - Lecture 4 33
Spiral and domain chaos in Rayleigh-Bénard convection
Rotation Rate
Ray
leig
h N
umbe
r
no pattern(conduction)
stripes(convection)
KL
spiralchaos
domainchaos
ÿ�
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Tel Aviv, January, 2006:Pattern Formation in Spatially Extended Systems - Lecture 4 34
Amplitude equation description(Tu and MCC, 1992)]
Amplitudes of rolls at 3 orientationsAi(r , t), i = 1 . . .3
∂tA1 = εA1+ ∂2x1A1− A1(A
21+ g+A2
2+ g−A23)
∂tA2 = εA2+ ∂2x2A2− A2(A
22+ g+A2
3+ g−A21)
∂tA3 = εA3+ ∂2x3A3− A3(A
23+ g+A2
1+ g−A22)
whereε = (R − Rc(�)/Rc(�)Length scale ξ ∼ ε−1/2
Time scale τ ∼ ε−1
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Tel Aviv, January, 2006:Pattern Formation in Spatially Extended Systems - Lecture 4 35
Chaotic dynamics
Suggests characteristic lengths and times scaling asξ ∼ ε−1/2 and
τ ∼ ε−1
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Tel Aviv, January, 2006:Pattern Formation in Spatially Extended Systems - Lecture 4 36
Generalized Swift-Hohenberg simulations of domain chaos
∂ψ
∂t= εψ + (∇2+ 1)2ψ − g1ψ
3
+ g2z·∇ × [(∇ψ)2∇ψ ] + g3∇·[(∇ψ)2∇ψ ]
e.g.g1 = 1, g2 = 2.6, g3 = 1,5, ε = 0.1 (MCC, Meiron and Tu, 1994)
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Tel Aviv, January, 2006:Pattern Formation in Spatially Extended Systems - Lecture 4 37
GSH stripes
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Tel Aviv, January, 2006:Pattern Formation in Spatially Extended Systems - Lecture 4 38
GSH orientations
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Tel Aviv, January, 2006:Pattern Formation in Spatially Extended Systems - Lecture 4 39
cf. amplitude simulations
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Tel Aviv, January, 2006:Pattern Formation in Spatially Extended Systems - Lecture 4 40
GSH orientations
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Tel Aviv, January, 2006:Pattern Formation in Spatially Extended Systems - Lecture 4 41
GSH domain walls
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Tel Aviv, January, 2006:Pattern Formation in Spatially Extended Systems - Lecture 4 42
Visual demonstration of scaling
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Tel Aviv, January, 2006:Pattern Formation in Spatially Extended Systems - Lecture 4 43
Challenges
• System-specific questions
• Definition and characterization
• Transitions to spatiotemporal chaos and between different chaotic
states
• Coarse grained descriptions
• Control
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Tel Aviv, January, 2006:Pattern Formation in Spatially Extended Systems - Lecture 4 44
Coarse grained description
• Can we find simplified descriptions of spatiotemporal chaotic
systems atlarge length scales?
� conserved quantity (cf. hydrodynamics)
� near continuous transition
� collective motion such as defects
• Is the simplified description analogous to a thermodynamic
equilibrium system?
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Tel Aviv, January, 2006:Pattern Formation in Spatially Extended Systems - Lecture 4 45
Rough argument
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Tel Aviv, January, 2006:Pattern Formation in Spatially Extended Systems - Lecture 4 45
Rough argument
Expect a Langevin description at large scales
∂ty = D(y)+ η
y is vector of large length scale variables,D is some effective deterministic dynamics,
andη is noise coming from small scale chaotic dynamics.
![Page 48: Pattern Formation in Spatially Extended Systems Lecture 4mcc/MiniCourse/TA_lecture4_slides.pdf · Tel Aviv, January, 2006: Pattern Formation in Spatially Extended Systems - Lecture](https://reader033.vdocuments.net/reader033/viewer/2022050218/5f645b8e4e63077578454167/html5/thumbnails/48.jpg)
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Tel Aviv, January, 2006:Pattern Formation in Spatially Extended Systems - Lecture 4 45
Rough argument
Expect a Langevin description at large scales
∂ty = D(y)+ η
y is vector of large length scale variables,D is some effective deterministic dynamics,
andη is noise coming from small scale chaotic dynamics.
Sinceη represents the effect of many small scale fast chaotic degrees of freedom acting
on the large scales we might expect it to be Gaussian and white⟨ηi(r , t)ηj (r ′, t ′)
⟩ = �ij δ(r − r ′)δ(t − t ′)
![Page 49: Pattern Formation in Spatially Extended Systems Lecture 4mcc/MiniCourse/TA_lecture4_slides.pdf · Tel Aviv, January, 2006: Pattern Formation in Spatially Extended Systems - Lecture](https://reader033.vdocuments.net/reader033/viewer/2022050218/5f645b8e4e63077578454167/html5/thumbnails/49.jpg)
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Tel Aviv, January, 2006:Pattern Formation in Spatially Extended Systems - Lecture 4 45
Rough argument
Expect a Langevin description at large scales
∂ty = D(y)+ η
y is vector of large length scale variables,D is some effective deterministic dynamics,
andη is noise coming from small scale chaotic dynamics.
Sinceη represents the effect of many small scale fast chaotic degrees of freedom acting
on the large scales we might expect it to be Gaussian and white⟨ηi(r , t)ηj (r ′, t ′)
⟩ = �ij δ(r − r ′)δ(t − t ′)
In systems deriving from a microscopicHamiltoniandynamicsconstraintsrelate the
noise�ij and the deterministic termsD (the fluctuation-dissipation theorem).
![Page 50: Pattern Formation in Spatially Extended Systems Lecture 4mcc/MiniCourse/TA_lecture4_slides.pdf · Tel Aviv, January, 2006: Pattern Formation in Spatially Extended Systems - Lecture](https://reader033.vdocuments.net/reader033/viewer/2022050218/5f645b8e4e63077578454167/html5/thumbnails/50.jpg)
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Tel Aviv, January, 2006:Pattern Formation in Spatially Extended Systems - Lecture 4 45
Rough argument
Expect a Langevin description at large scales
∂ty = D(y)+ η
y is vector of large length scale variables,D is some effective deterministic dynamics,
andη is noise coming from small scale chaotic dynamics.
Sinceη represents the effect of many small scale fast chaotic degrees of freedom acting
on the large scales we might expect it to be Gaussian and white⟨ηi(r , t)ηj (r ′, t ′)
⟩ = �ij δ(r − r ′)δ(t − t ′)
In systems deriving from a microscopicHamiltoniandynamicsconstraintsrelate the
noise�ij and the deterministic termsD (the fluctuation-dissipation theorem).
In systems based on adissipativesmall scale dynamics,if the dominant macroscopic
dynamics is sufficiently simple, or sufficiently constrained by symmetries, these
relationships mayhappento occur.
![Page 51: Pattern Formation in Spatially Extended Systems Lecture 4mcc/MiniCourse/TA_lecture4_slides.pdf · Tel Aviv, January, 2006: Pattern Formation in Spatially Extended Systems - Lecture](https://reader033.vdocuments.net/reader033/viewer/2022050218/5f645b8e4e63077578454167/html5/thumbnails/51.jpg)
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Tel Aviv, January, 2006:Pattern Formation in Spatially Extended Systems - Lecture 4 46
Examples
• Chaotic Kuramoto-Sivashinsky dynamics reduces to noisy
Burgers equation [Zaleski (1989)]
• Chaotic Ising map model near the transition
� Langevin equation for dynamics of domain walls same as in
equilibrium system [Miller and Huse, (1993)]
� Coarse grained configurations satisfy detailed balance and
have a distribution given by an effective free energy [Egolf,
Science287, 101 (2000)]
• Defect dynamics description of 2D Complex Ginzburg Landau
chaos [Brito et al., Phys. Rev. Lett.90, 063801 (2003)]
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Tel Aviv, January, 2006:Pattern Formation in Spatially Extended Systems - Lecture 4 47
Conclusions
In this lecture I introduced some of the basic ideas of chaos, and discussed
the application of these ideas to pattern forming systems.
I discussed one of the first experiments on chaos in continuum systems
where the chaotic dynamics involves many of the ideas discussed in the
previous lectures.
I then introduced spatiotemporal chaos, which remains a poorly
characterized and understood phenomenon, and discussed
• Definition and characterization
• Transitions
• Coarse grained descriptions