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Transcript of The road to catastrophe: stability and collapse in 2D many particle systems Maria R. D’Orsogna,...
![Page 1: The road to catastrophe: stability and collapse in 2D many particle systems Maria R. D’Orsogna, Y.L. Chuang, A. Bertozzi, L. Chayes Department of Mathematics,](https://reader036.fdocuments.net/reader036/viewer/2022062321/56649e415503460f94b335c3/html5/thumbnails/1.jpg)
The road to catastrophe:
stability and collapsein 2D many particle systems
Maria R. D’Orsogna, Y.L. Chuang, A. Bertozzi, L. Chayes Department of Mathematics, UCLA
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Why?
flocks
army ants
bacteria
herds
barracuda
jack, tuna
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But also:
Unmanned Vehicle Operations
Exploration :
Space, Underwater
Dangerous missions:
Land-mine removal, Earthquake recovery
Military missions
Individuals: limited capabilities
Teams: new, better propertieswithout leaders
…to artificial systems?
From Nature …
?
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Interactions:
Mediated by background: Gradients of chemical or physical fields
food, light concentrations
temperature
electromagnetic fields
Nucleation agents: External agents as triggers
tuna fish under floating objects
Direct information exchange between particles:
fish, birds
Bacteria, plankton
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Interactions: design challenges
Pattern formation
Dispersive behavior vs. Convergence to a site
Complex behavior without crashing
One example of an emergent behavior that I was not anticipating: I was trying to get the robots to spread evenly throughout their environment, trying to have them move themselves so that there were robots everywhere in the whole room, leaving no empty spaces. And I made an error in the program; I flipped some signs in the equations. And when I ran the software, the robots formed into little clumps. Essentially they made polka dots on the floor, which was very entertaining after the fact.
James McLarkin, Nova-PBS December 2004
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A First Study:
Vicsek algorithm CVA (PRL,1995):
Constant speed
Velocity direction adjusts
according to neighbor directions
+ noise
Phase transition to finite velocity
|v| ~ (c- )0.45
t=0 (a)
High density – high noise (c)
Low density – high noise (b)
High density – low noise (d)
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Simple discrete model:
2( )
( )i j i j
a r
ii i i i i j
j
x x x x
l li j a r
vm v v U x x
t
U x x C e C e
Morse potential
Rayleigh friction 2
iv
Levine et al 2000
Schweitzer et al 2000
Mogilner et al 2003
Self propulsion:
Self-acceleration+
Friction
Optimal speed
2
iv
Attractive-Repulsive potential:
Ca, Cr, la, lr
Parameter choice
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A few examples:
0.5
1.0, 40.0
0.6, 0.1a r
a r
C C
l l
0.8, 0.5
0.5, 1.0
2.0, 0.1a r
a r
C C
l l
Example 1: Example 2:
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A few examples:
Why are they qualitatevely different?
What if we add more particles?
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Naive parameters:
0.8, 0.5
0.5, 1.0
2.0, 0.1a r
a r
C C
l l
N=100 N=200 N=300
The density is increasing!
Why is the system not extensive?
Example 2
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Another example:
3, 0.5
0.5, 1.0
2.0, 0.1a r
a r
C C
l l
Example 3
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Persisting double spiral Higher self propulsion
Double spirals
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What is the role of the potential?
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D. Ruelle,
Statistical Mechanics, Rigorous results
if not: CATASTROPHIC COLLAPSE!
From Statistical Mechanics:
Given a many-body microscopic system
Is a `real’ macroscopic description possible?i.e. thermodynamics
Interactions must obey ‘H-stability’ constraints
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H-stability:
N
ijji iBNxxU allfor |)(|
A system of N >> 1 interacting agents
is H-stable if a non-negative
constant B exists such that:
where the l.h.s. is the total potential felt by the i-th agent
Pairwise interactions:
H-stable constraints on the two-body potential
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( ) 0nU r d r
Catastrophic !
Pair-wise potential:
d
STABLE CATASTROPHE
2( ) 0U r d r 2( ) 0U r d r
On lattice
Pair-wise potential
Qualitatively similar
Soft-core, exponentially decaying, minimum exists
TWO particles will find a minimum, optimal distance
in BOTH cases
An H-Stable condition:
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( ) 0nU r d r
Catastrophic !
Pair-wise potential:
d
STABLE CATASTROPHE
2( ) 0U r d r 2( ) 0U r d r
Infinite number of particles on lattice
Pair-wise potential
An H-Stable condition:
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H-stability: Guiding interaction criteria
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Morse Potential and H-stability:2
i
i
v
v
Catastrophic:
particles collapse as
Stable:
volume occupied as
N D’Orsogna et al 2005
1l C
l C
/r al l l
/r aC C C
in rotation
N
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Morse Potential and H-stability:2
i
i
v
v
Catastrophic:
particles collapse as
Stable:
volume occupied as
N D’Orsogna et al 2005
1l C
l C
/r al l l
/r aC C C
in rotation
N
1
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Catastrophic features and patterns:
U(r)
r
Negative area
C
1l C
l C
l
No intrinsic separation
Self-propelling speed
N=60
N=100
N=40 N=150
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Catastrophic features and patterns:
Minimum at r=0 borderline
C
1l C
l C
l
U(r)
r
Negative area
Intrinsic length-scale=0
Self-propelling speed
N=60
N=60 N=100 N=200 N=300
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Ring Formation:
/ 22 sin(2 / ) / 2 sin(2 / ) /
1
sin2
a r
Nr N l r N la r
n a r
C C ne e
r l l N
Implicit formula:
Excellent agreement!Number of particles
Rin
g r
adiu
s
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Catastrophic Features:
C
1l C
l C
l
U(r)
r
Negative area
Finite intrinsic separation
Self-propelling speed
N=40 N=100N=150
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Potential features and patterns:
r
1r
1C C
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Potential Features:
Optimal spacing
`Crystalline’ at large N
Small values
r
1r
1C C
U(r)
rSTABLE
Pair-wise
Rigid disk Flock
Different
initial conditions
Example 1
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Potential Features:
Optimal spacing
Collapse at large N
Larger values
U(r)
r
CATASTROPHIC
r
1r
1C C
Pair-wise
Example 2
Core free vortices, Catastrophic
N
Flock
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Catastrophic Vortices:
Fly apart increases with N:
Centrifugal force mv2/r vs. interactions
mr) force vs. N-dense system
~ r max
Area decreases with N!
fixed, catastrophic vortex regime
Clockwise - Counterclockwise
coexistence
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Other potentials?
Lennard-Jones Hard disks
Always stable
2( )
( )i j i j
a r
ii i i i i j
j
x x x x
l li j a r
vm v v U x x
t
U x x C e C e
p
2( )
( )i j i j
a r
ii i i i i j
j
x x x x
l li j a r
vm v v U x x
t
U x x C e C e
Power law divergences
p >= 2
Separatrix l C(2p+1)/2 =1
Always stable
p < 2
Stable vs. catastrophic
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Ideas:
Add hard core
Increase N:
decrease optimal distance
until hard core barrier is hit
stability
2 particle optimal distance Increase N:
decrease optimal distance
until optimal distance =0
catastrophe
Convergence without crashing?
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How to go from discrete to continuum?
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Irving Kirkwood:
1 1
1 1
1
( ,..., , ,..., , )
d ... ... 1
k k k
N N
N N
Nk
R R pk k
f R R p p t
f R dR dp dp
pff U f
t m
f: Probability distribution function in phase space
Hamiltonian equations of motion, U potential
Liouville equation
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1 1
1 1
1
( ,..., , ,..., , )
d ... ... 1
k k k
N N
N N
Nk
R R pk k
f R R p p t
f R dR dp dp
pff U f
t m
f: Probability distribution function in phase space
Hamiltonian equations of motion, U potential
Liouville equation
1 1
1 1
( ,..., , ,..., )
, ... ...
N N
N N
a R R p p
a f a f dR dR dp dp
a: Dynamic variable
expectation value = Macroscopic value of a
Use Liouville equation to find dynamics of variable <a,f> Hydrodynamics equations, JCP 1950
Irving Kirkwood:
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Irving Kirkwood 2:
1
( , ) ( ),N
k kk
r t m R r f
MACROSCOPIC DENSITY
1
( , ) ( , ) ( ),N
k kk
r t u r t p R r f
MEAN FIELD VELOCITY
2
1
( , ) ( ),2
Nk
k kk k
pE r t R r f
m
KINETIC ENERGY DENSITY
Continuity equation, momentum transport, energy transport
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Non-Hamiltonian systems?
But: These Liouville equations are valid for conserved systems!
CAN PROVE existence of Liouville’s equation for NON Hamiltonian systems
CAN generalize Irving Kirkwood continuum limit!
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Our simple model becomes:
1
( ) ( )N
i ii
r m r r
average in phase space
Continuum:
Euler
Irving-Kirkwood
2( ) ( ) ( ) ...
( ) 0
r
vv v v v r U r r dr
t
vt
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Continuum swarmsset rotational velocities
0
( ) ( ) lnR U r R dR D r
Density implicitly defined
( sin ,cos )v
(r)
radius
Constant speed
H-unstable
DiscreteContinuum
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Continuum equations:
Linear stability analysis around
0
ˆv v
Uniform density, velocity
TRANSLATIONAL MOTION:
, , , r a a rC C l l Predict instabilities, most unstable wavelengths
2( ) ( ) ( )
( ) 0
r
vv v v v r U r r dr
t
vt
Unstable at short wavelengths
Unstable at long wavelengths
Stable
Un
stab
le
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H-unstable 1
1.0, 0.5
0.5, 7.9
2.0, 0.5a r
a r
C C
l l
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H-unstable 2
1.0, 0.5
0.5, 1.0
2.0, 0.5a r
a r
C C
l l
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Noise?
2( )
( )i j i j
a r
ii i i i i j
j
x x x x
l li j a r
vm v v U x x
t
U x x C e C e
t
tt’t-t’
white noise
tTransition from flock to vortex
For large noise values
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Noise?
tcrit
ical
COM is fixed
t
COM is moving
noise (regular time increases)
x o
f ce
nte
r of
mass
FLOCK, VCOM is constant
SWARM, VCOM is 0
noise
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Variable masses:
Variable massesVortex
Segregation
mi ri= Interactions
Same segregation behavior for variable i-
s
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Site avoidance:
Split Patterns
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Site convergence:
T=0 Flock
Medium attraction to target
Wait a little bit
T=Tfinal Swarm
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Site convergence:
T=0 Flock
Medium attraction to target
When center of mass is close to target
Turn on noise
Randomize
Turn off noise
T=Tfinal Swarm
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Biologically relevant?
Under starving conditions the bacteria will aggregate
2D double spirals collapse into 3D aggregates
Direct interactions
Myxococcus xanthus
Stigmatella aurantiaca
t
Maybe!
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Conclusions:
Potential determines stability of structures in large agent limit
H-stability
statistical mechanics – biology – device control
Can apply to other potentials in literature
For stability: Just Add Hard Core repulsion!
Can tune cross-over from stable-dispersive
to catastrophic-site convergent
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( ) 0nU r d r
Catastrophic !
d spacing
d
the
U(r)
r
Why this condition?
U at r ~ infinity 0
U on lattice at d
If then it is more favorable to be collapsed
rdrUdrrrUddNeighborsdU 2)( 2)(~0~)()(
0)( 2 rdrU
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1 1 1 1( ,..., , ,..., , ) ( ,..., , ,..., , )NN N s s sf R R p p t f R R p p t
f + Liouville
BBGKY hierarchy equations
An application, BBGKY
1( , , )f R p t
1 1( ,..., , ,..., , )Ns s sf R R p p t
Single particle distribution function
Probability of finding s<N particles with specified positions, momenta
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Framework:
discrete:
individual based,
Lagrange
i=1, …, N particles
ODE-s
(Vicsek 1995)
continuum:
Fluid-like fields, Euler
PDEs
(Toner 1998)
( , )x t
( , )v x t
( ),ix t
( )iv t
Follow i-th motion
Fields at x,t
x