A TASTE OF CHAOS By Adam T., Andy S., and Shannon R.

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A TASTE OF CHAOS By Adam T., Andy S., and Shannon R.

Transcript of A TASTE OF CHAOS By Adam T., Andy S., and Shannon R.

Page 1: A TASTE OF CHAOS By Adam T., Andy S., and Shannon R.

A TASTE OF CHAOS

By Adam T., Andy S., and Shannon R.

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“You've never heard of Chaos theory? Non-linear equations?”

-Dr. Ian Malcolm, fictional chaotician

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A TASTE OF CHAOS

• Aperiodic (not a repeated pattern of motion)

• Unpredictable due to sensitive dependence on initial conditions

• Not random… completely deterministic• Governed by non-linear equations of

motion (not just terms like x or x’, but also xn, (x’)n … although not all non-linear eq.’s are chaotic)

• Examples: weather (“butterfly effect”), circuits, fluid dynamics, etc.

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Experiment

Chaotic motion

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Materials Of Chaos!• Driven harmonic oscillator accessory• Mechanical Oscillator• Photo gate• Rotary motion sensor• Springs• Magnet• DC Power supply• Point mass (of unknown origins)

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The Apparatus

• Mechanical oscillator drive

• Springs• Magnet

• Point Mass

• Sinusoidal driving force on spring 1

• Linear Restoring force• Sinusoidal varying

damping force• Sinusoidal varying

force of gravity

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Initial conditions and settings

• Potential wells can create harmonic oscillations depending on initial conditions and settings

• Example changing driving amplitude created enough tension in spring one giving the point mass enough energy

for……Chaos!

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A Well with potential

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Potential Wells

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The Chaotic Oscillator:Equations of Motion

(Newton’s Law – angular version of F=ma)

...121 frictionmagneticgravityspringspringI

I

...

))(()( 21

frictionmagneticgravity

relaxedeqrelaxedeq xtDrxkyrykI

r

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The Chaotic Oscillator:Equations of Motion

So far, contributions from spring force terms appear linear, but…

...

)(21221

2

frictionmagneticgravity

relaxedeqrelaxedeq tDkyykxxkI

rkk

I

r

?21 CC

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Driving function

…Yeah.

The Chaotic Oscillator:Equations of Motion

...

)(21221

2

frictionmagneticgravity

relaxedeqrelaxedeq tDkyykxxkI

rkk

I

r

)()cos(2)( 22ddd rdrtdrdtD

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Time-dependent driving function D(t)

Gravity? Magnetic force?! ? … ?!!“dipole-induced dipole interaction”?

Friction, etc.?!?! ??!!?

The Chaotic Oscillator:Equations of Motion

)sin( eqI

rmgFr

),(

3

1~R

5

1~R

5

1~R

…velocity-dependent damping?!

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or

(experiment?)

The Chaotic Oscillator:Equations of Motion

5

1~R

nm

meqrelaxedeqrelaxedeq R

yk

I

rmgyykxxk

I

rkk

I

r

)(

)cos()sin(1221

2

)cos(222meqmm rrrrR

))cos(

(cos2

1

R

rry meqm

nR

fCCCC

)(

))(cos()sin( 40321

)(21 CC

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Torque vs. angle

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• Analytical techniques of little use in non-linear situations

• We rely on numerical methods of solving the eqn’s of motion

• Due to extreme sensitivity, small computational errors can have drastic effects…

• Thus, advances in technology have been historically necessary for sophisticated studies of chaos

Solving non-linear equations

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“Inevitably, underlying instabilities

begin to appear…”

“God help us, we’re in the

hands of engineers”

-Dr. Ian Malcolm, fictional chaotician

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What do you get when you cross

a shark with a telescope?

Question:

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Answer:

=X ||

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The Runge-Kutta Method

The Solution to All Our Problems

(Or at least the first-order differential equation ones)

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Numerical Solutions to ODEs

• Most differential equations have no analytical solution.

• We must approximate them numerically.– Euler

– Improved Euler– Runge-Kutta

• Trade-off:

Computational ease vs. Accuracy

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Classical Runge-Kutta

• Approximate solution of first-order ODEs.

• Know initial conditions.

• Choose step size.

• Recurrence relation:

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Classical Runge-Kutta

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2nd Order ODEs

• Classical Runge-Kutta is excellent… unless you’re us.

• Our equation of motion

is second order.

• Thus, we need a slightly more tricky method of approximation.

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Somethin’ Trickier

• We can write a 2nd-order ODE as two coupled 1st-order ODEs.

• Then we have Runge-Kutta recurrence relations

Ladies and Gentlemen, I give you…

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Somethin’ Trickier

• Notice that K1 and I1 are determined by initial conditions.

• Notice, also, that all other Ki and Ii are dependent on the preceding Kis and Iis.

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Our Equation of Motion

• We can apply this technique to our equation of motion.

• Set

• Thus,

• And we have two coupled 1st-order equations.

• Excellent…

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Our Equation of Motion

• But wait! That mysterious magnetic/gravitational/frictional acceleration term is not known….

•But we can find the angular acceleration due to these forces at a given time or a given position…

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Our Equation of Motion

• After we know these points, we can interpolate with a spline.

• But first, we must collect the data.

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Data for Spline

• Creating a representation of force for gravity, magnetism, and lets say umm friction.

• Removal of springs and driving force

• Rotating point mass and disk combination

• Plot acceleration vs. position (hopeful representation)

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The Spline Interpolation

This is a clever subtitle.

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Spline Interpolation

• The problem:– We have a set of discrete points.– We need a continuous function.

• The solution:– Spline interpolation!

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Types of Splines

• Linear spline– Simply connect the dots

• Quadratic spline– Takes into account four points

• Cubic spline– Si(xi)=Si+1(xi)

– Twice continuous differentiable

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Types of Splines

• Linear spline– Simply connect the dots

• Quadratic spline– Takes into account four points

• Cubic spline– Si(xi)=Si+1(xi)

– Twice continuous differentiable

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Quadratic Spline

• The interpolation gives a different function between every two points.

• The coefficients of the spline are given by the recurrence relation

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Our Spline (Take 1)

• Find {ti,θi} and {tj,ωj}.

• Use a spline interpolation to form functions t(θ) and α(t).

• Obtain α(θ) by way of α(t(θ)).

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Our Spline (Take 1)

• Spline of {θi,ti} to get t(θ).

– Uses the equation on the last slide.

• α(t) found by differentiating the spline of {tk,ωk}. (dω(t)/dt = α(t).)

• Same recurrence relation for zis as before.

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Our Spline (Take 1)

• This method of determining α(θ) was abandoned.

– We realized that DataStudio

will record {θi,αi}.

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Our Spline (Take 2)

• A quadratic spline was calculated a data set {θi,αi}.

• Here’s a sample portion of the spline.

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Return to Runge-Kutta

Endgame

We are now able to approximate the solution θ(t).

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The ResultsInitial conditions:

Start from right eq. position.

ωi = 0

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The Results

Initial conditions:

Start from left eq. position.

ωi = 0

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Motion of Chaos

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Motion of the GrimaceG

rimac

e

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“That is one big pile of $@!*”

-Dr. Ian Malcolm,Fictional chaotician

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“That is one big pile of $@!*”

-Dr. Ian Malcolm,Fictional chaotician

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Poincare Plot

• Periodic data points instead of a constant stream

• Less cluttered evaluation of data

• Puts harmonic motion in the spotlight

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Poincare plot

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Thanks everyone…

Keep it chaotic