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

3. You've never heard of Chaos theory? Non-linear equations? -Dr. Ian Malcolm, fictional chaotician

4. 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 x n, (x) n … although not all non-linear eq.s are chaotic) Examples: weather (butterfly effect), circuits, fluid dynamics, etc.

5. Experiment Chaotic motion

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

7. 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

8. 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!

9. A Well with potential

10. Potential Wells

11. The Chaotic Oscillator: Equations of Motion (Newtons Law – angular version of F=ma)

12. The Chaotic Oscillator: Equations of Motion So far, contributions from spring force terms appear linear, but…

13. Driving function …Yeah. The Chaotic Oscillator: Equations of Motion

14. Time-dependent driving function D(t) Gravity? Magnetic force?! ? … ?!! dipole-induced dipole interaction? Friction, etc.?!?! ??!!? The Chaotic Oscillator: Equations of Motion …velocity-dependent damping?!

15. or (experiment?) The Chaotic Oscillator: Equations of Motion

16. Torque vs. angle

17. Analytical techniques of little use in non-linear situations We rely on numerical methods of solving the eqns 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

18. Inevitably, underlying instabilities begin to appear… God help us, were in the hands of engineers -Dr. Ian Malcolm, fictional chaotician

19. What do you get when you cross a shark with a telescope? Question:

20. Answer : = X ||

21. The Runge-Kutta Method The Solution to All Our Problems (Or at least the first-order differential equation ones)

22. 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

23. Classical Runge-Kutta Approximate solution of first-order ODEs. Know initial conditions. Choose step size. Recurrence relation:

24. Classical Runge-Kutta

25. 2 nd Order ODEs Classical Runge-Kutta is excellent… unless youre us. Our equation of motion is second order. Thus, we need a slightly more tricky method of approximation.

26. Somethin Trickier We can write a 2 nd -order ODE as two coupled 1 st -order ODEs. Then we have Runge-Kutta recurrence relations Ladies and Gentlemen, I give you…

27. Somethin Trickier Notice that K 1 and I 1 are determined by initial conditions. Notice, also, that all other K i and I i are dependent on the preceding K i s and I i s.

28. Our Equation of Motion We can apply this technique to our equation of motion. Set Thus, And we have two coupled 1 st -order equations. Excellent…

29. 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…

30. Our Equation of Motion After we know these points, we can interpolate with a spline. But first, we must collect the data.

31. 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)

33. The Spline Interpolation This is a clever subtitle.

34. Spline Interpolation The problem: –We have a set of discrete points. –We need a continuous function. The solution: –Spline interpolation!

35. Types of Splines Linear spline –Simply connect the dots Quadratic spline –Takes into account four points Cubic spline –S i (x i )=S i+1 (x i ) –Twice continuous differentiable

36. Types of Splines Linear spline –Simply connect the dots Quadratic spline –Takes into account four points Cubic spline –S i (x i )=S i+1 (x i ) –Twice continuous differentiable

37. Quadratic Spline The interpolation gives a different function between every two points. The coefficients of the spline are given by the recurrence relation

38. Our Spline (Take 1) Find {t i,θ i } and {t j,ω j }. Use a spline interpolation to form functions t(θ) and α(t). Obtain α(θ) by way of α(t(θ)).

39. Our Spline (Take 1) Spline of {θ i,t i } to get t(θ). –Uses the equation on the last slide. α(t) found by differentiating the spline of {t k,ω k }. (dω(t)/dt = α(t).) Same recurrence relation for z i s as before.

40. Our Spline (Take 1) This method of determining α(θ) was abandoned. –We realized that DataStudio will record {θ i,α i }.

41. Our Spline (Take 2) A quadratic spline was calculated a data set {θ i,α i }. Heres a sample portion of the spline.

42. Return to Runge-Kutta Endgame We are now able to approximate the solution θ(t).

43. The Results Initial conditions: Start from right eq. position. ω i = 0

44. The Results Initial conditions: Start from left eq. position. ω i = 0

45. Motion of Chaos

46. Motion of the Grimace Grimace time

47. That is one big pile of $@!* -Dr. Ian Malcolm, Fictional chaotician

48. That is one big pile of $@!* -Dr. Ian Malcolm, Fictional chaotician

49. Poincare Plot Periodic data points instead of a constant stream Less cluttered evaluation of data Puts harmonic motion in the spotlight

50. Poincare plot

51. Thanks everyone… Keep it chaotic