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Introduction Fractals Mathematical Modelling Lecture 14 – Fractals Phil Hasnip [email protected] Phil Hasnip Mathematical Modelling
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Page 1: Mathematical Modelling Lecture 14 Fractals · 2012-03-02 · Fractals Overview of Course Model construction ! dimensional analysis Experimental input ! fitting Finding a ‘best’

IntroductionFractals

Mathematical ModellingLecture 14 – Fractals

Phil [email protected]

Phil Hasnip Mathematical Modelling

Page 2: Mathematical Modelling Lecture 14 Fractals · 2012-03-02 · Fractals Overview of Course Model construction ! dimensional analysis Experimental input ! fitting Finding a ‘best’

IntroductionFractals

Overview of Course

Model construction −→ dimensional analysisExperimental input −→ fittingFinding a ‘best’ answer −→ optimisationTools for constructing and manipulating models −→networks, differential equations, integrationTools for constructing and simulating models −→randomnessReal world difficulties −→ chaos and fractals

The material in these lectures is not in A First Course inMathematical Modeling.

Phil Hasnip Mathematical Modelling

Page 3: Mathematical Modelling Lecture 14 Fractals · 2012-03-02 · Fractals Overview of Course Model construction ! dimensional analysis Experimental input ! fitting Finding a ‘best’

IntroductionFractals

Aim

To study shapes with fractional dimensions

Phil Hasnip Mathematical Modelling

Page 4: Mathematical Modelling Lecture 14 Fractals · 2012-03-02 · Fractals Overview of Course Model construction ! dimensional analysis Experimental input ! fitting Finding a ‘best’

IntroductionFractals

Natural shapes

In our earlier discussions of scaled models we emphasised theimportance of geometrical similarity.

This is easy for man-made structures like skyscrapers andsubmarines – what about natural shapes like trees, clouds andcoastlines?

In 1982 Benoit Mandelbrot addressed these questions in ‘Howlong is the coastline of Britain?’

Phil Hasnip Mathematical Modelling

Page 5: Mathematical Modelling Lecture 14 Fractals · 2012-03-02 · Fractals Overview of Course Model construction ! dimensional analysis Experimental input ! fitting Finding a ‘best’

IntroductionFractals

How long is a sine wave?

Before we look at our coastline, let’s tackle a simpler problem:the length of a sine wave. We’ll use the box counting method:

Draw a grid of N21 squares over the shape

Count squares needed to contain shape, S(N1)

Reduce the size of squares so now have N22 , and recount

This is like using a smaller and smaller ruler

Phil Hasnip Mathematical Modelling

Page 6: Mathematical Modelling Lecture 14 Fractals · 2012-03-02 · Fractals Overview of Course Model construction ! dimensional analysis Experimental input ! fitting Finding a ‘best’

IntroductionFractals

Box counting method

We expect that the total length is no. boxes × size of box, i.e.

L = S(N).1N

= constant

In other words we expect:

S(N) = constant× N

Of course we must remember:

Near start, ruler is big −→ measurements inaccurateNear end, ruler is small −→ line thickness causes problems

Phil Hasnip Mathematical Modelling

Page 7: Mathematical Modelling Lecture 14 Fractals · 2012-03-02 · Fractals Overview of Course Model construction ! dimensional analysis Experimental input ! fitting Finding a ‘best’

IntroductionFractals

How long is a sine wave?

As we shrink the size of the boxes, our estimate of the lengthconverges to the real length.

Phil Hasnip Mathematical Modelling

Page 8: Mathematical Modelling Lecture 14 Fractals · 2012-03-02 · Fractals Overview of Course Model construction ! dimensional analysis Experimental input ! fitting Finding a ‘best’

IntroductionFractals

How long is the coastline of Britain?

This time the length does not converge, it seems to change withthe no. boxes N in each dimension.

In fact:S(N) = constant× Nd

but d is not an integer.

The coastline has a fractional dimension −→ fractal!

Phil Hasnip Mathematical Modelling

Page 9: Mathematical Modelling Lecture 14 Fractals · 2012-03-02 · Fractals Overview of Course Model construction ! dimensional analysis Experimental input ! fitting Finding a ‘best’

IntroductionFractals

How long is the coastline of Britain?

Euclidean geometry always has integer dimensions – length isN, area N2, volume N3 and so on. Natural shapes do not.

Use box counting methodPlot on a log-log graphSlope −→ fractional dimension df

Phil Hasnip Mathematical Modelling

Page 10: Mathematical Modelling Lecture 14 Fractals · 2012-03-02 · Fractals Overview of Course Model construction ! dimensional analysis Experimental input ! fitting Finding a ‘best’

IntroductionFractals

Generalised box counting method

We can use box counting to measure area, volume etc. too.

If we reduce the size of our box to get b times no. boxes ineach dimension, then the measured quantity m will change as:

S(bN) = bdS(N)

where d is the fractal (box counting) dimension.

E.g. halving the length of each box −→ have 2 times boxes ineach dimension −→ measured area goes up by b2 boxes.

Phil Hasnip Mathematical Modelling

Page 11: Mathematical Modelling Lecture 14 Fractals · 2012-03-02 · Fractals Overview of Course Model construction ! dimensional analysis Experimental input ! fitting Finding a ‘best’

IntroductionFractals

Simple model – the Koch curve

Generating a Koch curve is simple. Starting with a straight line:

1 Split every straight line section into three2 Put an equilateral triangle on every middle section3 Remove the triangle’s base4 Repeat from step 1

Phil Hasnip Mathematical Modelling

Page 12: Mathematical Modelling Lecture 14 Fractals · 2012-03-02 · Fractals Overview of Course Model construction ! dimensional analysis Experimental input ! fitting Finding a ‘best’

IntroductionFractals

Simple model – the Koch curve

Phil Hasnip Mathematical Modelling

Page 13: Mathematical Modelling Lecture 14 Fractals · 2012-03-02 · Fractals Overview of Course Model construction ! dimensional analysis Experimental input ! fitting Finding a ‘best’

IntroductionFractals

Simple model – the Koch curve

Phil Hasnip Mathematical Modelling

Page 14: Mathematical Modelling Lecture 14 Fractals · 2012-03-02 · Fractals Overview of Course Model construction ! dimensional analysis Experimental input ! fitting Finding a ‘best’

IntroductionFractals

Simple model – the Koch curve

Phil Hasnip Mathematical Modelling

Page 15: Mathematical Modelling Lecture 14 Fractals · 2012-03-02 · Fractals Overview of Course Model construction ! dimensional analysis Experimental input ! fitting Finding a ‘best’

IntroductionFractals

Simple model – the Koch curve

Phil Hasnip Mathematical Modelling

Page 16: Mathematical Modelling Lecture 14 Fractals · 2012-03-02 · Fractals Overview of Course Model construction ! dimensional analysis Experimental input ! fitting Finding a ‘best’

IntroductionFractals

Simple model – the Koch curve

Phil Hasnip Mathematical Modelling

Page 17: Mathematical Modelling Lecture 14 Fractals · 2012-03-02 · Fractals Overview of Course Model construction ! dimensional analysis Experimental input ! fitting Finding a ‘best’

IntroductionFractals

Simple model – the Koch curve

Phil Hasnip Mathematical Modelling

Page 18: Mathematical Modelling Lecture 14 Fractals · 2012-03-02 · Fractals Overview of Course Model construction ! dimensional analysis Experimental input ! fitting Finding a ‘best’

IntroductionFractals

Simple model – the Koch curve

Phil Hasnip Mathematical Modelling

Page 19: Mathematical Modelling Lecture 14 Fractals · 2012-03-02 · Fractals Overview of Course Model construction ! dimensional analysis Experimental input ! fitting Finding a ‘best’

IntroductionFractals

Simple model – the Koch curve

What is its fractal dimension?

iteration L0 11 4× 1

3 = 43

2 16× 19 =

(43

)2

......

n 4n × 13n =

(43

)n

Phil Hasnip Mathematical Modelling

Page 20: Mathematical Modelling Lecture 14 Fractals · 2012-03-02 · Fractals Overview of Course Model construction ! dimensional analysis Experimental input ! fitting Finding a ‘best’

IntroductionFractals

Simple model – the Koch curve

Now if I make my ruler 13 of its original length, I get 3 times the

boxes in each dimension, but the number of boxes I count gets4 times bigger. Remember:

S(bN) = bdS(N)

so in this case we have:

b = 3S(3N) = 4S(N)

⇒ 3d = 4

⇒ d =ln 4ln 3

Phil Hasnip Mathematical Modelling

Page 21: Mathematical Modelling Lecture 14 Fractals · 2012-03-02 · Fractals Overview of Course Model construction ! dimensional analysis Experimental input ! fitting Finding a ‘best’

IntroductionFractals

Simple model – the Koch curve

Every time we replace a third of each line with two-thirdsi.e. each time we make length l → 4

3 lAs we keep going, l −→∞

Phil Hasnip Mathematical Modelling

Page 22: Mathematical Modelling Lecture 14 Fractals · 2012-03-02 · Fractals Overview of Course Model construction ! dimensional analysis Experimental input ! fitting Finding a ‘best’

IntroductionFractals

Simple model – the Koch curve

Koch curve has definite start and end pointsIs infinitely long!We expected length to converge – why doesn’t it?As ruler made smaller, see more and more detailThere is always more detail to see!

Phil Hasnip Mathematical Modelling

Page 23: Mathematical Modelling Lecture 14 Fractals · 2012-03-02 · Fractals Overview of Course Model construction ! dimensional analysis Experimental input ! fitting Finding a ‘best’

IntroductionFractals

The Koch snowflake

We can make different shapes using different starting points.

Starting from an equilateral triangle −→ the Koch snowflake.

Phil Hasnip Mathematical Modelling

Page 24: Mathematical Modelling Lecture 14 Fractals · 2012-03-02 · Fractals Overview of Course Model construction ! dimensional analysis Experimental input ! fitting Finding a ‘best’

IntroductionFractals

The Koch snowflake

Phil Hasnip Mathematical Modelling

Page 25: Mathematical Modelling Lecture 14 Fractals · 2012-03-02 · Fractals Overview of Course Model construction ! dimensional analysis Experimental input ! fitting Finding a ‘best’

IntroductionFractals

The Koch snowflake

The perimeter is now basically three Koch curves, so samefractal dimension as before.

−→ Infinite perimeter, but finite area!

Could also change the algorithm, e.g. replace section withsquares rather than triangles.

Phil Hasnip Mathematical Modelling

Page 26: Mathematical Modelling Lecture 14 Fractals · 2012-03-02 · Fractals Overview of Course Model construction ! dimensional analysis Experimental input ! fitting Finding a ‘best’

IntroductionFractals

Summary

Fractals have unusual scaling properties −→ fractionaldimensionsPossible to have infinite perimeter, finite areaCan use the box counting method to measure fractaldimension

Phil Hasnip Mathematical Modelling


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