1 / 16

Correlation Dimension d c

Understand the correlation dimension (dc) in fractals, comparing it with the box-counting dimension (dF), common in multifractals like the Henon map. Learn how dc and dF diverge in complex structures like strange attractors.

Download Presentation

Correlation Dimension d c

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Correlation Dimensiondc • Another measure of dimension • Consider one point on a fractal and calculate the number of other points N(s) which have distances less than s. • Average over all starting points • C(s) • Plot ln(C(s)) against log(s) • gradient=dc

  2. Example • Henon Map - xn+1=a-xn2+byn yn+1=xn dc=1.21 • Notice that the strange attractor in the Henon Map while it has structure at all length scales is not exactly self-similar

  3. Definitions of dimension • Two definitions so far • dF - the number of boxes need to cover fractal • dc - number of points within a given distance on fractal • Question: • is dc=dF ? • Very often no!

  4. When do the two dimensions agree ? • For exactly self-similar fractals like the Sierpinski triangle dC=dF

  5. When do they not - strange attractors • Eg. The logistic map at ac x=ax(1-x) • dc=0.498 • dF=0.537 • So, in this case these two dimensions are not equal! • Same is true for Henon.

  6. MultiFractals • For most “real world” fractals dc is not equal to dF ! • Strange attractors fall into this category eg. logistic map • These attractors have structure at all length scales but are not exactly self-similar. • Called multifractals

  7. Examples of multifractals • Diffusion limited aggregation or DLA • grow a crystal by allowing molecules to move randomly until they stick to substrate • stick preferentially near tips of growing structure • (multi)fractal • In 2D (correlation) fractal dimension DLA cluster is dF=1.7.. • i.e mass=L1.7

  8. Viscous fingering • Similar problem: • two miscible liquids (gelatin and water), DLA-like structure appears when mixed carefully. • Low surface tension • immiscible liquids (water and oil) fingers are wider • tension is large • For oil recovery - add soap to lower surface tension - allows water to penetrate shales and flush out oil ...

  9. Fractals in Nature • Coastline of Norway • Fjords of all sizes ! • Length of coastline depends on scale at which we look • count how many boxes the outline of the coast penetrates • see dF=1.52! • Scales from 30,000 km to 2500 km • Bronchial tree.

  10. Explanation • It looks fractal - but how do we know for sure ... • A single tube of diameter D splits into 2 tubes of diameter d • 2(d/D)3=1 approx. • Remember Cantor’s set …. D=ln(2)/ln(3) or .. 2*(1/3)D=1 • fractal with dimension D=3! • Space-filling!

  11. Fractal dimensionfor multifractals • For exact fractal NrD=1 N=number of pieces r=length of each • Generalize: • eg. At each iteration split into 2 pieces but with different lengths r1 and r2 r1D+r2D=1

More Related