Chapter 13 - Davidson Physics

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CHAPTER 14. FRACTALS AND KINETIC GROWTH MODELS 526}plotFrame . append ( 0 , Math . l o g ( r ) , Math . l o g ( massEnclosed ) ) ;r P r i n t *= 2 ;}}plotFrame . s e t V i s i b l e ( true ) ;public void r e s e t ( ) {c o n t r o l . setValue ( "L" , 6 1 ) ;c o n t r o l . setValue ( "p" , 0 . 5 9 2 7 ) ;s e t S t e p s P e r D i s p l a y ( 1 0 ) ;enableStepsPerDisplay ( true ) ;}}public s t a t i c void main ( S t r i n g [ ] a r g s ) {SimulationControl . createApp (new SingleClusterApp ( ) ) ;}We will use the Leath or single cluster growth algorithm in Problem 14.3 to generate a spanningcluster at the percolation threshold. The fractal dimension is determined by counting the numberof sites M in the cluster within a distance r of the center of mass of the cluster. The center ofmass is defined byr cm = 1 ∑r i , (14.4)Nwhere N is the total number of particles in the cluster. A typical plot of ln M(r) versus ln r isshown in Figure 14.4. Because the cluster cannot grow past the edge of the lattice, we do notinclude data for r ≈ L.Problem 14.3. Single cluster growth and the fractal dimensiona. Explain how the Leath algorithm generates single clusters in a way that is equivalent to themultiple clusters that are generated by visiting all sites. More precisely, the Leath algorithmgenerates percolation clusters with a distribution of cluster sizes equal to sn s . For example, ifyou grow 10 clusters of size s = 2, then n s = 10/2 = 5. The additional factor of s is due to thefact that each site of the cluster has an equal chance of being the seed of the cluster, and hencethe same cluster can be generated in s ways.b. Grow as large a spanning cluster as you can and look at it on different length scales. Oneway to do so is to divide the screen into four windows, each of which magnifies a part of thecluster shown in the previous window. Does the part of the cluster shown in each window lookapproximately self-similar?c. Choose p = 0.5927 and L ≥ 61 and generate at least ten configurations of spanning clusters.Determine the number of occupied sites M(r) within a distance r of the seed site of each cluster.(Better results can be found by choosing the origin to be the center of mass of each cluster.)Average M(r) over the spanning clusters. Estimate D from the log-log plot of M versus r (seeFigure 14.4). If time permits, generate percolation clusters on larger lattices.i
CHAPTER 14. FRACTALS AND KINETIC GROWTH MODELS 527108ln M64200 1 2 3 4 5ln rFigure 14.4: Plot of ln M versus ln r for a single spanning percolation cluster generated at p =0.5927 on a L = 129 square lattice. The straight line is a linear least squares fit to the data. Theslope of this line is 1.91 and is an estimate of the fractal dimension D. The exact value of D for apercolation cluster at p = p c in two dimensions is D = 91/48 ≈ 1.896.d. Generate clusters at p = 0.65, a value of p greater than p c , for L = 101. Make a log-log plot ofM(r) versus r. Is the slope approximately equal to the value of D found in part (c)? Does theslope increase or decrease for larger r? Repeat for p = 0.80. Is a spanning cluster generated atp > p c a fractal?e. The fractal dimension of percolation clusters is not an independent exponent, but satisfies thescaling relation,D = d − β/ν, (14.5)where β and ν are defined in Table 13.1. The relation (14.5) can be understood by the followingfinite-size scaling argument. The number of sites in the spanning cluster on a lattice of lineardimension L is given byM(L) ∼ P ∞ (L)L d , (14.6)where P ∞ is the probability that an occupied site belongs to the spanning cluster and L d is thetotal number of sites in the lattice. In the limit of an infinite lattice and p near p c , we knowthat P ∞ (p) ∼ (p − p c ) β and ξ(p) ∼ (p − p c ) −ν independent of L. Hence for L ∼ ξ, we have thatP ∞ (L) ∼ L −β/ν (see (13.11)), and we can writeM(L) ∼ L −β/ν L d ∼ L D . (14.7)The relation (14.5) follows. Use the exact values of β and ν from Table 13.1 to find the exactvalue of D for d = 2. Is your estimate for D consistent with this value?
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Chapter 13 - Davidson Physics

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