Braunstein Lidia A, Buldyrev Sergey V, Havlin Shlomo, Stanley H Eugene
Center for Polymer Studies and Department of Physics, Boston University, Boston, Massachusetts 02115, USA.
Phys Rev E Stat Nonlin Soft Matter Phys. 2002 May;65(5 Pt 2):056128. doi: 10.1103/PhysRevE.65.056128. Epub 2002 May 21.
We study the behavior of self-avoiding walks (SAWs) on square and cubic lattices in the presence of strong disorder. We simulate the disorder by assigning random energy epsilon taken from a probability distribution P(epsilon) to each site (or bond) of the lattice. We study the strong disorder limit for an extremely broad range of energies with P(epsilon) is proportional to 1/epsilon. For each configuration of disorder, we find by exact enumeration the optimal SAW of fixed length N and fixed origin that minimizes the sum of the energies of the visited sites (or bonds). We find the fractal dimension of the optimal path to be d(opt)=1.52+/-0.10 in two dimensions (2D) and d(opt)=1.82+/-0.08 in 3D. Our results imply that SAWs in strong disorder with fixed N are much more compact than SAWs in disordered media with a uniform distribution of energies, optimal paths in strong disorder with fixed end-to-end distance R, and SAWs on a percolation cluster. Our results are also consistent with the possibility that SAWs in strong disorder belong to the same universality class as the maximal SAW on a percolation cluster at criticality, for which we calculate the fractal dimension d(max)=1.64+/-0.02 for 2D and d(max)=1.87+/-0.05 for 3D, values very close to the fractal dimensions of the percolation backbone in 2D and 3D.
我们研究了在存在强无序情况下,正方形和立方晶格上的自回避行走(SAW)行为。我们通过从概率分布P(ε)中为晶格的每个格点(或键)分配一个随机能量ε来模拟无序。我们研究了P(ε)与1/ε成正比的极广能量范围内的强无序极限。对于每种无序构型,我们通过精确枚举找到固定长度N和固定起点的最优SAW,其使访问格点(或键)的能量总和最小。我们发现最优路径的分形维数在二维(2D)中为d(opt)=1.52±0.10,在三维(3D)中为d(opt)=1.82±0.08。我们的结果表明,具有固定N的强无序中的SAW比能量均匀分布的无序介质中的SAW、具有固定端到端距离R的强无序中的最优路径以及渗流团簇上的SAW要紧凑得多。我们的结果还与强无序中的SAW与临界状态下渗流团簇上的最大SAW属于同一普适类的可能性一致,对于后者,我们计算出二维中的分形维数d(max)=1.64±0.02,三维中的分形维数d(max)=1.87±0.05,这些值与二维和三维中渗流骨架的分形维数非常接近。