Suppr超能文献

复杂网络上具有质量依赖碎片化的守恒质量聚集模型。

Conserved-mass aggregation model with mass-dependent fragmentation on complex networks.

作者信息

Kwon Sungchul, Lee Dong-Jin, Kim Yup

机构信息

Department of Physics and Research Institute for Basic Sciences, Kyung Hee University, Seoul 130-701, Korea.

出版信息

Phys Rev E Stat Nonlin Soft Matter Phys. 2008 Sep;78(3 Pt 2):036113. doi: 10.1103/PhysRevE.78.036113. Epub 2008 Sep 19.

Abstract

We study the conserved-mass aggregation model with mass-dependent fragmentation on random networks (RNs) and scale-free networks (SFNs) with degree distribution Pk approximately k(-gamma). In the model, masses isotropically diffuse with unit rate. With rate omega, a mass m(lambda) is fragmented from a node with mass m , and moves to one of the linked nodes with the equal probability. For lambda=0 , the model is known to undergo the condensation phase transitions at a certain criticality rho(c) . From the mean-field balance equation for an aggregate, we analytically show that the present model exhibits different behavior depending on network structures. The condensation always occurs for lambda<0 . For 0<lambda<1 , finite-sized systems on RNs and SFNs with gamma>3 undergoes the condensation transitions (sharp crossovers) at rho(c) which diverges with network size N as N(lambda). Hence, in the limit N-->infinity , masses uniformly distribute without the condensation (fluid phase). On the other hand, for gamma< or =3 , a crossover lambda(c)=1(gamma-1) exists. The condensation always occurs for lambda<lambda(c) , while the fluid phase is always stable at any nonzero density for lambda> or =lambda(c) . The phase separation results from the competition between the heterogeneity of network structure and the enhanced chipping by lambda . We numerically confirm all the predictions.

摘要

我们研究了在随机网络(RNs)和度分布(P_k\approx k^{-\gamma})的无标度网络(SFNs)上具有质量依赖碎片化的守恒质量聚集模型。在该模型中,质量以单位速率各向同性扩散。以速率(\omega),质量(m(\lambda))从质量为(m)的节点碎片化,并以相等概率移动到其中一个相连节点。对于(\lambda = 0),已知该模型在某个临界值(\rho_c)处经历凝聚相变。通过对聚集体的平均场平衡方程进行分析,我们表明当前模型根据网络结构表现出不同的行为。对于(\lambda < 0),凝聚总是会发生。对于(0 < \lambda < 1),(\gamma > 3)的RNs和SFNs上的有限尺寸系统在(\rho_c)处经历凝聚转变(急剧交叉),(\rho_c)随网络大小(N)以(N^{\lambda})的形式发散。因此,在(N \to \infty)的极限情况下,质量均匀分布而不发生凝聚(流体相)。另一方面,对于(\gamma \leq 3),存在一个交叉点(\lambda_c = \frac{1}{\gamma - 1})。当(\lambda < \lambda_c)时,凝聚总是会发生,而当(\lambda \geq \lambda_c)时,流体相在任何非零密度下总是稳定的。相分离是由网络结构的异质性与(\lambda)增强的碎片化之间的竞争导致的。我们通过数值方法证实了所有预测。

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验