引用本文格式: 贾啸,王睿婕,张田. 随机网络中三种渗流相变的交叉 [J]. 四川大学学报: 自然科学版, 2020, 57: 903~908.
 
随机网络中三种渗流相变的交叉
Crossover of three percolation transitions types in random networks
摘要点击 243  全文点击 43  投稿时间:2020-04-26  修订日期:2020-05-12
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DOI编号   
中文关键词   渗流  相变  相对方差  随机网络
英文关键词   Percolation  Phase transition  Relative variance  Random networks
基金项目   国家自然科学基金(61703292); 四川省教育厅基金项目(18ZB0483)
作者单位E-mail
贾啸 四川师范大学物理与电子工程学院 成都 610101 xiaojia@sicnu.edu.cn 
王睿婕 阿坝师范学院, 阿坝 623002 ruijiewang001@163.com 
张田 四川师范大学物理与电子工程学院 成都 610101  
Author NameAffiliationE-mail
JIA Xiao College of Physics and Electronic Engineering, Sichuan Normal University, Chengdu 610101, China xiaojia@sicnu.edu.cn 
WANG Rui-Jie Aba Teachers University, Aba 623002, China ruijiewang001@163.com 
ZHANG Tian College of Physics and Electronic Engineering, Sichuan Normal University, Chengdu 610101, China  
中文摘要
    基于一个简单的分段线性权函数, 研究了参数α统一两种不同三临界的渗流模型,当调节α的值从1到0渗流相变从连续变化到多重不连续和不连续.通过计算不同系统尺寸下序参量的相对方差,表明α=0.6时在热力学极限下跌落为一个奇异峰,α=0.5时在某个超临界区交织在一起,α=0.4时在一个延伸的区间上随系统尺寸的增加变得更大.因此对于相变从连续变化到多重不连续和从多重不连续变化到不连续,三相点分别位于0.6和0.5以及0.4和0之间.该框架为理解随机网络中不同类型相变之间的交叉行为提供了见解.
英文摘要
    Based on a simple piecewise linear weighted function, we unified two different types of tricritical percolation with a parameter α in one model, in which by tuning α from 1 to 0 the phase transition can switch from continuous to multiple discontinuous to discontinuous. By calculating the relative variance of the order parameter with different system sizes, we find that it collapses to a singular peak at the critical point in the thermodynamical limit at α=0.6, and interlaces together on a supercritical interval at α=0.5, and becomes larger on an extended interval at α=0.4 with increasing system size, respectively. It shows that the tricritical value of α is between 0.6 and 0.5 as well as between 0.4 and 0 from continuous to multiple discontinuous as well as from multiple discontinuous to discontinuous respectively. Our framework provides insights into understanding the crossover behaviors between different types of phase transition in random networks.

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