
Type of Document Dissertation Author Jeon, YoungPyo Author's Email Address yjeon1@lsu.edu, liger4004@hotmail.com URN etd10062006233328 Title Distribution Dynamics of Complex Systems Degree Doctor of Philosophy (Ph.D.) Department Chemical Engineering Advisory Committee
Advisor Name Title Martin A. Hjortso Committee Chair Elizabeth J. PodlahaMurphy Committee Member Kalliat T. Valsaraj Committee Member Karsten E. Thompson Committee Member Srinath V. Ekkad Dean's Representative Keywords
 population balance
 complex systems
 distribution dynamics
 networks
Date of Defense 20060921 Availability unrestricted Abstract A complex system is defined as a system with many interdependent parts having emergent selforganization; analyzing and designing such complex systems is a new challenge. A common observable structure of many complex systems is the network, which is connections among nodes, and thus inherently difficult to describe. The goal of this research is to introduce an effective methodology to describe complex systems, and thus we will construct a population balance (distribution kinetics) model based on the associationdissociation process to describe the evolution of complex systems.Networks are commonly observed structures in complex systems with interdependent parts that selforganize. How networks come into existence and how they change with time are fundamental issues in numerous networked systems. Based on the nodallinkage distribution, we propose a unified population dynamics approach for the network evolution. Sizeindependent rate coefficients yield an exponential network without preferential attachment, and sizedependent rate coefficients produce a power law network with preferential attachment.
For nonlinearly growing networks, when the total number of connections increases faster than the total number of nodes, the network is said to accelerate. We propose a systematic model, a population dynamics model, for the dynamics of growing networks represented by distribution kinetics equations, and perform the moment calculations to describe the dynamics of such networks.
Power law distributions have been observed in numerous physical and social systems; for example, the size distributions of particles and cities are often power laws. Each system is an ensemble of clusters, comprising units that combine with or dissociate from the cluster. To describe the growth of clusters, we hypothesize that a distribution obeys a governing population dynamics equation based on reversible associationdissociation processes. The rate coefficients considered to depend on the cluster size as power expressions provide an explanation for the asymptotic evolution of power law distributions.
To mathematically represent humaninitiated phenomena, which recently recognized as power law distributions, we apply the framework of cluster kinetics to the study of waitingtime distributions of human activities. The model yields both exponential and power law distributed systems, depending on the expressions for the rate coefficients in a FokkerPlanck equation.
Files
Filename Size Approximate Download Time (Hours:Minutes:Seconds)
28.8 Modem 56K Modem ISDN (64 Kb) ISDN (128 Kb) Higherspeed Access Jeon_dis.pdf 1.21 Mb 00:05:35 00:02:52 00:02:30 00:01:15 00:00:06