Failure mechanisms of complex systems

Non-equilibrium systems;Statistical physics;Extremal dynamics;Cascading failure;Network congestion;Monte Carlo simulations;Reliability theory

Siddique, Shahnewaz ; Feron, Eric M. Aerospace Engineering Volovoi, Vitali V. Hodges, Dewey H. Schrage, Daniel P. Shamma, Jeff S. ; Feron, Eric M.

University:Georgia Institute of Technology

Department:Aerospace Engineering

关键词: Non-equilibrium systems;    Statistical physics;    Extremal dynamics;    Cascading failure;    Network congestion;    Monte Carlo simulations;    Reliability theory;   

Others  :  https://smartech.gatech.edu/bitstream/1853/51831/1/SIDDIQUE-DISSERTATION-2014.pdf

美国|英语

来源: SMARTech Repository

【 摘 要 】

Understanding the behavior of complex, large-scale, interconnected systems in a rigorous and structured manner is one of the most pressing scientific and technological challenges of current times. These systems include, among many others, transportation and communications systems, smart grids and power grids, financial markets etc. Failures of these systems have potentially enormous social, environmental and financial costs. In this work, we investigate the failure mechanisms of load-sharing complex systems. The systems are composed of multiple nodes or components whose failures are determined based on the interaction of their respective strengths and loads (or capacity and demand respectively) as well as the ability of a component to share its load with its neighbors when needed. Each component possesses a specific strength (capacity) and can be in one of three states: failed, damaged or functioning normally. The states are determined based on the load (demand) on the component.We focus on two distinct mechanisms to model the interaction between components strengths and loads. The first, a Loss of Strength (LOS) model and the second, a Customer Service (CS) model. We implement both models on lattice and scale-free graph network topologies. The failure mechanisms of these two models demonstrate temporal scaling phenomena, phase transitions and multiple distinct failure modes excited by extremal dynamics. We find that the resiliency of these models is sensitive to the underlying network topology. For critical ranges of parameters the models demonstrate power law and exponential failure patterns. We find that the failure mechanisms of these models have parallels to failure mechanisms of critical infrastructure systems such as congestion in transportation networks, cascading failure in electrical power grids, creep-rupture in composite structures, and draw-downs in financial markets. Based on the different variants of failure, strategies for mitigating and postponing failure in these critical infrastructure systems can be formulated.