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Related Experiment Video

Updated: Jun 8, 2026

The Diffusion of Passive Tracers in Laminar Shear Flow

The Diffusion of Passive Tracers in Laminar Shear Flow

Published on: May 1, 2018

Lattice splitting under intermittent flows.

Markus Schläpfer¹, Konstantinos Trantopoulos

¹Laboratory for Safety Analysis, ETH Zurich, 8092 Zurich, Switzerland.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics

|September 28, 2010

Summary

We investigated how stochastic intermittent flows split regular square lattices. Flow patterns influence splitting time, with higher fluctuation frequency initially shortening it, but inertia effects can lengthen it. Network size after splitting depends minimally on flow frequency.

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Area of Science:

Network science
Statistical physics
Complex systems

Background:

Regular square lattices are fundamental network structures.
Stochastic intermittent flows can destabilize network integrity.
Understanding network splitting is crucial for infrastructure resilience.

Purpose of the Study:

To analyze the splitting dynamics of regular square lattices under stochastic intermittent flows.
To determine the influence of flow patterns and fluctuation frequency on network splitting time.
To investigate the impact of network parameters on the post-splitting component size.

Main Methods:

Modeling network splitting using stochastic intermittent flows.
Generating various flow patterns by node state alternation.
Employing Monte Carlo simulations to study splitting dynamics.
Analyzing the relationship between flow characteristics and network behavior.

Main Results:

Flows decrease with the number of node groups following a power law.
Splitting time is dependent on flow patterns and fluctuation frequency.
Inertia effects can cause splitting time to increase at high fluctuation frequencies.
Largest connected component size after splitting is largely independent of fluctuation frequency but decreases with link capacities.

Conclusions:

Network splitting is sensitive to flow dynamics and inertia.
Findings have implications for the stability of real-world networks, like power grids.
Resilience of networks with intermittent energy sources can be better understood.