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Synchronization from disordered driving forces in arrays of coupled oscillators
Sebastian F Brandt1, Babette K Dellen, Ralf Wessel
1Department of Physics, Campus Box 1105, Washington University, St. Louis, Missouri 63130-4899, USA.
Physical Review Letters
|February 21, 2006
Summary
Disordered driving forces can control chaos in nonlinear oscillator networks. Introducing optimal phase disorder can lead to network synchrony, transforming chaotic dynamics into predictable, periodic behavior.
Area of Science:
- Nonlinear dynamics
- Complex systems science
- Network theory
Background:
- Coupled nonlinear oscillator networks are fundamental in modeling complex systems.
- Synchronous driving forces often lead to chaotic dynamics in these networks.
- Understanding the impact of external force disorder is crucial for controlling system behavior.
Purpose of the Study:
- To investigate how disorder in external driving forces affects the dynamical behavior of coupled nonlinear oscillator networks.
- To determine the relationship between phase disorder and network synchrony.
- To explore the potential for controlling chaos through external influences.
Main Methods:
- Simulations of coupled nonlinear oscillator networks under varying degrees of phase disorder in external driving forces.
- Analysis of network dynamics, focusing on measures of chaos and synchrony.
- Systematic variation of phase disorder to identify critical transitions and optimal conditions.
Main Results:
- Synchronous driving (zero phase disorder) results in chaotic network dynamics.
- Random phase disorder leads to regular, periodic network behavior.
- An optimal level of intermediate phase disorder maximizes network synchrony, surpassing that achieved with purely random or synchronous driving.
Conclusions:
- The spatiotemporal structure of external forces is a key factor in controlling chaos in nonlinear systems.
- Phase disorder can be effectively utilized to induce and optimize network synchrony.
- These findings offer novel strategies for controlling complex nonlinear systems through tailored external influences.
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