Poster (After Call)

FZJ-2022-04866

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Noise-induced breakdown in linear self-driven particle systems

Cordes, J. (Corresponding author)FZJ* ; Tordeux, A. ; Schadschneider, A. ; Rüdiger, B. ; Ugurcan, B.

2022

Please use a persistent id in citations: http://hdl.handle.net/2128/32750

Abstract: Self-driven particle systems can describe many self-organized phenomena. Prominentexamples are oscillation, wave, lane, or band formations, among other swarming andcoordinated movements. Collective dynamics are classically understood in the literature of active matter as motility-induced phase separation using meta-stable non-linear in-teraction models. Critical settings of the parameters separate the disorder states from non-uniform dynamics describing macroscopic patterns and structures. We show in this contribution that noise effects can initiate the spontaneous formation of waves insingle-file motions of self-driven particles. In contrast to usual modeling approaches, no non-linear interaction mechanisms or phase transitions are necessary to coordinate the dynamics. The stochastic effects initiate the self-organization in the second order in a purely linear and ergodic framework. The coupling of the noise to a discrete gradient in space yields the emergence of stop-and-go waves, which we characterize using speed and spacing auto-correlation functions. Varying the characteristics of the noise allows for obtaining rich dynamics ranging from coupled dynamics and stable homogeneous dynamics to stop-and-go patterns with deterministic oscillating features.

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Contributing Institute(s):

1. Zivile Sicherheitsforschung (IAS-7)

Research Program(s):

1. 5111 - Domain-Specific Simulation Data Life Cycle Labs (SDLs) and Research Groups (POF4-511) (POF4-511)
2. DFG project 446168800 - Multi-Agent-Modellierung der Dynamik von dichten Fußgängermengen: Vorhersagen Verstehen (446168800) (446168800)

Appears in the scientific report 2022

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Database coverage:

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