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An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids

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Published on: December 4, 2017

Large compact clusters and fast dynamics in coupled nonequilibrium systems.

Shauri Chakraborty¹, Sukla Pal¹, Sakuntala Chatterjee¹

¹Department of Theoretical Sciences, S.N. Bose National Centre for Basic Sciences, Block-JD, Sector-III, Salt Lake, Kolkata 700098, India.

Physical Review. E

|June 15, 2016

Summary

This study reveals particle clustering in a coupled nonequilibrium system. Novel phases emerge where separated particles create ordered and disordered landscape regions, leading to unique coarsening dynamics.

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Area of Science:

Complex Systems
Statistical Physics
Soft Matter Physics

Background:

Nonequilibrium systems exhibit unique behaviors not seen in equilibrium.
Coupled particle-landscape interactions are crucial for understanding emergent phenomena.
Fluctuating landscapes can drive particle dynamics and self-organization.

Purpose of the Study:

To investigate particle clustering on macroscopic scales in a coupled particle-landscape system.
To explore novel nonequilibrium phases and their characteristics.
To analyze the dynamics of landscape ordering and coarsening.

Main Methods:

Simulating a two-species particle system advected by a fluctuating landscape.
Modifying the landscape through particle interactions.
Analyzing phase diagrams across varying particle densities and dimensions (1D and 2D).
Characterizing coarsening and steady-state dynamics.

Main Results:

Demonstrated particle clustering on macroscopic scales.
Generated a phase diagram revealing novel nonequilibrium phases.
Observed complete phase separation between particle species.
Showcased landscape ordering with coexisting disordered regions.
Identified algebraic growth of time scales for coarsening and steady-state dynamics.

Conclusions:

Coupled particle-landscape interactions lead to emergent macroscopic clustering and novel nonequilibrium phases.
The system exhibits unique dynamics distinct from pure domain systems.
The findings offer insights into self-organization in complex interacting systems.