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Phase diagram and density large deviations of a nonconserving ABC model.
1Department of Physics of Complex Systems, Weizmann Institute of Science, Rehovot, Israel.
Physical Review Letters
|March 10, 2012
Summary
Particle-nonconserving processes influence driven diffusive systems. Slow nonconserving dynamics allow computation of particle density large deviation functions using equilibrium methods like Maxwell
Area of Science:
- Statistical Mechanics
- Non-equilibrium Physics
- Complex Systems
Background:
- Driven diffusive systems are fundamental models in non-equilibrium statistical mechanics.
- Particle-nonconserving processes introduce complexity to steady-state properties.
- Understanding these effects is crucial for modeling various physical and biological systems.
Purpose of the Study:
- To investigate the impact of particle-nonconserving processes on driven diffusive systems.
- To develop a method for calculating steady-state properties under slow nonconserving dynamics.
- To establish connections between non-equilibrium driven systems and equilibrium thermodynamics.
Main Methods:
- Utilizing a generalized ABC model to represent driven diffusive systems.
- Analyzing the steady-state density profile of the conserving model.
- Applying methods analogous to equilibrium systems, including chemical potential and Maxwell's construction, in the limit of slow nonconserving processes.
Main Results:
- Demonstrated that the large deviation function of particle density can be computed using the steady-state profile of the conserving model under slow nonconserving processes.
- Showed that a chemical potential can be defined in this limit.
- Identified first-order transitions via Maxwell's construction, mirroring equilibrium system behavior.
Conclusions:
- The study provides a novel method to analyze driven diffusive systems with slow particle-nonconserving dynamics.
- This approach bridges non-equilibrium physics with equilibrium concepts, offering new analytical tools.
- The method is potentially applicable to a broader range of driven systems with similar dynamics.
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