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Nonequilibrium temperatures in steady-state systems with conserved energy
Eric Bertin1, Olivier Dauchot, Michel Droz
1Department of Theoretical Physics, University of Geneva, CH-1211 Geneva 4, Switzerland.
Summary
This study introduces a statistical temperature for nonequilibrium systems, finding it more relevant than the fluctuation-dissipation temperature. This new temperature parameter quantifies deviation from equilibrium in conserved quantity models.
Area of Science:
- Statistical mechanics
- Condensed matter physics
- Non-equilibrium systems
Background:
- Lattice models with conserved quantities are crucial for understanding energy transport.
- Defining temperature in non-equilibrium systems remains a challenge.
- Existing fluctuation-dissipation relations may not fully capture non-equilibrium thermodynamics.
Purpose of the Study:
- To define and investigate a statistical temperature for a class of non-equilibrium lattice models.
- To compare this statistical temperature with the temperature derived from the fluctuation-dissipation relation.
- To establish a physically relevant temperature measure for isolated non-equilibrium systems.
Main Methods:
- Exact solutions for a subclass of lattice models.
- Computation of response functions.
- Numerical renormalization group (NRG) procedures.
- Mean-field theory for quantitative predictions.
Main Results:
- A statistical temperature, T(th), was defined analogous to the equilibrium micro-canonical ensemble.
- The fluctuation-dissipation temperature, T(FD), was found to differ from T(th) when the relation is linear.
- T(th) was shown to be more physically relevant, equalizing in subsystems of large isolated systems.
- A universal parameter describing deviation from equilibrium was identified across models.
Conclusions:
- The statistical temperature T(th) provides a more physically meaningful measure in non-equilibrium steady states.
- The identified deviation parameter offers a way to characterize non-equilibrium behavior universally.
- Mean-field theory yields quantitative predictions for this deviation parameter.