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An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids
Published on: December 4, 2017
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Intrinsic and Extrinsic Thermodynamics for Stochastic Population Processes with Multi-Level Large-Deviation
Eric Smith1,2,3,4
1Department of Biology, Georgia Institute of Technology, 310 Ferst Drive NW, Atlanta, GA 30332, USA.
Entropy (Basel, Switzerland)
|December 8, 2020
Summary
This study introduces a new thermodynamic framework based on large deviations, applicable beyond traditional conservation laws. It defines intensive and extensive thermodynamics using information theory for complex systems like chemical reactions.
Area of Science:
- Thermodynamics
- Information Theory
- Statistical Mechanics
Background:
- Traditional thermodynamics relies on conservation laws and microscopic reversibility.
- Stochastic thermodynamics extends these concepts to systems with fluctuating dynamics.
- Hierarchies of timescales and large-deviation properties offer a new perspective.
Purpose of the Study:
- To establish core features of a thermodynamic description independent of conservation laws.
- To decompose irreversibility contributions among subsystems.
- To develop a framework applicable to rule-based systems and living matter.
Main Methods:
- Utilizing large-deviation properties and Hartley information.
- Decomposing thermodynamic descriptions into intensive (relative entropy) and extensive (housekeeping entropy rate) components.
- Analyzing stochastic chemical reaction networks.
Main Results:
- Defined intensive and extensive thermodynamics based on information content of system states.
- Established a Legendre duality for the housekeeping entropy rate.
- Characterized fully-irreversible processes alongside detailed-balance limits.
Conclusions:
- The proposed framework provides a fundamental thermodynamic description for systems with timescale hierarchies.
- This approach is independent of conservation laws and microscopic reversibility.
- It offers a path towards inherent thermodynamic descriptions for complex, living systems.
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