Linear irreversible thermodynamics and Onsager reciprocity for information-driven engines

Area of Science:

• Thermodynamics
• Information Theory
• Statistical Mechanics

Background:

• Information is increasingly recognized as a thermodynamic resource in nonequilibrium systems.
• Autonomous information processing is a key area in modern physics and computer science.
• Understanding the interplay between information and thermodynamics is crucial for developing new technologies.

Purpose of the Study:

• To establish a theoretical framework for linear irreversible thermodynamics in autonomous information processing.
• To investigate the validity of Onsager reciprocity in the context of information flow.
• To derive universal bounds for the efficiency of information-driven engines.

Main Methods:

• Development of a linear irreversible thermodynamics framework for information processing systems.
• Mathematical proof of Onsager reciprocity including information affinity.
• Derivation of efficiency bounds using linear response theory.

Main Results:

• Onsager reciprocity is shown to hold true for information-driven processes.
• A symmetric linear response matrix is demonstrated for both information and thermodynamic affinities.
• A universal bound for the efficiency at maximum power of information-driven engines in the linear regime was derived.

Conclusions:

• Information flow plays a fundamental role in linear irreversible thermodynamics.
• The established framework provides a basis for understanding and designing information-driven thermodynamic engines.
• The findings have implications for fields ranging from nanoscale engines to biological systems.