Optimization of an active heat engine.

Area of Science:

• Statistical physics
• Nanotechnology
• Thermodynamics

Background:

• Microscale heat engines are vital for biological and artificial nanotechnology.
• Theoretical research in nonequilibrium statistical physics is stimulated by microscale engine optimization.
• Existing models often lack a consistent definition of effective temperature for active systems.

Purpose of the Study:

• To optimize heat engines operating with active particles (active Ornstein-Uhlenbeck model).
• To define and utilize a meaningful effective temperature for thermodynamic performance.
• To derive optimal parameters for maximum power and efficiency in active heat engines.

Main Methods:

• Considered noninteracting overdamped particles in a harmonic potential subjected to thermal reservoirs or active self-propulsion.
• Developed a cyclical machine by periodically varying potential and noise parameters.
• Established an exact mapping between passive and active models to define effective temperature T_{eff}(t).

Main Results:

• Defined a novel effective temperature T_{eff}(t) distinct from existing active temperatures.
• Showed that T_{eff}(t) enables optimization of active engines irrespective of persistence time or self-propulsion velocity.
• Derived explicit formulas for optimal cycle period and phase delay for maximum power and Curzon-Ahlborn efficiency.

Conclusions:

• The defined effective temperature T_{eff}(t) is crucial for understanding and optimizing active heat engine performance.
• Linear irreversible thermodynamics provides a framework for achieving maximum power and efficiency.
• A consistent definition of effective temperature is essential for evaluating engine efficiency in active systems.