Obtaining efficient thermal engines from interacting Brownian particles under time dependent periodic drivings

TL;DR

This paper proposes a new approach to designing efficient cyclic thermal engines using interacting Brownian particles driven periodically, with optimized protocols enhancing performance based on stochastic thermodynamics analysis.

Contribution

It introduces a novel method for creating reliable thermal engines with interacting particles under periodic driving, optimizing efficiency through parameter control.

Findings

Interactions improve engine efficiency

Optimized protocols enhance power output

Framework applicable to work-to-work and heat-to-work engines

Abstract

We introduce an alternative route for obtaining reliable cyclic engines, based on interacting Brownian particles under time-periodic drivings. General expressions for the thermodynamic fluxes, such as power and heat, are obtained using the framework of Stochastic Thermodynamics. Several protocols for optimizing the engine performance are considered, by looking at system parameters such as the output forces and their phase-difference. We study both work-to-work and heat-to-work engines. Our results suggest that carefully designed interactions between particles can lead to more efficient engines.