Maximum power Stirling-like heat engine with a harmonically confined Brownian particle

TL;DR

This paper investigates a mesoscopic Stirling-like heat engine using a Brownian particle in harmonic confinement, optimizing power output through control theory, and analyzing efficiency and power dependence on system parameters.

Contribution

It introduces a novel stochastic thermodynamics model of a Stirling-like engine with optimal control protocols for maximum power output.

Findings

Maximum power output is achieved with specific control protocols.

Engine efficiency at maximum power is characterized.

Dependence of power and efficiency on system parameters is quantified.

Abstract

Heat engines transform thermal energy into useful work, operating in a cyclic manner. For centuries, they have played a key role in industrial and technological development. Historically, only gases and liquids have been used as working substances, but the technical advances achieved over the past decades allow for expanding the experimental possibilities and designing engines operating with a single particle. In this case, the system of interest cannot be addressed at a macroscopic level and their study is framed in the field of stochastic thermodynamics. In the present work, we study mesoscopic heat engines built with a Brownian particle submitted to harmonic confinement and immersed in a fluid acting as a thermal bath. We design a Stirling-like heat engine, composed of two isothermal and two isochoric branches, by controlling both the stiffness of the harmonic trap and the temperature of the bath. Specifically, we focus on the irreversible, non quasi-static, case -- whose finite duration enables the engine to deliver a non-zero output power. This is a crucial aspect, which enables the optimisation of the thermodynamic cycle by maximising the delivered power -- thereby addressing a key goal at the practical level. The optimal driving protocols are obtained by using both variational calculus and optimal control theory tools. Also, we numerically explore the dependence of the maximum output power and the corresponding efficiency on the system parameters.