A Numerical Method to Find the Optimal Thermodynamic Cycle in Microscopic Heat Engine

TL;DR

This paper introduces a genetic algorithm-based numerical method to optimize thermodynamic cycles in microscopic heat engines, enhancing their performance by systematically finding optimal parameter variations.

Contribution

The paper presents a novel systematic numerical approach using genetic algorithms to optimize heat engine cycles, applicable to slowly varying parameter regimes.

Findings

The method effectively finds optimal cycles for microscopic heat engines.

It demonstrates good performance in systems with slowly varying parameters.

The approach can be generalized to other thermodynamic optimization problems.

Abstract

Heat engines are fundamental physical objects to develop nonequilibrium thermodynamics. The thermodynamic performance of the heat engine is determined by the choice of cycle and time-dependence of parameters. Here, we propose a systematic numerical method to find a heat engine cycle to optimize some target functions. We apply the method to heat engines with slowly varying parameters and show that the method works well. Our numerical method is based on the genetic algorithm which is widely applied to various optimization problems.