Characterization and optimization of heat engines: Pareto-optimal fronts and universal features (2504.21717v1)

Abstract: Characterizing and optimizing nanoscopic heat engines require an appropriate understanding of the interplay between power, efficiency, entropy production and fluctuations. Despite significant recent advancements, including linear stochastic thermodynamics and thermodynamic uncertainty relations (TURs), a complete scenario remains elusive. In this work, we give a further step by showing that, under certain common and general conditions, the heat engine regime can be characterized by the minima of power fluctuations and entropy production, which together delimit its optimal performance, achieved when these conditions are fully satisfied. Conversely, when these conditions are not strictly met, the occurrence of the minimum still approximately describes the system, suggesting a broader range of applicability. Contrasting with most of studies in which the system optimization is carried out solely taking into account the power and efficiency, we introduce a multi-objective optimization framework based on Pareto fronts, also considering the role of fluctuation and dissipation. Our results reveal a general trend: while simultaneous optimization over a few parameters typically yields convex Pareto fronts, these fronts become concave as more parameters are varied freely and non-conservative driving becomes significant. Illustrating our findings, we consider simple two and three state systems as well as richer collective systems, exhibiting novel aspects of optimizations and protocol phase transitions.