Unified approach to power-efficiency trade-off relations of generic thermal machines
Yu-Han Ma, Cong Fu
TL;DR
The paper develops a universal framework connecting power and efficiency for generic thermal machines by tying cycle irreversibility to a scaling $A(\tau)\propto\tau^{-\alpha}$ and describing machines with a sign-function formalism. It derives a saturable $P$–$\eta$ bound and a general EMP formula $\eta_{\rm MP}=\frac{\alpha\eta_{\rm rev}}{1+\alpha-\chi\eta_{\rm rev}}$, showing that increasing $\alpha$ can bring EMP closer to reversible efficiency, while recovering known results for $\alpha=1$ (low-dissipation) and providing the first $\alpha=2$ result for finite-time quantum adiabatic Otto cycles. The framework unifies thermodynamic bounds across engines, refrigerators, and heat pumps and yields concrete, regime-specific forms (e.g., slow-driving isothermal and finite-time quantum adiabatic) to guide practical optimization. The work thus enables consistent optimization of diverse thermal devices in non-equilibrium regimes and predicts enhanced performance under tailored system–reservoir interactions.
Abstract
We present a general framework for determining the power-efficiency trade-off relations across arbitrary thermal machines, addressing the lack of unified optimization results stemming from their diverse functionalities (e.g., heat engines, refrigerators, and heat pumps). For time-dependent cycle irreversibility $A(τ)$ following a $τ^{-α}$ power law, where $α$ is an interaction-dependent parameter, we show that engineering the interactions between thermal machines and reservoirs enables control over the trade-off relations, with the efficiency at maximum power approaching Carnot efficiency as $α$ increases. Setting $α=1$ naturally recovers typical low-dissipation regime results. Additionally, we derive the first power-efficiency trade-off for finite-time quantum adiabatic Otto machines with $τ^{-2}$-scaling. This work establishes a unified constraint for thermodynamic cycles across non-equilibrium regimes, facilitating consistent optimization of diverse thermal devices in practice.
