随机 Scovil--Schulz-DuBois 热机及其三种循环
Stochastic Scovil--Schulz-DuBois machine and its three types of cycles
AI总结：
该研究在 SSDB 热机的量子跃迁轨迹中识别出制冷、热机和中性三种循环，并用半马尔可夫过程方法揭示其统计规律，解释了热浴温度超过阈值后功率不再增加的原因。
摘要：
在 Scovil--Schulz-DuBois (SSDB) 热机的量子跃迁轨迹中识别出三种循环：作为制冷的 R 循环、作为热机的 H 循环以及机器处于中性状态的 N 循环。通过半马尔可夫过程方法研究了这些循环的统计性质。我们发现，在大时间极限下，机器是作为热机还是制冷机运行，取决于单位时间内 R 循环数与 H 循环数之比。进一步将热浴温度提高到某个阈值以上并不会增加机器的功率输出。其原因在于，在这种情况下，N 循环的概率大于 H 循环和 R 循环。尽管 SSDB 热机通过在这三种循环之间随机切换来运行，但在单条量子跃迁轨迹的层面上，其热机效率和制冷机的性能系数保持恒定。
英文摘要：
Three types of cycles are identified in the quantum jump trajectories of the Scovil--Schulz-DuBois (SSDB) machine: an R-cycle as refrigeration, an H-cycle as a heat engine, and an N-cycle in which the machine is neutral. The statistics of these cycles are investigated via a semi-Markov process method. We find that in the large time limit, whether the machine operates as a heat engine or refrigerator depends on the ratio between the numbers of R-cycles and H-cycles per unit time. Further increasing the hot bath temperature above a certain threshold does not increase the machine's power output. The cause is that, in this situation, the N-cycle has a greater probability than the H-cycle and R-cycle. Although the SSDB machine operates by randomly switching between these three cycles, at the level of a single quantum jump trajectory, its heat engine efficiency and the refrigerator's coefficient of performance remain constant.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
