arXiv：cs.LG· Kristian Holme, Jean Rabault, Ricardo Vinuesa, Mikael Mortensen·· 5 小时前AI 评分36
时间尺度分离实现旋转爆震发动机模态转换的深度强化学习控制
Timescale Separation Enables Deep Reinforcement Learning Control of Rotating Detonation Engine Mode Transitions
AI 导读
研究者通过将深度强化学习问题重构到跟随爆震波模式的移动参考系中，使波结构对智能体呈现准稳态，从而实现快速爆震传播与较慢工作模态动力学之间的尺度分离。在一维降阶RDE模型中，通过调节空间分段喷射压力，移动参考系训练的控制器比静止参考系更可靠，且有效激励周期范围更广。结果表明，对称性感知的移动参考系公式或可推广至相关多尺度流动控制问题。
正文
Abstract:Rotating detonation engines (RDEs) are a promising propulsion concept that may offer higher thermodynamic efficiency and specific impulse than conventional systems, but nonlinear phenomena, including transitions to oscillatory or chaotic propagation modes, can hinder practical operation. Deep Reinforcement Learning (DRL) has emerged as a promising method for controlling complex nonlinear dynamics such as those observed in RDEs. However, the multi-timescale nature of the RDE system makes direct application of DRL challenging. We address this challenge by reformulating the DRL problem in a moving reference frame that follows the detonation-wave pattern, making the wave structure appear quasi-steady to the agent. This reformulation enables scale separation between fast detonation propagation and slower operating-mode dynamics. We train DRL controllers to modulate spatially segmented injection pressure in a one-dimensional reduced-order RDE model and induce rapid transitions between different mode-locked states. Across a range of actuation periods, initial states, and target modes, controllers trained in the moving frame learn more reliably than those trained in a stationary frame and remain effective over a broader range of actuation periods. These results suggest that symmetry-aware moving reference frame formulations may be useful for related multiscale flow-control problems and that scale separation should be exploited whenever possible to enable DRL control of multi-timescale systems.
| Subjects: | Fluid Dynamics (physics.flu-dyn); Machine Learning (cs.LG) |
| Cite as: | arXiv:2604.14398 [physics.flu-dyn] |
| (or arXiv:2604.14398v2 [physics.flu-dyn] for this version) | |
| https://doi.org/10.48550/arXiv.2604.14398 arXiv-issued DOI via DataCite |
Submission history
From: Kristian Holme [view email]
[v1]
Wed, 15 Apr 2026 20:27:56 UTC (2,681 KB)
[v2]
Fri, 2 Oct 2026 07:49:25 UTC (3,793 KB)
来源：arXiv：cs.LG · arxiv.org