Reformulating DRL in a moving reference frame enables reliable control of rapid transitions between mode-locked states in a 1D RDE model by separating fast detonation propagation from slower operating-mode dynamics.
Closed-loop supersonic flow control with a high-speed experimental deep reinforcement learning framework.Journal of Fluid Mechanics, 1009:A3, April 2025
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
method 1
citation-polarity summary
fields
physics.flu-dyn 1years
2026 1verdicts
UNVERDICTED 1roles
method 1polarities
use method 1representative citing papers
citing papers explorer
-
Timescale Separation Enables Deep Reinforcement Learning Control of Rotating Detonation Engine Mode Transitions
Reformulating DRL in a moving reference frame enables reliable control of rapid transitions between mode-locked states in a 1D RDE model by separating fast detonation propagation from slower operating-mode dynamics.