Artículos de revistas
Closed-loop separation control using machine learning
Fecha
2015-05Registro en:
Gautier, N.; Aider, J. L.; Duriez, Thomas Pierre Cornil; Noack, B. R.; Segond, M.; et al.; Closed-loop separation control using machine learning; Cambridge University Press; Journal of Fluid Mechanics; 770; 5-2015; 442-457
0022-1120
CONICET Digital
CONICET
Autor
Gautier, N.
Aider, J. L.
Duriez, Thomas Pierre Cornil
Noack, B. R.
Segond, M.
Abel, M.
Resumen
We present the first closed-loop separation control experiment using a novel, model-free strategy based on genetic programming, which we call 'machine learning control'. The goal is to reduce the recirculation zone of backward-facing step flow at Reh = 1350 manipulated by a slotted jet and optically sensed by online particle image velocimetry. The feedback control law is optimized with respect to a cost functional based on the recirculation area and a penalization of the actuation. This optimization is performed employing genetic programming. After 12 generations comprised of 500 individuals, the algorithm converges to a feedback law which reduces the recirculation zone by 80 %. This machine learning control is benchmarked against the best periodic forcing which excites Kelvin-Helmholtz vortices. The machine learning control yields a new actuation mechanism resonating with the low-frequency flapping mode instability. This feedback control performs similarly to periodic forcing at the design condition but outperforms periodic forcing when the Reynolds number is varied by a factor two. The current study indicates that machine learning control can effectively explore and optimize new feedback actuation mechanisms in numerous experimental applications.