Journal
JOURNAL OF FLUID MECHANICS
Volume 770, Issue -, Pages 442-457Publisher
CAMBRIDGE UNIV PRESS
DOI: 10.1017/jfm.2015.95
Keywords
control theory; flow control; separated flows
Categories
Funding
- DGA
- French ANR (Chaire d'Excellence TUCOROM)
- LINC project - ECs Marie-Curie ITN program (FP7-PEOPLE-ITN) [289447]
- French ANR (SEPACODE)
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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 Re-h = 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.
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