Closed-loop separation control using machine learning

Created by W.Langdon from gp-bibliography.bib Revision:1.8010

@Article{Gautier:2015:FLM,

• author = "N. Gautier and J.-L. Aider and T. Duriez and B. R. Noack and M. Segond and M. Abel",
• title = "Closed-loop separation control using machine learning",
• journal = "Journal of Fluid Mechanics",
• volume = "770",
• month = "5",
• year = "2015",
• ISSN = "1469-7645",
• pages = "442--457",
• oai = "oai:arXiv.org:1405.0908",
• keywords = "genetic algorithms, genetic programming, control theory, flow control, separated flows, physics - fluid dynamics",
• URL = "http://arxiv.org/abs/1405.0908",
• URL = "http://journals.cambridge.org/article_S0022112015000956",
• DOI = "doi:10.1017/jfm.2015.95",
• size = "16 pages",
• abstract = "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 optimised with respect to a cost functional based on the recirculation area and a penalization of the actuation. This optimisation 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 percent. 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 optimise new feedback actuation mechanisms in numerous experimental applications.",

}

Genetic Programming entries for Nicolas Gautier Jean-Luc Aider Thomas Duriez Bernd R Noack Marc Segond Markus W Abel

Citations