Closed-loop separation control using machine learning
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{Gautier:2015:FLM,
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author = "N. Gautier and J.-L. Aider and T. Duriez and
B. R. Noack and M. Segond and M. Abel",
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title = "Closed-loop separation control using machine
learning",
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journal = "Journal of Fluid Mechanics",
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volume = "770",
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month = "5",
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year = "2015",
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ISSN = "1469-7645",
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pages = "442--457",
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oai = "oai:arXiv.org:1405.0908",
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keywords = "genetic algorithms, genetic programming, control
theory, flow control, separated flows, physics - fluid
dynamics",
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URL = "http://arxiv.org/abs/1405.0908",
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URL = "http://journals.cambridge.org/article_S0022112015000956",
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DOI = "doi:10.1017/jfm.2015.95",
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size = "16 pages",
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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