Closed-loop separation control over a sharp edge ramp using genetic programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{Debien:2016:expF,
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author = "Antoine Debien and Kai A. F. F. {von Krbek} and
Nicolas Mazellier and Thomas Duriez and
Laurent Cordier and Bernd R. Noack and Markus W. Abel and
Azeddine Kourta",
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title = "Closed-loop separation control over a sharp edge ramp
using genetic programming",
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journal = "Experiments in Fluids",
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year = "2016",
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volume = "57",
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number = "3",
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keywords = "genetic algorithms, genetic programming, feedback flow
control, turbulent boundary layer, active vortex
generators, machine learning control",
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bibsource = "OAI-PMH server at export.arxiv.org",
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identifier = "doi:10.1007/s00348-016-2126-8",
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oai = "oai:arXiv.org:1508.05268",
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ISSN = "1432-1114",
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URL = "http://arxiv.org/abs/1508.05268",
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DOI = "doi:10.1007/s00348-016-2126-8",
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size = "19 pages",
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abstract = "We experimentally perform open and closed-loop control
of a separating turbulent boundary layer downstream
from a sharp edge ramp. The turbulent boundary layer
just above the separation point has a Reynolds number
{\$}{\$}Re{\_}{\{}{\backslash}theta
{\}}{\backslash}approx 3500{\$}{\$} R e $\theta$ approx
3500 based on momentum thickness. The goal of the
control is to mitigate separation and early
re-attachment. The forcing employs a spanwise array of
active vortex generators. The flow state is monitored
with skin-friction sensors downstream of the actuators.
The feedback control law is obtained using model-free
genetic programming control (GPC) (Gautier et al. in J
Fluid Mech 770:442--457, 2015). The resulting flow is
assessed using the momentum coefficient, pressure
distribution and skin friction over the ramp and stereo
PIV. The PIV yields vector field statistics, e.g. shear
layer growth, the back-flow area and vortex region. GPC
is benchmarked against the best periodic forcing. While
open-loop control achieves separation reduction by
locking-on the shedding mode, GPC gives rise to similar
benefits by accelerating the shear layer growth.
Moreover, GPC uses less actuation energy.",
- }
Genetic Programming entries for
Antoine Debien
Kai A F F von Krbek
Nicolas Mazellier
Thomas Duriez
Laurent Cordier
Bernd R Noack
Markus W Abel
Azeddine Kourta
Citations