Feedback Control of Turbulent Shear Flows by Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Misc{oai:arXiv.org:1505.01022,
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title = "Feedback Control of Turbulent Shear Flows by Genetic
Programming",
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author = "Thomas Duriez and Vladimir Parezanovic and
Kai {von Krbek} and Jean-Paul Bonnet and Laurent Cordier and
Bernd R. Noack and Marc Segond and Markus Abel and
Nicolas Gautier and Jean-Luc Aider and
Cedric Raibaudo and Christophe Cuvier and Michel Stanislas and
Antoine Debien and Nicolas Mazellier and Azeddine Kourta and
Steven L. Brunton",
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year = "2015",
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month = may # "~05",
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note = "Comment: 49 pages, many figures, submitted to Phys Rev
E",
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keywords = "genetic algorithms, genetic programming, physics -
fluid dynamics",
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bibsource = "OAI-PMH server at export.arxiv.org",
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oai = "oai:arXiv.org:1505.01022",
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URL = "http://arxiv.org/abs/1505.01022",
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abstract = "Turbulent shear flows have triggered fundamental
research in nonlinear dynamics, like transition
scenarios, pattern formation and dynamical modelling.
In particular, the control of nonlinear dynamics is
subject of research since decades. In this publication,
actuated turbulent shear flows serve as test-bed for a
nonlinear feedback control strategy which can optimise
an arbitrary cost function in an automatic
self-learning manner. This is facilitated by genetic
programming providing an analytically treatable control
law. Unlike control based on PID laws or neural
networks, no structure of the control law needs to be
specified in advance. The strategy is first applied to
low-dimensional dynamical systems featuring aspects of
turbulence and for which linear control methods fail.
This includes stabilising an unstable fixed point of a
nonlinearly coupled oscillator model and maximising
mixing, i.e. the Lyapunov exponent, for forced Lorenz
equations. For the first time, we demonstrate the
applicability of genetic programming control to four
shear flow experiments with strong nonlinearities and
intrinsically noisy measurements. These experiments
comprise mixing enhancement in a turbulent shear layer,
the reduction of the recirculation zone behind a
backward facing step, and the optimised reattachment of
separating boundary layers. Genetic programming control
has outperformed tested optimised state-of-the-art
control and has even found novel actuation
mechanisms.",
- }
Genetic Programming entries for
Thomas Duriez
Vladimir Parezanovic
Kai A F F von Krbek
Jean-Paul Bonnet
Laurent Cordier
Bernd R Noack
Marc Segond
Markus W Abel
Nicolas Gautier
Jean-Luc Aider
Cedric Raibaudo
Christophe Cuvier
Michel Stanislas
Antoine Debien
Nicolas Mazellier
Azeddine Kourta
Steven L Brunton
Citations