Artificial intelligence control of a turbulent jet
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Fan:2018:afmc,
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author = "Dewei Fan and Yu Zhou and Bernd Noack",
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title = "Artificial intelligence control of a turbulent jet",
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booktitle = "Proceedings of the 21st Australasian Fluid Mechanics
Conference",
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address = "Adelaide, Australia",
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editor = "T. C. W. Lau and R. M. Kelso",
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publisher = "HAL CCSD",
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year = "2018",
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month = "10-13 " # dec,
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keywords = "genetic algorithms, genetic programming, engineering
sciences, physics, mechanics, fluids mechanics",
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type = "info:eu-repo/semantics/conferenceObject",
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isbn13 = "978-0-646-59784-3",
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URL = "https://hal.archives-ouvertes.fr/hal-02398705",
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URL = "https://people.eng.unimelb.edu.au/imarusic/proceedings/21/Contribution_615_final.pdf",
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size = "4 pages",
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abstract = "An artificial intelligence (AI) control system is
developed to manipulate a turbulent jet with a view to
maximising its mixing. The system consists of sensors
(two hot-wires), genetic programming for learning/
evolving and execution mechanism (6 unsteady radial
minijets). Mixing performance is quantified by the jet
centerline mean velocity. AI control discovers a
hitherto unexplored combination of flapping and helical
forcings. Such a combination of several actuation
mechanisms-if not creating new ones-is practically
inaccessible to conventional methods like a systematic
parametric analysis and gradient search, and vastly
outperforms the optimised periodic axisymmetric,
helical or flapping forcing produced from conventional
open-or closed-loop controls. Intriguingly, the
learning process of AI control discovers all these
forcings in the order of increased performance. The AI
control has dismissed sensor feedback and
multi-frequency components for optimisation. Our study
is the first highly successful AI control experiment
for a non-trivial spatially distributed actuation of a
turbulent flow. The results show the great potential of
AI in conquering the vast opportunity space of control
laws for many actuators and sensors and manipulating
turbulence.",
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annote = "Shenzhen Graduate School; Harbin Institute of
Technology; The Hong Kong Polytechnic University [Hong
Kong] (POLYU); Laboratoire d'Informatique pour la
Mecanique et les Sciences de l'Ingenieur (LIMSI) ;
Universite Paris Saclay (COmUE)-Centre National de la
Recherche Scientifique (CNRS)-Sorbonne Universite - UFR
d'Ingenierie (UFR 919) ; Sorbonne Universite
(SU)-Sorbonne Universite (SU)-Universite
Paris-Saclay-Universite Paris-Sud - Paris 11 (UP11)",
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bibsource = "OAI-PMH server at api.archives-ouvertes.fr",
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contributor = "Laboratoire d'Informatique pour la Mecanique et les
Sciences de l'Ingenieur",
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coverage = "Adelaide, Australia",
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description = "International audience",
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identifier = "hal-02398705",
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language = "en",
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oai = "oai:HAL:hal-02398705v1",
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notes = "https://people.eng.unimelb.edu.au/imarusic/proceedings/21%20AFMC%20TOC.html",
- }
Genetic Programming entries for
Dewei Fan
Yu Zhou
Bernd R Noack
Citations