Artificial intelligence control of a turbulent jet

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Fan:2018:afmc,

• author = "Dewei Fan and Yu Zhou and Bernd Noack",
• title = "Artificial intelligence control of a turbulent jet",
• booktitle = "Proceedings of the 21st Australasian Fluid Mechanics Conference",
• address = "Adelaide, Australia",
• editor = "T. C. W. Lau and R. M. Kelso",
• publisher = "HAL CCSD",
• year = "2018",
• month = "10-13 " # dec,
• keywords = "genetic algorithms, genetic programming, engineering sciences, physics, mechanics, fluids mechanics",
• type = "info:eu-repo/semantics/conferenceObject",
• isbn13 = "978-0-646-59784-3",
• URL = "https://hal.archives-ouvertes.fr/hal-02398705",
• URL = "https://people.eng.unimelb.edu.au/imarusic/proceedings/21/Contribution_615_final.pdf",
• size = "4 pages",
• 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.",
• 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)",
• bibsource = "OAI-PMH server at api.archives-ouvertes.fr",
• contributor = "Laboratoire d'Informatique pour la Mecanique et les Sciences de l'Ingenieur",
• coverage = "Adelaide, Australia",
• description = "International audience",
• identifier = "hal-02398705",
• language = "en",
• oai = "oai:HAL:hal-02398705v1",
• 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