Artificial intelligence control applied to drag reduction of the fluidic pinball

Created by W.Langdon from gp-bibliography.bib Revision:1.8081

@Article{Maceda:2019:GAMM,

• author = "Guy {Cornejo Maceda} and Bernd Noack and Francois Lusseyran and Nan Deng and Luc Pastur and Marek Morzynski",
• title = "Artificial intelligence control applied to drag reduction of the fluidic pinball",
• journal = "Proceedings in Applied Mathematics and Mechanics",
• year = "2019",
• pages = "e201900268",
• month = nov,
• note = "Special issue: 90th Annual Meeting of the International Association of Applied Mathematics and Mechanics (GAMM)",
• keywords = "genetic algorithms, genetic programming, computer science, systems and control, artificial intelligence, AI, engineering sciences, mechanics, fluids mechanics",
• publisher = "HAL CCSD; Wiley-VCH Verlag",
• URL = "https://hal.archives-ouvertes.fr/hal-02398649",
• URL = "https://hal.archives-ouvertes.fr/hal-02398649/file/2019_PAMM_CornejoMaceda_SUBMITTED.pdf",
• DOI = "doi:10.1002/pamm.201900268",
• ISSN = "1617-7061",
• abstract = "The aim of our work is to advance a self-learning, model-free control method to tame complex nonlinear flows-building on the pioneering work of Dracopoulous [1]. The cornerstone is the formulation of the control problem as a function optimisation problem. The control law is derived by solving a nonsmooth optimisation problem thanks to an artificial intelligence technique, genetic programming (GP). Metaparameters optimisation of the algorithm and complexity penalization have been our main contribution and have been tested on a cluster of three equidistant cylinders immersed in a incoming flow, the fluidic pinball. The means of control is the independent rotation of the cylinders. GP derived a control law associated to each cylinder in order to minimise the net drag power and managed to outperform past open-loop studies with a 46.0 percent net drag power reduction by combining two strategies from literature. This success of MIMO control including sensor history is promising for exploring even more complex dynamics.",
• annote = "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); Poznan University of Technology",
• bibsource = "OAI-PMH server at api.archives-ouvertes.fr",
• contributor = "Laboratoire d'Informatique pour la Mecanique et les Sciences de l'Ingenieur",
• description = "International audience",
• identifier = "hal-02398649; DOI: 10.1002/pamm.201900268",
• language = "en",
• oai = "oai:HAL:hal-02398649v1",
• relation = "info:eu-repo/semantics/altIdentifier/doi/10.1002/pamm.201900268",
• rights = "info:eu-repo/semantics/OpenAccess",

}

Genetic Programming entries for Guy Yoslan Cornejo Maceda Bernd R Noack Francois Lusseyran Nan Deng Luc Pastur Marek Morzynski

Citations