Unsteady actuation and feedback control of the experimental fluidic pinball using genetic programming

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

@Article{Raibaudo:2021:EiF,

• author = "C. Raibaudo and R. Martinuzzi",
• title = "Unsteady actuation and feedback control of the experimental fluidic pinball using genetic programming",
• journal = "Experiments in Fluids",
• year = "2021",
• volume = "62",
• number = "219",
• month = oct,
• keywords = "genetic algorithms, genetic programming, engineering sciences, mechanics, fluids mechanics, statistics, machine learning, optimisation and control",
• ISSN = "0723-4864",
• bibsource = "OAI-PMH server at api.archives-ouvertes.fr",
• language = "en",
• oai = "oai:HAL:hal-03371788v1",
• URL = "https://hal-univ-orleans.archives-ouvertes.fr/hal-03371788",
• DOI = "doi:10.1007/s00348-021-03309-1",
• abstract = "The application of genetic programming for closed-loop wake stabilization downstream of a triangular cluster of three rotating cylinders, referred to as the fluidic pinball, is investigated experimentally. The implementation of unsteady actuation for control is considered and the benefits over steady control Raibaudo (Phys Fluids 32:015108, 2020) discussed. Experiments are performed at Reynolds number Re approx 2200. Two-component planar PIV measurements and hot-wire anemometry are used to characterise the wake with and without actuation. Each cylinder is controlled independently, and the rotation speed is sinusoidally modulated. Linear genetic programming is implemented for the optimisation of feedback controllers. Two objectives are considered: drag reduction or wake symmetrization, for which two cost functions J are defined. Open-loop control using sinusoidal modulation is performed to study the efficiency of unsteady actuation compared to constant rotation speeds. Genetic programming is shown to be more efficient than traditional methods for optimisation of a large number of control parameters. For the fluidic pinball, optimal solutions are found to be more robust when compared to open-loop genetic algorithms.",
• notes = "Department of Mechanical and Manufacturing Engineering, Schulich School of Engineering, University of Calgary, Calgary, Canada",

}

Genetic Programming entries for Cedric Raibaudo Robert Martinuzzi

Citations