Closed-loop separation control over a sharp edge ramp using genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@Article{Debien:2016:expF,

• author = "Antoine Debien and Kai A. F. F. {von Krbek} and Nicolas Mazellier and Thomas Duriez and Laurent Cordier and Bernd R. Noack and Markus W. Abel and Azeddine Kourta",
• title = "Closed-loop separation control over a sharp edge ramp using genetic programming",
• journal = "Experiments in Fluids",
• year = "2016",
• volume = "57",
• number = "3",
• keywords = "genetic algorithms, genetic programming, feedback flow control, turbulent boundary layer, active vortex generators, machine learning control",
• bibsource = "OAI-PMH server at export.arxiv.org",
• identifier = "doi:10.1007/s00348-016-2126-8",
• oai = "oai:arXiv.org:1508.05268",
• ISSN = "1432-1114",
• URL = "http://arxiv.org/abs/1508.05268",
• DOI = "doi:10.1007/s00348-016-2126-8",
• size = "19 pages",
• abstract = "We experimentally perform open and closed-loop control of a separating turbulent boundary layer downstream from a sharp edge ramp. The turbulent boundary layer just above the separation point has a Reynolds number {\$}{\$}Re{\_}{\{}{\backslash}theta {\}}{\backslash}approx 3500{\$}{\$} R e $\theta$ approx 3500 based on momentum thickness. The goal of the control is to mitigate separation and early re-attachment. The forcing employs a spanwise array of active vortex generators. The flow state is monitored with skin-friction sensors downstream of the actuators. The feedback control law is obtained using model-free genetic programming control (GPC) (Gautier et al. in J Fluid Mech 770:442--457, 2015). The resulting flow is assessed using the momentum coefficient, pressure distribution and skin friction over the ramp and stereo PIV. The PIV yields vector field statistics, e.g. shear layer growth, the back-flow area and vortex region. GPC is benchmarked against the best periodic forcing. While open-loop control achieves separation reduction by locking-on the shedding mode, GPC gives rise to similar benefits by accelerating the shear layer growth. Moreover, GPC uses less actuation energy.",

}

Genetic Programming entries for Antoine Debien Kai A F F von Krbek Nicolas Mazellier Thomas Duriez Laurent Cordier Bernd R Noack Markus W Abel Azeddine Kourta

Citations