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

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@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