Drag reduction of a car model by linear genetic programming control

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

  author =       "Ruiying Li and Bernd R. Noack and Laurent Cordier and 
                 Jacques Boree and Fabien Harambat and Eurika Kaiser and 
                 Thomas Duriez",
  title =        "Drag reduction of a car model by linear genetic
                 programming control",
  note =         "Comment: 39 pages, 23 figures",
  year =         "2016",
  month =        sep # "~08",
  keywords =     "genetic algorithms, genetic programming, physics -
                 fluid dynamics",
  bibsource =    "OAI-PMH server at export.arxiv.org",
  oai =          "oai:arXiv.org:1609.02505",
  URL =          "http://arxiv.org/abs/1609.02505",
  abstract =     "We investigate open- and closed-loop active control
                 for aerodynamic drag reduction of a car model.
                 Turbulent flow around a blunt-edged Ahmed body is
                 examined at $Re_{H}\approx3\times10^{5}$ based on body
                 height. The actuation is performed with pulsed jets at
                 all trailing edges combined with a Coanda deflection
                 surface. The flow is monitored with pressure sensors
                 distributed at the rear side. We apply a model-free
                 control strategy building on Dracopoulos \& Kent
                 (Neural Comput. \& Applic., vol. 6, 1997, pp. 214-228)
                 and Gautier et al. (J. Fluid Mech., vol. 770, 2015, pp.
                 442-457). The optimised control laws comprise periodic
                 forcing, multi-frequency forcing and sensor-based
                 feedback including also time-history information
                 feedback and combination thereof. Key enabler is linear
                 genetic programming as simple and efficient framework
                 for multiple inputs (actuators) and multiple outputs
                 (sensors). The proposed linear genetic programming
                 control can select the best open- or closed-loop
                 control in an unsupervised manner. Approximately
                 33percent base pressure recovery associated with
                 22percent drag reduction is achieved in all considered
                 classes of control laws. Intriguingly, the feedback
                 actuation emulates periodic high-frequency forcing by
                 selecting one pressure sensor in the optimal control
                 law. Our control strategy is, in principle, applicable
                 to all multiple actuators and sensors experiments.",
  notes =        "see \cite{Li:2017:expfluids}",

Genetic Programming entries for Ruiying Li Bernd R Noack Laurent Cordier Jacques Boree Fabien Harambat Eurika Kaiser Thomas Duriez