Drag reduction of a car model by linear genetic programming control

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

@Article{Li:2017:expfluids,
  author =       "Ruiying Li and Bernd R. Noack and Laurent Cordier and 
                 Jacques Boree and Fabien Harambat",
  title =        "Drag reduction of a car model by linear genetic
                 programming control",
  journal =      "Experiments in Fluids",
  year =         "2017",
  volume =       "58",
  number =       "8",
  pages =        "103",
  month =        aug,
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "1432-1114",
  DOI =          "doi:10.1007/s00348-017-2382-2",
  size =         "20 pages",
  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 based on body height. The actuation is
                 performed with pulsed jets at all trailing edges
                 (multiple inputs) combined with a Coanda deflection
                 surface. The flow is monitored with 16 pressure sensors
                 distributed at the rear side (multiple outputs). We
                 apply a recently developed model-free control strategy
                 building on genetic programming in Dracopoulos and Kent
                 (Neural Comput Appl 6:214--228, 1997) and Gautier et
                 al. (J Fluid Mech 770:424--441, 2015). The optimized
                 control laws comprise periodic forcing, multi-frequency
                 forcing and sensor-based feedback including also
                 time-history information feedback and combinations
                 thereof. Key enabler is linear genetic programming
                 (LGP) as powerful regression technique for optimizing
                 the multiple-input multiple-output control laws. The
                 proposed LGP 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. In addition, the control identified
                 automatically the only sensor which listens to
                 high-frequency flow components with good signal to
                 noise ratio. Our control strategy is, in principle,
                 applicable to all multiple actuators and sensors
                 experiments.",
  notes =        "Does not have real page numbers, treat 103 as an
                 article id?",
}

Genetic Programming entries for Ruiying Li Bernd R Noack Laurent Cordier Jacques Boree Fabien Harambat

Citations