Drag reduction of a car model by linear genetic programming control

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

@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