Drag reduction of a car model by linear genetic programming control

Created by W.Langdon from gp-bibliography.bib Revision:1.8081

@Misc{oai:arXiv.org:1609.02505,

• 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

Citations