Machine learning control for drag reduction of a car model in experiment

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

@InProceedings{Li:2017:IFAC,

• author = "Ruiying Li and Bernd R. Noack and Laurent Cordier and Jacques Boree and Fabien Harambat",
• title = "Machine learning control for drag reduction of a car model in experiment",
• booktitle = "20th IFAC World Congress",
• year = "2017",
• editor = "Dimitri Peaucelle",
• pages = "Paper ThP23.1",
• address = "Toulouse, France",
• month = jul # " 9-14",
• organisation = "International Federation of Automatic Control",
• keywords = "genetic algorithms, genetic programming, evolutionary algorithms, real-time control, machine learning control, drag reduction, car, physics, PHYS, MECA, MEFL, mechanics of the fluids",
• identifier = "hal-01856274",
• language = "en",
• oai = "oai:HAL:hal-01856274v1",
• type = "info:eu-repo/semantics/conferenceObject",
• URL = "https://hal.archives-ouvertes.fr/hal-01856274",
• URL = "http://www.ifac2017.org/sites/www.ifac2017.org/files/u88/IFAC17_ContentListWeb_4.html",
• abstract = "We investigate experimentally a novel model-free in-time control strategy, called Machine Learning Control (MLC), for aerodynamic drag reduction of a car model. Fluidic actuation is applied at the trailing edge of a blunt-edged Ahmed body combined with a curved deflection surface. The impact of actuation on the flow is monitored with base pressure sensors.Based on the idea of genetic programming, the applied model-free control strategy detects and exploits nonlinear actuation mechanisms in an unsupervised manner with the aim of minimising the drag. Key enabler is linear genetic programming as simple and efficient framework for multiple inputs (actuators) and multiple outputs (sensors). The optimised control laws comprise periodic forcing, multi-frequency forcing and sensor-based feedback control. Approximately 33 percent base pressure recovery associated with 22 percent drag reduction is achieved by the optimal control law for a turbulent flow at Reynolds number 300000 based on body height.",
• notes = "Author sometimes given as Ruying Li

  http://www.ifac2017.org/ oai:HAL:hal-01856274v1,

  Contributor : Limsi Publications ",

}

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

Citations