- author = "Ruying Li and Bernd R. Noack and Laurent Cordier and Jacques Boree and Fabien Harambat",
- title = "Machine learning control of the turbulent wake past {3D} bluff body",
- booktitle = "3AF International Conference on Applied Aerodynamics, AAAF",
- year = "2017",
- address = "Ecole Centrale de Lyon, France",
- month = mar # " 27-29",
- organisation = "French Aeronautics and Space Society 3AF",
- keywords = "genetic algorithms, genetic programming, machine learning control, drag reduction, car, physics, mechanics of the fluids",
- bibsource = "OAI-PMH server at api.archives-ouvertes.fr",
- contributor = "Laboratoire d'Informatique pour la M{\'e}canique et les Sciences de l'Ing{\'e}nieur and AAAF and AAAF",
- identifier = "hal-01856273",
- language = "en",
- oai = "oai:HAL:hal-01856273v1",
-
URL = "
https://hal.archives-ouvertes.fr/hal-01856273",
- abstract = "We investigate experimentally a novel model-free control strategy, called Machine Learning Control (MLC), for aerodynamic drag reduction of a 3D bluff body. Fluidic actuation is applied at the blunt trailing edge of the body combined with a curved deflection surface. The impact of actuation on the flow is monitored with base pressure sensors. 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 systems with multiple inputs (actuators) and multiple outputs (sensors). The ansatz of control laws include periodic forcing, multi-frequency forcing and sensor-based feedback control. Approximately 33percent base pressure recovery is achieved by the optimal control law for a turbulent flow at Re_H {$\approx$} 3 {$\times$} 10 5 based on body height.",
-
notes = "http://3af-aerodynamics2017.com/
oai:HAL:hal-01856273v1 Also known as
\cite{li:hal-01856273}
Contributor : Limsi Publications
