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THOMAS DURIEZ - Feedback Control of Turbulent Shear Flows by Machine Learning
Laboratorio de FluidoDinámica, CONICET / Universidad de Buenos Aires - Facultad de
Jueves 7/4/2016, 14 hs.
Aula Seminario, 2do piso, Pabellón I.
We propose a novel closed-loop control strategy of turbulent flows using machine learning methods in a model-free manner. This strategy, called Machine Learning Control (MLC), allows -for the first time - to detect and exploit all enabling nonlinear actuation mechanisms in an unsupervised automatic manner.
In a first part of the talk we demonstrate how MLC is able to detect and exploit key nonlinearities of dynamical systems relevant to fluids mechanics. Then, we focus on MLC applications for in time control of experimental shear flows and demonstrate how it outperforms state-of-the-art control.
In particular, MLC is applied to three different experimental closed-loop control setups: (1) the TUCOROM mixing layer tunnel, (2) the Görtler PMMH water tunnel with a backward facing step, and (3) the LML Boundary Layer wind tunnel with a separating turbulent boundary layer. In all three cases, MLC finds a control which yields a significantly better performance with respect to the given cost functional as compared to the best previously tested open-loop actuation.
We foresee numerous potential applications to most nonlinear multiple-input multiple-output (MIMO) flow control problems, particularly in experiments. In particular, the model-free architecture of MLC enables its application to a large class of complex nonlinear systems in all areas of science.