Feedback Control of Turbulent Shear Flows by Genetic Programming

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

@Misc{oai:arXiv.org:1505.01022,

• title = "Feedback Control of Turbulent Shear Flows by Genetic Programming",
• author = "Thomas Duriez and Vladimir Parezanovic and Kai {von Krbek} and Jean-Paul Bonnet and Laurent Cordier and Bernd R. Noack and Marc Segond and Markus Abel and Nicolas Gautier and Jean-Luc Aider and Cedric Raibaudo and Christophe Cuvier and Michel Stanislas and Antoine Debien and Nicolas Mazellier and Azeddine Kourta and Steven L. Brunton",
• year = "2015",
• month = may # "~05",
• note = "Comment: 49 pages, many figures, submitted to Phys Rev E",
• keywords = "genetic algorithms, genetic programming, physics - fluid dynamics",
• bibsource = "OAI-PMH server at export.arxiv.org",
• oai = "oai:arXiv.org:1505.01022",
• URL = "http://arxiv.org/abs/1505.01022",
• abstract = "Turbulent shear flows have triggered fundamental research in nonlinear dynamics, like transition scenarios, pattern formation and dynamical modelling. In particular, the control of nonlinear dynamics is subject of research since decades. In this publication, actuated turbulent shear flows serve as test-bed for a nonlinear feedback control strategy which can optimise an arbitrary cost function in an automatic self-learning manner. This is facilitated by genetic programming providing an analytically treatable control law. Unlike control based on PID laws or neural networks, no structure of the control law needs to be specified in advance. The strategy is first applied to low-dimensional dynamical systems featuring aspects of turbulence and for which linear control methods fail. This includes stabilising an unstable fixed point of a nonlinearly coupled oscillator model and maximising mixing, i.e. the Lyapunov exponent, for forced Lorenz equations. For the first time, we demonstrate the applicability of genetic programming control to four shear flow experiments with strong nonlinearities and intrinsically noisy measurements. These experiments comprise mixing enhancement in a turbulent shear layer, the reduction of the recirculation zone behind a backward facing step, and the optimised reattachment of separating boundary layers. Genetic programming control has outperformed tested optimised state-of-the-art control and has even found novel actuation mechanisms.",

}

Genetic Programming entries for Thomas Duriez Vladimir Parezanovic Kai A F F von Krbek Jean-Paul Bonnet Laurent Cordier Bernd R Noack Marc Segond Markus W Abel Nicolas Gautier Jean-Luc Aider Cedric Raibaudo Christophe Cuvier Michel Stanislas Antoine Debien Nicolas Mazellier Azeddine Kourta Steven L Brunton

Citations