TY  - JOUR
A1  - Kaiser, Eurika
A1  - Noack, Bernd R.
A1  - Cordier, Laurent
A1  - Spohn, Andreas
A1  - Segond, Marc
A1  - Abel, Markus
A1  - Daviller, Guillaume
A1  - Osth, Jan
A1  - Krajnovic, Sinisa
A1  - Niven, Robert K.
T1  - Cluster-based reduced-order modelling of a mixing layer
JF  - Journal of fluid mechanics
KW  - low-dimensional models
KW  - nonlinear dynamical systems
KW  - shear layers
Y1  - 2014
U6  - https://doi.org/10.1017/jfm.2014.355
SN  - 0022-1120
SN  - 1469-7645
VL  - 754
SP  - 365
EP  - 414
PB  - Cambridge Univ. Press
CY  - New York
ER  - 
TY  - JOUR
A1  - Parezanovic, Vladimir
A1  - Cordier, Laurent
A1  - Spohn, Andreas
A1  - Duriez, Thomas
A1  - Noack, Bernd R.
A1  - Bonnet, Jean-Paul
A1  - Segond, Marc
A1  - Abel, Markus
A1  - Brunton, Steven L.
T1  - Frequency selection by feedback control in a turbulent shear flow
JF  - Journal of fluid mechanics
N2  - Many previous studies have shown that the turbulent mixing layer under periodic forcing tends to adopt a lock-on state, where the major portion of the fluctuations in the flow are synchronized at the forcing frequency. The goal of this experimental study is to apply closed-loop control in order to provoke the lock-on state, using information from the flow itself. We aim to determine the range of frequencies for which the closed-loop control can establish the lock-on, and what mechanisms are contributing to the selection of a feedback frequency. In order to expand the solution space for optimal closed-loop control laws, we use the genetic programming control (CPC) framework. The best closed-loop control laws obtained by CPC are analysed along with the associated physical mechanisms in the mixing layer flow. The resulting closed-loop control significantly outperforms open-loop forcing in terms of robustness to changes in the free-stream velocities. In addition, the selection of feedback frequencies is not locked to the most amplified local mode, but rather a range of frequencies around it.
KW  - free shear layers
KW  - instability control
KW  - turbulence control
Y1  - 2016
U6  - https://doi.org/10.1017/jfm.2016.261
SN  - 0022-1120
SN  - 1469-7645
VL  - 797
SP  - 247
EP  - 283
PB  - Cambridge Univ. Press
CY  - New York
ER  - 
TY  - JOUR
A1  - Parezanovic, Vladimir
A1  - Laurentie, Jean-Charles
A1  - Fourment, Carine
A1  - Delville, Joel
A1  - Bonnet, Jean-Paul
A1  - Spohn, Andreas
A1  - Duriez, Thomas
A1  - Cordier, Laurent
A1  - Noack, Bernd R.
A1  - Abel, Markus
A1  - Segond, Marc
A1  - Shaqarin, Tamir
A1  - Brunton, Steven L.
T1  - Mixing layer manipulation experiment from open-loop forcing to closed-loop machine learning control
JF  - Flow, turbulence and combustion : an international journal published in association with ERCOFTAC
KW  - Shear flow
KW  - Turbulence
KW  - Active flow control
KW  - Extremum seeking
KW  - POD
KW  - Machine learning
KW  - Genetic programming
Y1  - 2015
U6  - https://doi.org/10.1007/s10494-014-9581-1
SN  - 1386-6184
SN  - 1573-1987
VL  - 94
IS  - 1
SP  - 155
EP  - 173
PB  - Springer
CY  - Dordrecht
ER  - 
TY  - JOUR
A1  - Quade, Markus
A1  - Abel, Markus
A1  - Shafi, Kamran
A1  - Niven, Robert K.
A1  - Noack, Bernd R.
T1  - Prediction of dynamical systems by symbolic regression
JF  - Physical review : E, Statistical, nonlinear and soft matter physics
N2  - We study the modeling and prediction of dynamical systems based on conventional models derived from measurements. Such algorithms are highly desirable in situations where the underlying dynamics are hard to model from physical principles or simplified models need to be found. We focus on symbolic regression methods as a part of machine learning. These algorithms are capable of learning an analytically tractable model from data, a highly valuable property. Symbolic regression methods can be considered as generalized regression methods. We investigate two particular algorithms, the so-called fast function extraction which is a generalized linear regression algorithm, and genetic programming which is a very general method. Both are able to combine functions in a certain way such that a good model for the prediction of the temporal evolution of a dynamical system can be identified. We illustrate the algorithms by finding a prediction for the evolution of a harmonic oscillator based on measurements, by detecting an arriving front in an excitable system, and as a real-world application, the prediction of solar power production based on energy production observations at a given site together with the weather forecast.
Y1  - 2016
U6  - https://doi.org/10.1103/PhysRevE.94.012214
SN  - 2470-0045
SN  - 2470-0053
VL  - 94
PB  - American Society for Pharmacology and Experimental Therapeutics
CY  - Bethesda
ER  -