Dokument-ID Dokumenttyp Verfasser/Autoren Herausgeber Haupttitel Abstract Auflage Verlagsort Verlag Erscheinungsjahr Seitenzahl Schriftenreihe Titel Schriftenreihe Bandzahl ISBN Quelle der Hochschulschrift Konferenzname Quelle:Titel Quelle:Jahrgang Quelle:Heftnummer Quelle:Erste Seite Quelle:Letzte Seite URN DOI Abteilungen OPUS4-37607 Wissenschaftlicher Artikel Kaiser, Eurika; Noack, Bernd R.; Cordier, Laurent; Spohn, Andreas; Segond, Marc; Abel, Markus; Daviller, Guillaume; Osth, Jan; Krajnovic, Sinisa; Niven, Robert K. Cluster-based reduced-order modelling of a mixing layer New York Cambridge Univ. Press 2014 50 Journal of fluid mechanics 754 365 414 10.1017/jfm.2014.355 Institut für Physik und Astronomie OPUS4-45145 Wissenschaftlicher Artikel Quade, Markus; Abel, Markus; Shafi, Kamran; Niven, Robert K.; Noack, Bernd R. Prediction of dynamical systems by symbolic regression 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. Bethesda American Society for Pharmacology and Experimental Therapeutics 2016 15 Physical review : E, Statistical, nonlinear and soft matter physics 94 10.1103/PhysRevE.94.012214 Institut für Physik und Astronomie OPUS4-45311 Wissenschaftlicher Artikel Parezanovic, Vladimir; Cordier, Laurent; Spohn, Andreas; Duriez, Thomas; Noack, Bernd R.; Bonnet, Jean-Paul; Segond, Marc; Abel, Markus; Brunton, Steven L. Frequency selection by feedback control in a turbulent shear flow 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. New York Cambridge Univ. Press 2016 37 Journal of fluid mechanics 797 247 283 10.1017/jfm.2016.261 Institut für Physik und Astronomie OPUS4-39413 Wissenschaftlicher Artikel Parezanovic, Vladimir; Laurentie, Jean-Charles; Fourment, Carine; Delville, Joel; Bonnet, Jean-Paul; Spohn, Andreas; Duriez, Thomas; Cordier, Laurent; Noack, Bernd R.; Abel, Markus; Segond, Marc; Shaqarin, Tamir; Brunton, Steven L. Mixing layer manipulation experiment from open-loop forcing to closed-loop machine learning control Dordrecht Springer 2015 19 Flow, turbulence and combustion : an international journal published in association with ERCOFTAC 94 1 155 173 10.1007/s10494-014-9581-1 Institut für Physik und Astronomie OPUS4-41369 misc Parezanović, Vladimir; Cordier, Laurent; Spohn, Andreas; Duriez, Thomas; Noack, Bernd R.; Bonnet, Jean-Paul; Segond, Marc; Abel, Markus; Brunton, Steven L. Frequency selection by feedback control in a turbulent shear flow 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. 2016 37 Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe 572 urn:nbn:de:kobv:517-opus4-413693 10.25932/publishup-41369 Mathematisch-Naturwissenschaftliche Fakultät OPUS4-41611 misc Kaiser, Eurika; Noack, Bernd R.; Cordier, Laurent; Spohn, Andreas; Segond, Marc; Abel, Markus; Daviller, Guillaume; Osth, Jan; Krajnovic, Sinisa; Niven, Robert K. Cluster-based reduced-order modelling of a mixing layer We propose a novel cluster-based reduced-order modelling (CROM) strategy for unsteady flows. CROM combines the cluster analysis pioneered in Gunzburger's group (Burkardt, Gunzburger & Lee, Comput. Meth. Appl. Mech. Engng, vol. 196, 2006a, pp. 337-355) and transition matrix models introduced in fluid dynamics in Eckhardt's group (Schneider, Eckhardt & Vollmer, Phys. Rev. E, vol. 75, 2007, art. 066313). CROM constitutes a potential alternative to POD models and generalises the Ulam-Galerkin method classically used in dynamical systems to determine a finite-rank approximation of the Perron-Frobenius operator. The proposed strategy processes a time-resolved sequence of flow snapshots in two steps. First, the snapshot data are clustered into a small number of representative states, called centroids, in the state space. These centroids partition the state space in complementary non-overlapping regions (centroidal Voronoi cells). Departing from the standard algorithm, the probabilities of the clusters are determined, and the states are sorted by analysis of the transition matrix. Second, the transitions between the states are dynamically modelled using a Markov process. Physical mechanisms are then distilled by a refined analysis of the Markov process, e. g. using finite-time Lyapunov exponent (FTLE) and entropic methods. This CROM framework is applied to the Lorenz attractor (as illustrative example), to velocity fields of the spatially evolving incompressible mixing layer and the three-dimensional turbulent wake of a bluff body. For these examples, CROM is shown to identify non-trivial quasi-attractors and transition processes in an unsupervised manner. CROM has numerous potential applications for the systematic identification of physical mechanisms of complex dynamics, for comparison of flow evolution models, for the identification of precursors to desirable and undesirable events, and for flow control applications exploiting nonlinear actuation dynamics. 2014 50 Postprints der Universität Potsdam : Mathematisch Naturwissenschaftliche Reihe 605 365 414 urn:nbn:de:kobv:517-opus4-416113 10.25932/publishup-41611 Mathematisch-Naturwissenschaftliche Fakultät