Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/7143
Title: Nonlinear estimation of transient flow field low dimensional states using artificial neural nets
Authors: Cohen, Kelly
Siegel, Stefan
Seidel, Juergen
Aradağ, Selin
McLaughlin, Thomas
Keywords: Turbulent cylinder wake
ANNE
Low dimensional modeling
DPOD
Flow control
Issue Date: 2012
Publisher: Pergamon-Elsevier Science Ltd
Abstract: Feedback flow control of the wake of a circular cylinder at a Reynolds number of 100 is an interesting and challenging benchmark for controlling absolute instabilities associated with bluff body wakes. A two dimensional computational fluid dynamics simulation is used to develop low-dimensional models for estimator design. Actuation is implemented as displacement of the cylinder normal to the flow. The estimation approach uses a low dimensional model based on a truncated 6 mode Double Proper Orthogonal Decomposition (DPOD) applied to the streamwise velocity component of the flow field. Sensor placement is based on the intensity of the resulting spatial modes. A non-linear Artificial Neural Network Estimator (ANNE) was employed to map the velocity data to the mode amplitudes of the DPOD model. For a given four sensor configuration, developed using a previously validated strategy, ANNE performed better than two state-of-the-art approaches, namely, a Quadratic Stochastic Estimator (QSE) and a Linear Stochastic Estimator with time delays (DSE). (C) 2011 Elsevier Ltd. All rights reserved.
URI: https://doi.org/10.1016/j.eswa.2011.07.135
https://hdl.handle.net/20.500.11851/7143
ISSN: 0957-4174
1873-6793
Appears in Collections:Makine Mühendisliği Bölümü / Department of Mechanical Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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