Nonlinear estimation of transient flow field low dimensional states using artificial neural nets

Authors:
Kelly Cohen;Stefan Siegel;Jürgen Seidel;Selin Aradag;Thomas Mclaughlin
Affiliations:
Department of Aerospace Engineering, University of Cincinnati, Cincinnati, OH, USA;Department of Aeronautics, United States Air Force Academy, CO 80840, USA;Department of Aeronautics, United States Air Force Academy, CO 80840, USA;Department of Mechanical Engineering, TOBB University of Economics and Technology, Ankara 06560, Turkey;Department of Aeronautics, United States Air Force Academy, CO 80840, USA
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

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Expert Systems with Applications: An International Journal

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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).