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

Cohen K., Siegel S., Seidel J., Aradağ Çelebioğl S., McLaughlin T.

Expert Systems with Applications, vol.39, no.1, pp.1264-1272, 2012 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 39 Issue: 1
  • Publication Date: 2012
  • Doi Number: 10.1016/j.eswa.2011.07.135
  • Journal Name: Expert Systems with Applications
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.1264-1272
  • Keywords: Turbulent cylinder wake, ANNE, Low dimensional modeling, DPOD, Flow control, FEEDBACK-CONTROL, WAKES
  • TED University Affiliated: No


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). © 2011 Elsevier Ltd. All rights reserved.