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

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Date Modified: 04/05/2017

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).

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  • Expert Systems with Applications
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  • This work was part of a pilot "mediated-deposit model" where library staff found potential works, later submitted for faculty review

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Identifier: 10.1016/j.eswa.2011.07.135
Link: http://dx.doi.org/10.1016/j.eswa.2011.07.135

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Permanent link to this page: https://scholar.uc.edu/show/bc386v185