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Filtering by: Creator/Author Mclaughlin, Thomas Remove constraint Creator/Author: Mclaughlin, Thomas Department Aerospace Engineering and Engineering Mechanics Remove constraint Department: Aerospace Engineering and Engineering Mechanics

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

    Article thumbnail: Nonlinear estimation of transient flow field low dimensional states using artificial neural nets
    Type:
    Article
    Description/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).
    Creator/Author:
    Cohen, Kelly; Mclaughlin, Thomas; Seidel, Jurgen; Siegel, Stefan, and Aradag, Selin
    Submitter:
    Kelly Cohen
    Date Uploaded:
    02/13/2017
    Date Modified:
    04/05/2017
    Date Created:
    2011-07
    License:
    All rights reserved