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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
    Descripción/Resumen:
    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).
    Creador/Autor:
    Cohen, Kelly; Mclaughlin, Thomas; Seidel, Jurgen; Siegel, Stefan, and Aradag, Selin
    Peticionario:
    Kelly Cohen
    Fecha modificada:
    02/13/2017
    Fecha modificada:
    04/05/2017
    Fecha de creacion:
    2011-07
    Licencia:
    All rights reserved

  • Filtered POD-based low-dimensional modeling of the 3D turbulent flow behind a circular cylinder

    Article thumbnail: Filtered POD-based low-dimensional modeling of the 3D turbulent flow behind a circular cylinder
    Type:
    Article
    Descripción/Resumen:
    Low-dimensional models have proven essential for feedback control and estimation of flow fields. While feedback control based on global flow estimation can be very efficient, it is often difficult to estimate the flow state if structures of very different length scales are present in the flow. The conventional snapshot-based proper orthogonal decomposition (POD), a popular method for low-order modeling, does not separate the structures according to size, since it optimizes modes based on energy. Two methods are developed in this study to separate the structures in the flow based on size. One of them is Hybrid Filtered POD method and the second one is 3D FFT-based Filtered POD approach performed using a fast Fourier transform (FFT)-based spatial filtering. In both the methods, a spatial low-pass filter is employed to precondition snapshot sets before deriving POD modes. Three-dimensional flow data from the simulation of turbulent flow over a circular cylinder wake at Re=20000 is used to evaluate the performance of the two methods. Results show that both the FFT-based 3D Filtered POD and Hybrid Filtered POD are able to capture the large-scale features of the flow, such as the von Karman vortex street, while not being contaminated by small-scale turbulent structures present in the flow.
    Creador/Autor:
    Cohen, Kelly; Seidel, Jurgen; Siegel, Stefan; McLaughlin, Thomas, and Aradag, Selin
    Peticionario:
    Kelly Cohen
    Fecha modificada:
    02/13/2017
    Fecha modificada:
    04/05/2017
    Fecha de creacion:
    2011-05
    Licencia:
    All rights reserved

  • Integration of Computations and Experiments for Flow Control Research With Undergraduate Students

    Article thumbnail: Integration of Computations and Experiments for Flow Control Research With Undergraduate Students
    Type:
    Article
    Descripción/Resumen:
    The methods and outcome of a senior undergraduate project related to the control of a turbulent cylinder wake flow using plasma actuators are summarized in this article. The study integrates computational fluid dynamics (CFD) with experimentation and combines fluid mechanics with flow control research, crossing the boundaries between engineering disciplines.Comput. Appl. Eng. Educ.
    Creador/Autor:
    Cohen, Kelly; McLaughlin, Thomas; Seaver, Christopher A., and Aradag, Selin
    Peticionario:
    Kelly Cohen
    Fecha modificada:
    02/08/2017
    Fecha modificada:
    04/05/2017
    Fecha de creacion:
    2009-02
    Licencia:
    All rights reserved