2017
DOI: 10.1017/jfm.2017.470
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Abstract: We present an active feedback blowing and suction (AFBS) procedure via model reduction for unsteady wake flow and the vortex-induced vibration (VIV) of circular cylinders. The reduced-order model (ROM) for the AFBS procedure is developed by the eigensystem realization algorithm (ERA), which provides a low-order representation of the unsteady flow dynamics in the neighbourhood of the equilibrium steady state. The actuation is considered via vertical suction and a blowing jet at the porous surface of a circular … Show more

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Cited by 28 publications
(6 citation statements)
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“…The control problem alone needs only the portion of the system that is observable and controllable. The practice of model reduction in flow control was treated in Bagheri et al (2009), Semeraro et al (2011), Poussot-Vassal & Sipp (2015 and Yao & Jaiman (2017). The approach was shown to be successful in the sense that the solution to the control problem was nearly unaffected by the use of a ROM.…”
Section: Introductionmentioning
confidence: 99%
“…Finally, we demonstrated that the trained CNN can be used to predict many different geometries. The efficient prediction of force coefficients using the present data-driven method has a great value for the iterative engineering design and the feedback flow control[34]. We hope that this work will help to expand the use of deep neural networks in a wider range of CFD applications.D and t, τ ≥ 0 can be expressed as: Lψ(x, t) = ∆p 2µ ψ(x, t), (B.1) LG(x, t; ξ, τ ) = δ(x − ξ)δ(t − τ ), (B.2) where D is the fluid domain of interest and ξ and τ are the dummy variables.…”
mentioning
confidence: 90%
“…The work of Rowley [49] further extended balanced truncation to high-dimensional systems where approximate balanced truncation can be achieved using a snapshot-based algorithm named balanced proper orthogonal decomposition (BPOD). The ERA is a system identification method that is equivalent to BPOD and allows direct application to flow systems using only DNS or experimental data [7,32,25,56,57] without requiring simulations of the adjoint system. These methods were originally designed for stable systems of large dimension and were subsequently extended to unstable systems either by a state-projection method [1] or by limiting the sampling time [25].…”
Section: Reduced-order Modellingmentioning
confidence: 99%
“…Mathematically equivalent to the balanced POD (Ma, Ahuja & Rowley 2011), the ROM resulting from the ERA is a linear projection of the original system on the set of modes possessing the most observable and controllable flow structures. Recently, the ERA-based ROM has been used for stability analysis and flow control problems by the fluid mechanics community in various instances (Ma et al 2011;Flinois, Morgans & Schmid 2015;Flinois & Morgans 2016;Yao & Jaiman 2017a). The work presented by Yao & Jaiman (2017b) attempts to construct a unified description of frequency lock-in for elastically mounted cylinders.…”
Section: Stability Analysis Via Model Reductionmentioning
confidence: 99%
“…Physics-driven approaches derive models based on the Navier-Stokes equations, including the Galerkin-POD method [7] and a recently developed resolvent-based method [8][9][10]. Alternatively, models can be built via data-driven approaches, including Dynamic Mode Decomposition (DMD) [11], Artificial Neural Networks (ANN) [12], Sparse Identification of Nonlinear Dynamics (SINDy) [13], and the Eigensystem Realization Algorithm (ERA) [14,15].…”
Section: Introductionmentioning
confidence: 99%