Closed-loop control of an open cavity flow using reduced-order models

By A. Barbagallo, Denis Sipp, Peter J. Schmid. Journal of Fluid Mechanics, 2009

248 citations recorded by the source corpus.

Subfields: Computational Mechanics, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/ecbab62e759631b98d1999dfcb8bcf51da2d9047

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Citations in the corpus

Model Reduction for Flow Analysis and Control (2017, methodology): “As mentioned in Section 3.3, the ERA in turn can be shown to give reduced-order models that are identical to those produced by BPOD (Ma et al. 2011).”
POD-Galerkin method for finite volume approximation of Navier–Stokes and RANS equations (2016, unknown): “In particular, in the fluid dynamics field, the classic control variable is the velocity at the boundary [58] since it could be used to control the velocity field in the domain or an output variable of interest.”
Artificial neural networks trained through deep reinforcement learning discover control strategies for active flow control (2018, methodology): “Despite the considerable efforts put into the theory of flow control, and the use of a variety of analytical and semi-analytical techniques (Barbagallo et al., 2009, 2012; Sipp & Schmid, 2016), bottom-up approaches based on an analysis of the flow equations face considerable difficulties when…”
Model Order Reduction in Fluid Dynamics: Challenges and Perspectives (2014, background): “Another comparison on a problem of designing closed-loop controllers for flow over a cavity was done in [13], where again the balanced POD modes achieved a stable closed-loop controller with fewer ROM degrees-of-freedom.”
Dynamics and Control of Global Instabilities in Open-Flows: A Linearized Approach (2010, methodology): “To identify these modes, it was suggested [113] to use the following quantity”
Proper orthogonal decomposition closure models for turbulent flows: A numerical comparison (2011, background): “…Ito & Ravindran 1998; Graham et al. 1999; Cohen et al. 2003; Bergmann et al. 2005; Lehmann et al. 2005; Hoepffner et al. 2006; Bagheri et al. 2009; Barbagallo et al. 2009; Ahuja & Rowley 2010; Akhtar & Nayfeh 2010) and data assimilation of atmospheric and oceanic flows (Luo et al. 2007; Daescu &…”
Dynamic mode decomposition of numerical and experimental data (2008, result): “Modes from the stable branch show increasingly more small-scale features inside the cavity, as the frequency λi increases, which is consistent with observations of Barbagallo et al. (2009).”
Applications of the dynamic mode decomposition (2011, result): “In contrast to the previous case, though, more arc-like eigenvalue clusters are visible, a feature that is often observed in the numerical calculationofglobalspectra.Structureswithinsuchaneigenvaluearccontainfluidelementsthatshowsimilarsupport in the flow domain but are characterized by different lo”