Optimal rotary control of the cylinder wake using proper orthogonal decomposition reduced-order model

By M. Bergmann, Laurent Cordier, J. Brancher. 2005

338 citations recorded by the source corpus.

Subfields: Computational Mechanics, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/68d738b27b908b258ee9f1bc0aec43400906e7cd

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In Computational Mechanics

In Statistical and Nonlinear Physics

Citations in the corpus

Local improvements to reduced-order models using sensitivity analysis of the proper orthogonal decomposition (2009, methodology): “Unlike the experiences reported in the literature (see e.g. Noack et al. 2003; Sirisup & Karniadakis 2004; Bergmann et al. 2005), we have not observed any numerical instability after integration over a number of vortex shedding periods.”
Efficient non‐linear model reduction via a least‐squares Petrov–Galerkin projection and compressive tensor approximations (2011, methodology): “Within these contexts, the projection-based model reduction technique has been successfully applied to problems in aerodynamics [8–12], aeroelasticity [13–17], control [18], structural dynamics [19–22], and non-destructive evaluation and parameter estimation [23], to name only a few.”
A low‐cost, goal‐oriented ‘compact proper orthogonal decomposition’ basis for model reduction of static systems (2011, background): “Unlike real-time problems, offlineonline decomposition strategies are ill-suited for this class of problems due to the existence of a ‘break-even’ point [7, 8, 9, 10], which is characterized by the number of online analyses required for computational cost savings to be achieved for the overall offli”
Proper orthogonal decomposition closure models for turbulent flows: A numerical comparison (2011, background): “…applications in science and engineering, such as fluid flow control (see for example 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…”
Vortex shedding suppression and wake control: A review (2016, background): “Table 12: A compilation on available data about the impact of the control methods on the drag reduction Author Relative drag reduction Range of Reynolds number Method Thomas et al. (2005) 90% 1.28×10 4 -2.54×10 4 Steady blowing with two actuators on a cylinder Amitay et al. (1997) 30% 7.55×10 4 Synt”
Machine Learning Control – Taming Nonlinear Dynamics and Turbulence (2016, background): “Of the many available approaches, a large subset is considered as an academic exercise, such as rotary [81, 27], streamwise [58], and transverse [53, 31] oscillation of a bluff body.”