Model Predictive Flow Control - Invited Paper -

King, Rudibert; Aleksić, Katarina; Gelbert, Gregor; Losse, N.; Muminovic, Rifet; Brunn, A.; Nitsche, W.; Bothien, Mirko R.; Moeck, Jonas P.; Paschereit, Christian Oliver; Noack, Bernd R.; Rist, Ulrich; Zengl, Marcus

doi:10.2514/6.2008-3975

Cited by 20 publications

(6 citation statements)

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“…As the following application of model predictive control will be limited to a nonlinear example exploiting a Galerkin system, the reader is referred to King et al (2008); ; where a linear MPC is applied in experiments to a burner and a bluff body.…”

Section: R Kingmentioning

confidence: 99%

Reduced-Order Modelling for Flow Control

Noack¹,

Morzyński²,

Tadmor³

2011

CISM International Centre for Mechanical Sciences

194

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Section: R Kingmentioning

confidence: 99%

Reduced-Order Modelling for Flow Control

Noack¹,

Morzyński²,

Tadmor³

2011

CISM International Centre for Mechanical Sciences

194

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“…Due to the nonlinear nature of the phenomenon a lot of research has been done on nonlinear control approaches, a survey about the application of neural networks and chaos theory can be found in [1], while the application of model predictive control for flow control is presented in [4], and on application of adaptive control using back stepping is presented in [5]. Some other advances in the analysis of different flows and its implication on control are compiled in [6] and [7].…”

Section: Introductionmentioning

confidence: 99%

Transient energy analysis of a spatially interconnected model for 3D Poiseuille flow

Chughtai

Werner

2010

Proceedings of the 2010 American Control Conference

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In this paper a new model for 3D Poiseuille flow is presented. The model is based on applying a combined spectralfinite difference approach on the velocity-vorticity formulation of the Navier-Stokes equations. In 3D the dominating feature of the problem is non-normality of the eigenvectors. One measure to assess the non-normality is the transient energy response. The model is validated by comparing the maximum transient energy for two different cases; for both cases the Reynolds number is fixed at 5000, in the first case the span-wise spatial frequency is set to zero and the stream-wise spatial frequency is varied from 0.1 to 2, while for the second case the streamwise frequency is set to zero and the span-wise frequency is varied from 0.1 to 3. It is known that for the first case the system is stable, while the second case is highly non-normal with maximum transient energy reaching 4500. It is observed that the model predicts both of these characteristics.Next, as a first step towards controller synthesis, a stabilizing state feedback controller is designed to minimize the transient energy response, by minimizing the induced L2-norm. The closed loop response shows that the energy is reduced by the factor of 30.

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“…Exploring implementable, optimal (or suboptimal) design methodologies which can be computationally realizable in the real time and with the degree of robustness in design and implementation are of interest. Recent studies in the realm of fluid flow, see [18,19] considered the application of the model predictive control (MPC) design in the fluid flow setting. More recently flow separation and vortex shedding suppression by applying numerical methods on a two-dimensional space grid that utilizes a large scale numerical computational scheme to account for the cost function evaluation with the predictive horizon equal to the period of vortex shedding has been considered [20].…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Control of Ginzburg-Landau Equation

Izadi

Koch

Dubljević

2018

Notes on Numerical Fluid Mechanics and Multidisciplinary Design

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This work explores the realization of model predictive control (MPC) design to an important problem of vortex shedding phenomena in fluid flow. The setting of vortex shedding phenomena is represented by a Ginzburg-Landau (GL) equation model and leads to the mathematical representation given by complex infinite dimensional parabolic PDEs. The underlying GL model is considered within the boundary control setting and the modal representation is considered to obtain discrete infinite dimensional system representation which is used in the model predictive control design. The model predictive control design accounts for optimal stabilization of the unstable GL equation model, and for the naturally present input constraints and/or state constraints. The feasibility of the optimization based model predictive controller is ensured through a large enough prediction horizon. The subsequent feasibility is ensured in a disturbance free model setting. The applicability of an easily realizable discrete controller design is demonstrated using simulation with known parameters from the literature.

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Model Predictive Flow Control - Invited Paper -

Cited by 20 publications

References 0 publications

Reduced-Order Modelling for Flow Control

Reduced-Order Modelling for Flow Control

Transient energy analysis of a spatially interconnected model for 3D Poiseuille flow

Model Predictive Control of Ginzburg-Landau Equation

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