Flow Control in a Driven Cavity Incorporating Excitation Phase Differential

Fitzpatrick, Kristin; Feng, Yiran; Lind, Rick; Kurdila, Andrew J.; Mikolaitis, David W.

doi:10.2514/1.4664

Cited by 20 publications

(9 citation statements)

References 4 publications

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“…The ability to understand and have control over fluid flow is a topic of active research, the benefits of which include reducing fuel costs in vehicles and improving the effectiveness of industrial processes [1,2]. Among numerous studies on flow control one finds research regarding flow control in aircrafts and airfoils [3,4], control of channel flows [5,6,7,8], control of turbulent boundary layers [9], control of combustion instability [10], stabilization of bluff-body flow [11], control of cylinder wakes [12,13,14], control of cavity flows [15,16,17,18,19], optimal control of vortex shedding [20,21] and control of fluid flow in capillaries [22,23].…”

Section: Introductionmentioning

confidence: 99%

Modeling, control and evaluation of fluid flow systems using adaptation based linear parameter varying models

Kasnakoğlu

2009

2009 European Control Conference (ECC)

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In this paper a systematic modeling and control approach for flow problems is considered. A nonlinear Galerkin model is obtained from the partial differential equations (PDEs) describing the flow; and a Linear Parameter Varying (LPV) model is constructed to approximate the Galerkin model, where the parameter variation of the LPV model is controller by an adaptation mechanism. The LPV model is then treated as a surrogate on which the control design is carried out, where the parameter variations provide a range of uncertainty in which the control design must perform satisfactorily. It is shown that if certain conditions are met, then such a controller design will succeed when applied to the nonlinear Galerkin model. The ideas developed in the present paper are illustrated through a flow control example governed by the Navier-Stokes (NS) PDEs, where it is observed that a controller design based on the proposed approach is successful in achieving a desired regulation within the flow domain. In addition, it is seen that the LPV model can be used to predict certain robustness properties of the closed-loop system.

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Section: Introductionmentioning

confidence: 99%

Modeling, control and evaluation of fluid flow systems using adaptation based linear parameter varying models

Kasnakoğlu

2009

2009 European Control Conference (ECC)

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“…Thus, from a scientific and technological point of view, modelling and understanding fluid flow is an issue of high importance [1,2]. Among extensive research on the topic one can find studies on flow control in aircrafts and airfoils [3,4], control of channel flows [5,6], control of turbulent boundary layers [7], control of combustion instability [8], stabilization of bluff-body flow [9], control of cylinder wakes [10,11], control of cavity flows [12][13][14][15] and optimal control of vortex shedding [16,17].…”

Section: Introductionmentioning

confidence: 99%

“…These methods address the problem that the control input gets embedded into the system coefficients and remedy the issue by producing stand-alone control terms in the dynamics. POD-based methods have been used for the modelling and control of numerous flow applications, including feedback control of cylinder wakes [10,11], control of cavity flows [12][13][14][15], optimal control of vortex shedding [17] and the stabilization of the flow over obstacles [25].…”

Section: Introductionmentioning

confidence: 99%

Feedback flow control employing local dynamical modelling with wavelets

Erbil

Kasnakoğlu

2009

Mathematical and Computer Modelling of Dynamical Systems

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In this paper, we utilize wavelet transform to obtain dynamical models describing the behaviour of fluid flow in a local spatial region of interest. First, snapshots of the flow are obtained from experiments or from computational fluid dynamics (CFD) simulations of the governing equations. A wavelet family and decomposition level is selected by assessing the reconstruction success under the resulting inverse transform. The flow is then expanded onto a set of basis vectors that are constructed from the wavelet function. The wavelet coefficients associated with the basis vectors capture the time variation of the flow within the spatial region covered by the support of the basis vectors. A dynamical model is established for these coefficients by using subspace identification methods. The approach developed is applied to a sample flow configuration on a square domain where the input affects the system through the boundary conditions. It is observed that there is good agreement between CFD simulation results and the predictions of the dynamical model. A controller is designed based on the dynamical model and is seen to be successful in regulating the velocity of a given point within the region of interest.

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“…However, it lacks the functionality of being practical and quick for real-time complex fluid mechanics applications, and such limitations cause difficulties especially in the development of flow control strategies (Fitzpatrick et al, 2005). In order to observe the flow structures and their characteristics in real-time systems in detail, a more practical procedure is needed.…”

Section: Introductionmentioning

confidence: 99%