Feedback Control of Cavity Flow Using Experimental Based Reduced Order Model

2005 ◽  
Author(s):  
Edgar Caraballo ◽  
X. Yuan ◽  
Jesse Little ◽  
Marco Debiasi ◽  
P Yan ◽  
...  
Keyword(s):  
Feedback Control ◽  
Cavity Flow ◽  
Reduced Order Model ◽  
Order Model ◽  
Reduced Order

Feedback control of subsonic cavity flows using reduced-order models

2007 ◽  
Vol 579 ◽  
pp. 315-346 ◽  
Author(s):  
M. SAMIMY ◽  
M. DEBIASI ◽  
E. CARABALLO ◽  
A. SERRANI ◽  
X. YUAN ◽  
...  
Keyword(s):  
Feedback Control ◽  
Surface Pressure ◽  
Controller Design ◽  
Cavity Flow ◽  
Open Loop ◽  
Reduced Order Model ◽  
Cavity Flows ◽  
Reduced Order Models ◽  
Order Model ◽  
Reduced Order

Development, experimental implementation, and the results of reduced-order model based feedback control of subsonic shallow cavity flows are presented and discussed. Particle image velocimetry (PIV) data and the proper orthogonal decomposition (POD) technique are used to extract the most energetic flow features or POD eigenmodes. The Galerkin projection of the Navier–Stokes equations onto these modes is used to derive a set of nonlinear ordinary differential equations, which govern the time evolution of the eigenmodes, for the controller design. Stochastic estimation is used to correlate surface pressure data with flow-field data and dynamic surface pressure measurements are used to estimate the state of the flow. Five sets of PIV snapshots of a Mach 0.3 cavity flow with a Reynolds number of 105 based on the cavity depth are used to derive five different reduced-order models for the controller design. One model uses only the snapshots from the baseline (unforced) flow while the other four models each use snapshots from the baseline flow combined with snapshots from an open-loop sinusoidal forcing case. Linear-quadratic optimal controllers based on these models are designed to reduce cavity flow resonance and are evaluated experimentally. The results obtained with feedback control show a significant attenuation of the resonant tone and a redistribution of the energy into other modes with smaller energy levels in both the flow and surface pressure spectra. This constitutes a significant improvement in comparison with the results obtained using open-loop forcing. These results affirm that reduced-order model based feedback control represents a formidable alternative to open-loop strategies in cavity flow control problems even in its current state of infancy.

Distributed Feedback Control of the Benjamin-Bona-Mahony-Burgers Equation by a Reduced-Order Model

2015 ◽  
Vol 5 (1) ◽  
pp. 61-74 ◽  
Author(s):  
Guang-Ri Piao ◽  
Hyung-Chun Lee
Keyword(s):  
Feedback Control ◽  
Numerical Experiments ◽  
Burgers Equation ◽  
Distributed Feedback ◽  
Reduced Order Model ◽  
Order Model ◽  
Reduced Order ◽  
Order Case ◽  
Functional Gain ◽  
Proper Orthogonal

AbstractA reduced-order model for distributed feedback control of the Benjamin-Bona-Mahony-Burgers (BBMB) equation is discussed. To retain more information in our model, we first calculate the functional gain in the full-order case, and then invoke the proper orthogonal decomposition (POD) method to design a low-order controller and thereby reduce the order of the model. Numerical experiments demonstrate that a solution of the reduced-order model performs well in comparison with a solution for the full-order description.

scholarly journals Reduced-order model based feedback control of the modified Hasegawa-Wakatani model

2013 ◽  
Vol 20 (4) ◽  
pp. 042501 ◽  
Author(s):  
I. R. Goumiri ◽  
C. W. Rowley ◽  
Z. Ma ◽  
D. A. Gates ◽  
J. A. Krommes ◽  
...  
Keyword(s):  
Feedback Control ◽  
Reduced Order Model ◽  
Order Model ◽  
Reduced Order ◽  
Model Based

scholarly journals Reduced-Order Modeling of Cavity Flow Oscillations across Multi-Mach Numbers Using Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Zhe Liu ◽  
Fangli Ning ◽  
Hui Ding ◽  
Qingbo Zhai ◽  
Juan Wei
Keyword(s):  
Deep Learning ◽  
Cavity Flow ◽  
Reduced Order Model ◽  
Flow Conditions ◽  
Order Model ◽  
Reduced Order ◽  
The Future ◽  
Flow Oscillations ◽  
Mach Numbers ◽  
Cavity Flow Oscillations

The reduced-order model can accurately and efficiently predict unsteady problems in many aerospace engineering applications. The traditional reduced-order model based on proper orthogonal decomposition (POD) and Galerkin projection has poor robustness and large error in predicting complex problems. In this paper, a reduced-order model combining POD and deep learning is proposed to predict cavity flow oscillations under different flow conditions. Firstly, POD modes and corresponding coefficients are obtained by POD. Then, two deep learning frameworks, including multilayer perceptron (MLP) and long short-term memory (LSTM) neural networks, are used to predict the future POD coefficients, respectively. Finally, the cavity flow oscillations across multi-Mach numbers are predicted by the POD modes and the future coefficients. The results show that both of these frameworks can accurately predict cavity flow oscillations when the flow conditions change, and the time cost is reduced by order of magnitude. In addition, due to the performance of LSTM is better than that of MLP, its calculation speed is faster.

Optimal Output Feedback Control of Asymmetric Systems Using Complex Modes

1993 ◽  
Vol 115 (2) ◽  
pp. 307-313 ◽  
Author(s):  
G. W. Fan ◽  
H. D. Nelson ◽  
M. P. Mignolet
Keyword(s):  
Feedback Control ◽  
Output Feedback ◽  
Output Feedback Control ◽  
High Order ◽  
Reduced Order Model ◽  
Control Procedure ◽  
Order Model ◽  
Complex Mode ◽  
Reduced Order ◽  
Complex Modes

A Linear Quadratic Regulator (LQR)-based least-squares output feedback control procedure using a complex mode procedure is developed for the optimal vibration control of high-order asymmetric discrete system. An LQ Regulator is designed for a reduced-order model obtained by neglecting high-frequency complex modes of the original system. The matrix transformations between physical coordinates and complex mode coordinates are derived. The complex mode approach appears to provide more accurate reduced-order models than the normal mode approach for asymmetric discrete systems. The proposed least-squares output feedback control procedure takes advantage of the fact that a full-state feedback control is possible without using an observer. In addition, the lateral vibration of a high-order rotor system can be effectively controlled by monitoring one single location along the rotor shaft, i.e., the number of measured states can be much less than the number of eigenvectors retained in producing the reduced-order model while acceptable performance of the controller is maintained. The procedure is illustrated by means of a 52 degree-of-freedom finite element based rotordynamic system. Simulation results show that LQ regulators based on a reduced-order model with 12 retained eigenvalues can be accurately approximated by using feedback of four measured states from one location along the rotor shaft. The controlled and uncontrolled transient responses, using various numbers of measured states, of the original high-order system are shown. Comparisons of reduced-order model results using normal modes and complex modes are presented. The spillover problem is discussed for both collocated and noncollocated cases based on this same example.

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