Browsing by Author "Samimy, Mo"

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    Feedback control design for subsonic cavity flows
    (Applied Mathematics Scientific Research Institute, 2009-01-31) Yuan, Xuetao; Caraballo, Elba V.; Little, J.; Debiasi, Marco; Serrani, A.; Ozbay, H.; Myatt, James H.; Samimy, Mo
    A benchmark problem in active aerodynamic flow control, suppression of strong pressure oscillations induced by flow over a shallow cavity, is addressed in this paper. Proper orthogonal decomposition and Galerkin projection techniques are used to obtain a reduced-order model of the flow dynamics from experimental data. The model is made amenable to control design by means of a control separation technique, which makes the control input appear explicitly in the equations. A prediction model based on quadratic stochastic estimation correlates flow field data with surface pressure measurements, so that the latter can be used to reconstruct the state of the model in real time. The focus of this paper is on the controller design and implementation. A linear-quadratic optimal controller is designed on the basis of the reduced-order model to suppress the cavity flow resonance. To account for the limitation on the magnitude of the control signal imposed by the actuator, the control action is modified by a scaling factor, which plays the role of a bifurcation parameter for the closed-loop system. Experimental results, in qualitative agreement with the theoretical analysis, show that the controller achieves a significant attenuation of the resonant tone with a redistribution of the energy into other frequencies, and exhibits a certain degree of robustness when operating in off-design conditions.
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    Further development of feedback control of cavity flow using experimental based reduced order model
    (AIAA, 2006-01-31) Caraballo, Edgar; Yuan, Xin; Little, Jesse; Debaisi, M.; Serrani, Andrea; Myatt, James; Samimy, Mo
    In our recent work we presented preliminary results for subsonic cavity flow control using a reduced-order model based feedback control derived from experimental measurements. The model was developed using the Proper Orthogonal Decomposition of PIV images in conjunction with the Galerkin projection of the Navier-Stokes equations onto the resulting spatial eigenfunctions. A linear-quadratic optimal controller was designed to reduce cavity flow resonance by controlling the time coefficient and tested in the experiments. The stochastic estimation method was used for real-time estimation of the corresponding time coefficients from 4 dynamic surface pressure measurements. The results obtained showed that the controller was capable of reducing the cavity flow resonance at the design Mach 0.3 flow, as well as at other flows with slightly different Mach number. In the present work we present several improvements made to the method. The reduced order model was derived from a larger set of PIV measurements and we used 6 sensors for the stochastic estimation of the instantaneous time coefficients. The reduced order model so obtained shows a better convergence of the time coefficients. This combined with the 6-sensor estimation improves the control performance while using a scaling factor closer to the theoretically expected value. The controller also performed better in off design flow conditions.
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    Influence of stochastic estimation on the control of subsonic cavity flow – A preliminary study
    (AIAA, 2006-06-30) Debiasi, Marco; Little, J.; Serrani, A.; Yuan, X.; Myatt, James; Samimy, Mo
    This work aims at understanding how the different elements involved in the feedback loop influence the overall control performance of a subsonic cavity flow based on reducedorder modeling. To this aim we compare preliminary and limited sets of experimental results obtained by modifying some relevant characteristics of the loop. Our results support the findings in the literature that use of quadratic stochastic estimation is preferable to the linear one for real-time update of the model parameters. They also seem to indicate the merit of using more than one time sample of the pressure for performing the real-time update of the model through stochastic estimation. The effect of using two different sets of pressure signals for the stochastic estimation also corroborates previous findings indicating the need for optimizing the number and the placement of the sensors used in the feedback control loop. Finally we observed that the characteristics of the actuator can alter significantly the overall control effect by introducing in the feedback loop additional, undesirable frequency components that are not modeled and hence controlled. A compensator for the actuator is currently being designed that will alleviate this problem thus enabling a clearer understanding of the overall control technique.