Browsing by Subject "Subsonic cavity flows"

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    Experimental results and bifurcation analysis on scaled feedback control for subsonic cavity flows
    (IEEE, 2006) Yuan, X.; Caraballo, E.; Debiasi, M.; Little, J.; Serrani, A.; Özbay, Hitay; Samimy, M.
    In this paper, we present the latest results of our ongoing research activities in the development of reduced-order models based feedback control of subsonic cavity flows. The model was developed using the Proper Orthogonal Decomposition of Particle Image Velocimetry images in conjunction with the Galerkin projection of the Navier-Stokes equations onto the resulting spatial eigenfunctions. Stochastic Estimation method was used to obtain the state estimation of the Galerkin system from real time surface pressure measurements. A linear-quadratic optimal controller was designed to reduce cavity flow resonance and tested in the experiments. Real-time implementation shows a significant reduction of the sound pressure level within the cavity, with a remarkable attenuation of the resonant tone and a redistribution of the energy into various modes with lower energy levels. A mathematical analysis of the performance of the LQ control, in agreement with the experimental results, is presented and discussed.
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    Seven tuning schemes for an ADALINE model to predict floor pressures in a subsonic cavity flow
    (Sage Publications Ltd., 2009) Efe, M. Ö.; Debiasi, M.; Yan, P.; Özbay, Hitay; Samimy, M.
    This paper presents a simple yet effective one-step-ahead predictor based on an adaptive linear element (ADALINE). Several tuning schemes are studied to see whether the obtained model is consistent. The process under investigation is a subsonic cavity flow system. The experimental data obtained from the system is post-processed to obtain a useful predictor. The contribution of the paper is to demonstrate that despite the spectral richness of the observed data, a simple model with various tuning schemes can help to a satisfactory extent. Seven algorithms are studied, including the least mean squares (LMS), recursive least squares (RLS), modified Kaczmarz's algorithm (MK), stochastic approximation algorithm (SA), gradient descent (GD), Levenbergĝ€ "Marquardt optimization technique (LM) and sliding mode-based tuning (SM). The model and its properties are discussed comparatively.