Browsing by Author "Efe, M. Ö."

Now showing 1 - 9 of 9

Open Access
Control of subsonic cavity flows by neural networks-analytical models and experimental validation
(American Institute of Aeronautics and Astronautics, 2005) Efe, M. Ö.; Debiasi, M.; Yan, P.; Özbay, Hitay; Samimy, M.
Flow control is attracting an increasing attention of researchers from a wide spectrum of specialties because of its interdisciplinary nature and the associated challenges. One of the main goals of The Collaborative Center of Control Science at The Ohio State University is to bring together researchers from different disciplines to advance the science and technology of flow control. This paper approaches the control of subsonic cavity flow, a study case we have selected, from a computational intelligence point of view, and offers a solution that displays an interconnected neural architecture. The structures of identification and control, together with the experimental implementation are discussed. The model and the controller have very simple structural configurations indicating that a significant saving on computation is possible. Experimental testing of a neural emulator and of a directly-synthesized neurocontroller indicates that the emulator can accurately reproduce a reference signal measured in the cavity floor under different operating conditions. Based on preliminary results, the neurocontroller appears to be marginally effective and produces spectral peak reductions analogous to those previously observed by the authors using linearcontrol techniques. The current research will continue to improve the capability of the neural emulator and of the neurocontroller.
Open Access
Infinite dimensional and reduced order observers for Burgers equation
(Taylor & Francis, 2005) Efe, M. Ö.; Özbay, Hitay; Samimy, M.
Obtaining a representative model in feedback control system design problems is a key step and is generally a challenge. For spatially continuous systems, it becomes more difficult as the dynamics is infinite dimensional and the well known techniques of systems and control engineering are difficult to apply directly. In this paper, observer design is reported for one-dimensional Burgers equation, which is a non-linear partial differential equation. An infinite dimensional form of the observer is demonstrated to converge asymptotically to the target dynamics, and proper orthogonal decomposition is used to obtain the reduced order observer. When this is done, the corresponding observer is shown to be successful under certain circumstances. The paper unfolds the connections between target dynamics, observer and their finite dimensional counterparts. A set of simulation results has been presented to justify the theoretical claims of the paper.
Open Access
Integral action based Dirichlet boundary control of Burgers equation
(IEEE, 2003) Efe, M. Ö.; Özbay, Hitay
Modeling and boundary control for Burgers Equation is studied in this paper. Modeling has been done via processing of numerical observations through singular value decomposition with Galerkin projection. This results in a set of spatial basis functions together with a set of Ordinary Differential Equations (ODEs) describing the temporal evolution. Since the dynamics described by Burgers equation is nonlinear, the corresponding reduced order dynamics turn out to be nonlinear. The presented analysis explains how boundary condition appears as a control input in the ODEs. The controller design is based on the linearization of the dynamic model. It has been demonstrated that an integral controller, whose gain is a function of the spatial variable, is sufficient to observe reasonably high tracking performance with a high degree of robustness.
Open Access
Low dimensional modelling and Dirichlét boundary controller design for Burgers equation
(Taylor & Francis, 2004) Efe, M. Ö.; Özbay, Hitay
Modelling and boundary control for the Burgers equation is studied in this paper. Modelling has been done via processing of numerical observations through proper orthogonal decomposition (POD) with Galerkin projection. This results in a set of spatial basis functions together with a set of ordinary differential equations (ODEs) describing the temporal evolution. Since the dynamics described by the Burgers equation are non-linear, the corresponding reduced-order dynamics turn out to be non-linear. The presented analysis explains how the free boundary condition appears as a control input in the ODEs and how controller design can be accomplished. The issues of control system synthesis are discussed from the point of practicality, performance and robustness. The numerical results obtained are in good compliance with the theoretical claims. A comparison of various different approaches is presented. © 2004 Taylor and Francis Ltd.
Open Access
Multi input dynamical modeling of heat flow with uncertain diffusivity parameter
(Taylor & Francis, 2003) Efe, M. Ö.; Özbay, Hitay
This paper focuses on the multi-input dynamical modeling of one-dimensional heat conduction process with uncertainty on thermal diffusivity parameter. Singular value decomposition is used to extract the most significant modes. The results of the spatiotemporal decomposition have been used in cooperation with Galerkin projection to obtain the set of ordinary differential equations, the solution of which synthesizes the temporal variables. The spatial properties have been generalized through a series of test cases and a low order model has been obtained. Since the value of the thermal diffusivity parameter is not known perfectly, the obtained model contains uncertainty. The paper describes how the uncertainty is modeled and how the boundary conditions are separated from the remaining terms of the dynamical equations. The results have been compared with those obtained through analytic solution. © Taylor and Francis Ltd.
Open Access
Neural network-based modelling of subsonic cavity flows
(Taylor & Francis, 2008) Efe, M. Ö.; Debiasi, M.; Yan, P.; Özbay, Hitay; Samimy, M.
A fundamental problem in the applications involved with aerodynamic flows is the difficulty in finding a suitable dynamical model containing the most significant information pertaining to the physical system. Especially in the design of feedback control systems, a representative model is a necessary tool constraining the applicable forms of control laws. This article addresses the modelling problem by the use of feedforward neural networks (NNs). Shallow cavity flows at different Mach numbers are considered, and a single NN admitting the Mach number as one of the external inputs is demonstrated to be capable of predicting the floor pressures. Simulations and real time experiments have been presented to support the learning and generalization claims introduced by NN-based models.
Open Access
Proper orthogonal decomposition for reduced order modeling: 2D heat flow
(IEEE, 2003-06) Efe, M. Ö.; Özbay, Hitay
Modeling issues of infinite dimensional systems is studied in this paper. Although the modeling problem has been solved to some extent, use of decomposition techniques still pose several difficulties. A prime one of this is the amount of data to be processed. Method of snapshots integrated with POD is a remedy. The second difficulty is the fact that the decomposition followed by a projection yields an autonomous set of finite dimensional ODEs that is not useful for developing a concise understanding of the input operator of the system. A numerical approach to handle this issue is presented in this paper. As the example, we study 2D heat flow problem. The results obtained confirm the theoretical claims of the paper and emphasize that the technique presented here is not only applicable to infinite dimensional linear systems but also to nonlinear ones.
Open Access
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.
Open Access
Support vector networks for prediction of floor pressures in shallow cavity flows
(IEEE, 2007) Efe, M. Ö.; Debiasi, M.; Yan, P.; Özbay, Hitay; Samimy, M.
During the last decade, Support Vector Machines (SVM) have proved to be very successful tools for classification and regression problems. The representational performance of this type of networks is studied on a cavity flow facility developed to investigate the characteristics of aerodynamic flows at various Mach numbers. Several test conditions have been experimented to collect a set of data, which is in the form of pressure readings from particular points in the test section. The goal is to develop a SVM based model that emulates the one step ahead behavior of the flow measurement at the cavity floor. The SVM based model is built for a very limited amount of training data and the model is tested for an extended set of test conditions. A relative error is defined to measure the reconstruction performance, and the peak value of the FFT magnitude of the error is measured. The results indicate that the SVM based model is capable of matching the experimental data satisfactorily over the conditions that are close to the training data collection conditions, and the performance degrades as the Mach number gets away from the conditions considered during training.