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Conference Object Citation - WoS: 9Citation - Scopus: 13Modeling of Subsonic Cavity Flows by Neural Networks(Ieee, 2004) Efe, MÖ; Debiasi, M; Özbay, H; Samimy, MInfluencing the behavior of a flow field is a core issue as its improvement can yield significant increase of the efficiency and performance of fluidic systems. On the other hand, the tools of classical control systems theory are not directly applicable to processes displaying spatial continuity as in fluid flows. The cavity flow is a good example of this and a recent research focus in aerospace science is its modeling and control. The objective is to develop a finite dimensional representative model for the system with appropriately defined inputs and outputs. Towards the goal of reconstructing the pressure fluctuations measured at the cavity floor, this paper-demonstrates that given some history of inputs and outputs, a neural network based feedforward model can be developed such that the response of the neural network matches the measured response. The advantages of using such a model arc the representational simplicity of the model, structural flexibility to enable controller design and the ability to Store information in an interconnected structure.Article Citation - WoS: 4Citation - Scopus: 4Multi Input Dynamical Modeling of Heat Flow With Uncertain Diffusivity Parameter(Taylor & Francis inc, 2003) Efe, MÖ; Özbay, HThis 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.
