NDLI: Artificial neural network-based modeling and intelligent control of transitional flows

Content Provider	IEEE Xplore Digital Library
Author	Sahan, R.A. Koc-Sahan, N. Albin, D.C. Liakopoulos, A.
Copyright Year	1997
Description	Author affiliation: Dept. of Mech. Eng., Marmara Univ., Istanbul, Turkey (Sahan, R.A.)
Abstract	Empirical eigenfunctions of transitional flow in a grooved channel are extracted by proper orthogonal decomposition (POD). POD is applied to numerical solutions of the governing Navier-Stokes partial differential equations at Reynolds numbers Re=430, 750, 1050 and at Prandtl number Pr=0.71 (air flow). For each value of Re, a low-dimensional set of nonlinear ordinary differential equations is derived by Galerkin projection. The Galerkin projection-based low-order dynamical models are used to generate the data required to efficiently train artificial neural networks in the range 400/spl les/Re/spl les/1200. Accurate artificial neural network-based models of the flow system are obtained. The study demonstrates the potential use of Galerkin projection-based and artificial neural network-based low-order models as valuable tools for flow modeling and for prediction of short- and long-term behavior of transitional flow systems. A possible real-time intelligent flow control scheme is briefly discussed.
Starting Page	359
Ending Page	364
File Size	571041
Page Count	6
File Format	PDF
ISBN	0780338766
DOI	10.1109/CCA.1997.627577
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1997-10-05
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Artificial neural networks Intelligent control Heat transfer Eigenvalues and eigenfunctions Partial differential equations Differential equations Predictive models Power system modeling Fault diagnosis Geometry
Content Type	Text
Resource Type	Article

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