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

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.