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Published August 1996 | Published
Book Section - Chapter Open

Active drag reduction using neural networks

Abstract

This paper presents the application of a neural network controller to the problem of active drag reduction in a fully turbulent 3D fluid flow regime. The neural network learns a function nearly identical to an analytically derived control law. We then demonstrate the ability of a neural controller to maintain a drag-reduced flow in a fully turbulent fluid simulation. Finally we examine the amount of parameter variation that may be required for a physical implementation of such a neural controller.

Additional Information

© 1996 IEEE. This work. is supported in part by the Center for Neuromorphic Systems Engineering as a part of the National Science Foundation Engineering Research Center Program under grant EEC-9402726; and by the California Trade and Commerce Agency, Office of Strategic Technology under grant C94-0165. This work is also supported in part by ARPA/ONR under grant no. N00014-93-1-0990, and by an AFOSR University Research Initiative grant no. F4962093-1-0332. Computer time has been supplied by the San Diego Supercomputer Center and by the NAS program at NASA Ames Research Center.

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