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.Attached Files
Published - 00542770.pdf
Files
00542770.pdf
Files
(490.9 kB)
Name | Size | Download all |
---|---|---|
md5:a106f999f1a612fd8edcf1528abf7540
|
490.9 kB | Preview Download |
Additional details
- Eprint ID
- 94002
- Resolver ID
- CaltechAUTHORS:20190320-143014388
- EEC-9402726
- NSF
- C94-0165
- California Trade and Commerce Agency
- N00014-93-1-0990
- Office of Naval Research (ONR)
- F4962093-1-0332
- Air Force Office of Scientific Research (AFOSR)
- Center for Neuromorphic Systems Engineering, Caltech
- Created
-
2019-03-20Created from EPrint's datestamp field
- Updated
-
2021-11-16Created from EPrint's last_modified field