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Reducing Wind Tunnel Data Requirements Using Neural NetworksThe use of neural networks to minimize the amount of data required to completely define the aerodynamic performance of a wind tunnel model is examined. The accuracy requirements for commercial wind tunnel test data are very severe and are difficult to reproduce using neural networks. For the current work, multiple input, single output networks were trained using a Levenberg-Marquardt algorithm for each of the aerodynamic coefficients. When applied to the aerodynamics of a 55% scale model of a U.S. Air Force/ NASA generic fighter configuration, this scheme provided accurate models of the lift, drag, and pitching-moment coefficients. Using only 50% of the data acquired during, the wind tunnel test, the trained neural network had a predictive accuracy equal to or better than the accuracy of the experimental measurements.
Document ID
19970021749
Acquisition Source
Ames Research Center
Document Type
Technical Memorandum (TM)
Authors
Ross, James C. (NASA Ames Research Center Moffett Field, CA United States)
Jorgenson, Charles C. (NASA Ames Research Center Moffett Field, CA United States)
Norgaard, Magnus (Technical Univ. of Denmark Lyngby, Denmark)
Date Acquired
September 6, 2013
Publication Date
May 1, 1997
Subject Category
Aerodynamics
Report/Patent Number
A-976463
NASA-TM-112193
NAS 1.15:112193
Accession Number
97N22647
Funding Number(s)
PROJECT: RTOP 519-20-22
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.

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