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Neural Network Based Aerodynamic Flight Simulation

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: N/A
Agency Tracking Number: 41037
Amount: $99,744.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: N/A
Solicitation Number: N/A
Timeline
Solicitation Year: N/A
Award Year: 1998
Award Start Date (Proposal Award Date): N/A
Award End Date (Contract End Date): N/A
Small Business Information
500 Wynn Drive
Huntsville, AL 35816
United States
DUNS: N/A
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Norman O. Speakman
 (205) 772-7200
Business Contact
Phone: () -
Research Institution
N/A
Abstract

The objective of this effort is to develop a neural network-based aerodynamic prediction model of an existing weapon systems and integrate this model into a PC-based six degree-of-freedom (6DOF) simulation of the weapon. Two extensive sets of wind tunnel data have been used to demonstrate the potential of using Artificial Neural Network programs as limited aerodynamic prediction codes. The algorithm used in this preliminary work is a feed-forward, back propagation network with special techniques implemented to aid the convergence to minimum error. The wind tunnel data sets used in the present training process contained several orientation/environmental variables, several geometric variables and, of course, the normal aerodynamic coefficients as outputs. The assessment of the accuracy of the neural network algorithm shows the predicted coefficient values to be in very good agreement with actual wind tunnel data with an average mean square error for each coefficient to be less than one part in one thousand for most of the data. Comparison with Missile Datcom for some of the configurations clearly demonstrate the potential of developing a generic aerodynamic prediction code suitable for use in a flight simulation.

* Information listed above is at the time of submission. *

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