| タイトル | Neural Network Prediction of New Aircraft Design Coefficients |
| 本文（外部サイト） | http://hdl.handle.net/2060/19970023478 |
| 著者（英） | Ross, James C.; Jorgensen, Charles C.; Norgaard, Magnus |
| 著者所属（英） | NASA Ames Research Center; Technical Univ. of Denmark |
| 発行日 | 1997-05-01 |
| 言語 | eng |
| 内容記述 | This paper discusses a neural network tool for more effective aircraft design evaluations during wind tunnel tests. Using a hybrid neural network optimization method, we have produced fast and reliable predictions of aerodynamical coefficients, found optimal flap settings, and flap schedules. For validation, the tool was tested on a 55% scale model of the USAF/NASA Subsonic High Alpha Research Concept aircraft (SHARC). Four different networks were trained to predict coefficients of lift, drag, moment of inertia, and lift drag ratio (C(sub L), C(sub D), C(sub M), and L/D) from angle of attack and flap settings. The latter network was then used to determine an overall optimal flap setting and for finding optimal flap schedules. |
| NASA分類 | Aeronautics (General) |
| レポートNO | 97N23806
NASA-TM-112197
A-976719
NAS 1.15:112197 |
| 権利 | No Copyright |
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