Neural Network Prediction of New Aircraft Design Coefficients
作者： Charles C. Jorgensen , James C. Ross , Peter Magnus Nørgård
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摘要: This paper discusses a neural network tool for more effective aircraft design evaluations during wind tunnel tests. Using hybrid optimization method, we have produced fast and reliable predictions of aerodynamical coefficients, found optimal flap settings, schedules. For validation, the was tested on 55% scale model USAF/NASA Subsonic High Alpha Research Concept (SHARC). Four different networks were trained to predict coefficients lift, drag, moment inertia, lift drag ratio (C(sub L), C(sub D), M), L/D) from angle attack settings. The latter then used determine an overall setting finding
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