Cooperative experimental and analytical research between McDonnell Douglas Aerospace (MDA) and NASA Langley Research Center (NASA LaRC) has led to an artificial intelligence procedure for predicting empennage buffeting pressures and elastic response as a function of upstream flow field and geometric conditions. This research program is a continuing MDA effort to develop a unified buffet design methodology. The current effort employs a combined neural network and finite element modeling method to predict flexible tail response based on rigid pressure information. This method is dependent on experimental data to train the neural network algorithms but is robust enough to expand its knowledge base with additional aircraft data. Initial results show an incredible potential to predict accurate RMS and frequency dependent tail pressures as well as flexible response while providing the future capability to incorporate upstream CFD data for advanced design aircraft buffet pressure predictions.
The use of artificial intelligence for buffet environments
Der Gebrauch von künstlicher Intelligenz zur Voraussage von Flatterschwingungen an Tragflächen und Höhenleitwerken
1993
9 Seiten, 14 Bilder, 2 Tabellen, 10 Quellen
Conference paper
English
Luftfahrttechnik , analytische Untersuchung , experimentelle Untersuchung , Tragflügel , Flugzeugsteuerwerk , Schütteln (Aerodynamik) , technische Entwicklung , künstliche Intelligenz , Expertensystem , Profilumströmung , Prognose , Druckbeanspruchung , Profil (Kontur) , Strömungsfeld , Entwurfsparameter , Finite-Elemente-Methode , Algorithmentheorie , Leitwerk (Flugzeug) , technische Beschreibung
Use of Artificial Neural Networks for Buffet Environments
Online Contents | 1994
|The characterization of non-stationary buffet environments
Tema Archive | 1995
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