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.


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    Title :

    The use of artificial intelligence for buffet environments


    Additional title:

    Der Gebrauch von künstlicher Intelligenz zur Voraussage von Flatterschwingungen an Tragflächen und Höhenleitwerken


    Contributors:


    Publication date :

    1993


    Size :

    9 Seiten, 14 Bilder, 2 Tabellen, 10 Quellen


    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




    THE USE OD ARTIFICIAL INTELLIGENCE FOR BUFFET ENVIRONMENTS

    JACOBS, J. / HEDGECOCK, C. / LICHTENWALNER, P. et al. | AIAA | 1993



    Use of artificial neural networks for buffet environments

    Jacobs, J. H. / Hedgecock, C. E. / Lichtenwalner, P. F. et al. | AIAA | 1994


    The characterization of non-stationary buffet environments

    Dima, Crin / Jacobs, Jack | AIAA | 1995


    The characterization of non-stationary buffet environments

    Dima, C. / Jacobs, J.H. | Tema Archive | 1995