This paper documents input data conditioning, input parameter selection, structure, training, and validation of neural network models of the Active Aeroelastic Wing aircraft. Neural networks can account for uncharacterized nonlinear effects and retain generalization capability. Model inputs include aircraft rates, accelerations, and control surface positions. Linear loads models were developed for network training starting points. The models were trained with rolls, loaded reversals, windup turns, and individual control surface doublets for load excitation. Data results from all loads models at Mach 0.90 and altitude of 15,000 ft. show an average model prediction error reduction of 18.6 percent.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Modeling Aircraft Wing Loads from Flight Data Using Neural Networks*†


    Additional title:

    Sae Technical Papers


    Contributors:

    Conference:

    World Aviation Congress & Exposition ; 2003



    Publication date :

    2003-09-08




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




    Modeling Aircraft Wing Loads from Flight Data Using Neural Networks

    Allen, Michael J. / Dibley, Ryan P. | NTRS | 2003



    Modeling Aircraft Wing Loads from Flight Data Using Neural Networks

    Allen, M. J. / Dibley, R. P. / Society of Automotive Engineers | British Library Conference Proceedings | 2003


    Flight Loads Prediction of High Aspect Ratio Wing Aircraft Using Multibody Dynamics

    Michele Castellani / Jonathan E. Cooper / Yves Lemmens | DOAJ | 2016

    Free access