A surrogate artificial neural network/machine learning model was developed to predict the acoustic interaction for a fixed-pitch rotor in proximity to a downstream cylindrical airframe typical of small Unmanned Aerial System (sUAS) platforms. The model was trained to predict the acoustic waveform under representative hover conditions as a function of rotational speed, airframe proximity, and observer angle. Training data were acquired in an anechoic chamber on both isolated rotors and rotor-airframe configurations. Acoustic amplitude and phase of the revolution-averaged interaction were predicted, which required up to 25 harmonics to capture the impulse event caused by the blade’s approach and departure from the airframe. Prediction performance showed, on average, that the models could estimate the acoustic amplitude and phase over the relevant harmonics for unseen conditions with 86% and 75% accuracy, respectively, enabling a time domain reconstruction of the waveform for the range of geometric and flow parameters tested.


    Zugriff

    Zugriff über TIB

    Verfügbarkeit in meiner Bibliothek prüfen


    Exportieren, teilen und zitieren



    Titel :

    An Artificial Neural Network Approach to Predict Rotor-Airframe Acoustic Waveforms


    Beteiligte:

    Kongress:

    AIAA Aviation Forum and Exposition ; 2023 ; San Diego, CA, US


    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Keine Angabe


    Sprache :

    Englisch




    An Artificial Neural Network Approach to Predict Rotor-Airframe Acoustic Waveforms

    Arthur D. Wiedemann / Christopher Fuller / Kyle Pascioni | NTRS


    An Artificial Neural Network Approach to Predict Rotor-Airframe Acoustic Waveforms

    Wiedemann, Arthur D. / Fuller, Christopher / Pascioni, Kyle A. | AIAA | 2023


    sUAS Rotor-Airframe Interaction

    Whelchel, Jeremiah / Alexander, William N. | AIAA | 2021


    Coupled rotor/airframe vibration analysis

    Sopher, R. / Studwell, R.E. / Cassarino, S. et al. | Tema Archiv | 1982