A fast method for predicting turbofan fan-stage broadband interaction noise is being developed. The downstream propagating acoustic power in the bypass duct due to the response of the fan exit guide vane (FEGV) to fan wake turbulence is computed based on two-dimensional flat-plate cascade analysis and Green’s method. This study focused on using machine learning to define the fan wake parameters used as inputs to the FEGV response and noise calculation. Machine-learning algorithms are being trained using computational fluid dynamics results. This paper describes the accuracy of machine learning given the available rotor wake data. Further, the effect of errors in the learned input data on the acoustic prediction was studied. Based on this study, the method shows great promise.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Machine Learning Aided Low-Order Predictions of Fan Stage Broadband Interaction Noise


    Contributors:

    Published in:

    AIAA Journal ; 62 , 6 ; 2174-2185


    Publication date :

    2024-06-01




    Type of media :

    Conference paper , Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Machine Learning Aided Fan Broadband Interaction Noise Prediction for Leaned and Swept Fans

    Li, Nuo / Winkler, Julian / Reimann, Craig A. et al. | TIBKAT | 2023


    Machine Learning Aided Fan Broadband Interaction Noise Prediction for Leaned and Swept Fans

    Li, Nuo / Winkler, Julian / Reimann, Craig A. et al. | AIAA | 2023



    Fan-stage broadband interaction noise trends

    Li, Nuo / Watchmann, Berkely / Ramsarran, Tyler et al. | AIAA | 2022


    Machine Learning Aided NR-V2X Quality of Service Predictions

    Reyhanoglu, Aslihan / Kar, Emrah / Kumec, Feyzi Ege et al. | IEEE | 2023