Accurate and efficient prediction of propeller acoustic performance is of paramount importance to the analysis and design of various urban air mobility concepts that have emerged over the last decade. Leveraging modern machine learning techniques of transfer learning (TL) and active learning (AL), we present a multifidelity data-driven framework that uses a combination of simulation and experiment data to train a neural network (NN) model for predicting the tonal noise of two-bladed propellers at multiple far-field observer locations given the airfoil profile, pitch-to-diameter ratio, forward speed, and rotational speed of the propeller. The NN model is first trained using a large number of inexpensive low-fidelity simulations and then enhanced by a small number of high-fidelity aeroacoustic wind tunnel measurements using TL. Two additional rounds of wind-tunnel experiments with new propeller designs are then suggested by an AL algorithm designed to effectively reduce the predictive error of the NN model. Results from held-out validation indicate that the NN trained with both low- and high-fidelity data based on TL delivers better performance than those trained solely with low- or high-fidelity data, and the inclusion of high-fidelity data selected by AL further improves the predictive accuracy.


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    Titel :

    Data-Driven Propeller Noise Prediction Approach with Transfer and Active Learning


    Beteiligte:
    Zhou, Beckett Y. (Autor:in) / Hanson, Liam (Autor:in) / Pullin, Shaun F. (Autor:in) / Zang, Bin (Autor:in) / Hauth, Jeremiah (Autor:in) / Huan, Xun (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    01.07.2025




    Medientyp :

    Aufsatz (Konferenz) , Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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