Helicopters provide exceptional mission flexibility but are limited by the noise generated by their rotors, especially the blade–vortex interaction (BVI) noise. Optimizing flight paths to minimize the noise impact is crucial to address this issue. However, predicting ground noise footprint for various flight paths requires considerable computational resources. This paper proposes a time-efficient framework to predict ground noise footprint by using a reduced-order model (ROM). The framework enables ground noise footprint prediction efficiently for a given operating condition, allowing assessment of the ground noise impact for the given trajectory. Using this framework, path optimization was conducted for three different cases with varying variables and constraints to minimize ground noise impact. Information about the aerodynamic load distributions and blade passage is required to analyze noise as various metrics at numerous locations and frequencies. The aerodynamic load distributions in the normal, chord, and span directions calculated by high-fidelity unsteady Reynolds-averaged Navier–Stokes simulations were modeled for five variables comprising operating conditions and control inputs. The local ROMs were constructed to mitigate the decreased prediction accuracy of the global ROM due to the inclusion of data both with and without BVI phenomena. Validation results showed that the framework yields accurate aerodynamic load distribution predictions with a 3% of ε and aerodynamic coefficients within 3%. The predicted average sound pressure level and BVI noise demonstrated errors within 0.53 and 0.65 dB, respectively.


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    Titel :

    Time-Efficient Aerodynamic Load Prediction Using Reduced-Order Modeling for Ground Noise Calculation


    Weitere Titelangaben:

    J. Aerosp. Eng.


    Beteiligte:
    Kwon, Soonmoung (Autor:in) / Kang, Yu-eop (Autor:in) / Hong, Yoonpyo (Autor:in) / Yee, Kwanjung (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    01.09.2025




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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