The market for new vertical takeoff and landing vehicles, including autonomous urban air taxis and drones for applications such as package delivery, imaging, and surveillance, is growing rapidly. However, aerodynamic noise continues to be the biggest roadblock to community acceptance and adoption. To predict the aerodynamic noise generated by an isolated quadcopter drone, derived from from first principles, we used the Lattice Boltzmann flow solver within NASA’s Launch Ascent and Vehicle Aerodynamics (LAVA) solver framework. The solver’s computational efficiency, and the complete absence of labor-intensive manual volume mesh generation in the workflow, are key to making routine aeroacoustic analysis of urban air taxis and drones from first principles possible.


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    Titel :

    Predicting Quadcopter Drone Noise Using the Lattice Boltzmann Method


    Beteiligte:
    Cadieux, Francois (Autor:in) / Barad, Michael (Autor:in) / Jensen, James (Autor:in) / Kiris, Cetin (Autor:in)

    Kongress:

    SC19 (The International Conference for high Performance Computing, Networking, Storage, and Analysis) ; 2019 ; Denver, CO, United States


    Erscheinungsdatum :

    17.11.2019


    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Keine Angabe


    Sprache :

    Englisch


    Schlagwörter :


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    Predicting Quadcopter Noise With The Lattice-Boltzmann Method

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    An Efficient Automatic Packet Delivery Using Quadcopter Drone

    Wisudawan, Hasbi N. P. / Nugraha, Aditya / Akmal, Alif Miftahul | IEEE | 2024



    Predicting noise spectrum of a small drone rotor in a confined environment: a lattice Boltzmann Vles analysis

    Colombo, Riccardo / Pii, Lorenzo Maria / Romani, Gianluca et al. | TIBKAT | 2023

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