The present work describes a numerical reproduction of the 22-in source diagnostic test fan rig of the NASA Glenn Research Center. Numerical flow simulations are performed for three different rotor/stator configurations and one rotational speed, representative of an approach operating condition, by using the lattice-Boltzmann solver PowerFLOW. The full stage and nacelle geometries are considered, and results are compared to available measurements. Tripping the rotor blades results in a slightly more accurate noise prediction as a consequence of a more accurate prediction of the velocity fluctuations in the rotor wake over the whole blade span. Fourier circumferential analyses are performed for an intake and a bypass duct section with the intent of explaining the origin of some tonal noise components and comparing the present results to available literature results. Finally, the effects of adding an acoustic treatment in the intake is shown by directly resolving the unsteady flowfield in a single-degree-of-freedom honeycomb layer.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Turbofan Broadband Noise Prediction Using the Lattice Boltzmann Method


    Contributors:
    Casalino, D. (author) / Hazir, A. (author) / Mann, A. (author)

    Published in:

    AIAA Journal ; 56 , 2 ; 609-628


    Publication date :

    2017-09-22


    Size :

    20 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Turbofan Broadband Noise Prediction using the Lattice Boltzmann Method

    Casalino, Damiano / Hazir, Andreas / Mann, Adrien | AIAA | 2016


    Towards Lattice-Boltzmann Prediction of Turbofan Engine Noise

    Casalino, Damiano / Ribeiro, Andre F. / Fares, Ehab et al. | AIAA | 2014



    Aircraft Cabin Outflow Valve Tonal and Broadband Noise Prediction Using the Lattice Boltzmann Method

    Shin, Yoonshik / Polidoro, Francesco / Gonzalez-Martino, Ignacio et al. | AIAA | 2017