Predictions of LAGOON Nose Landing Gear Aeroacoustics using Wall-Modeled Large Eddy Simulations

Speaker: Man-Long Wong, Science and Technology Corporation / NASA Ames Research Center
August 30, 2022

Presentation

Abstract

Wall-modeled large-eddy simulations (WMLESs) of the LAGOON nose landing gear are conducted with compressible Navier–Stokes equations and immersed boundary technique using the Launch, Ascent, and Vehicle Aerodynamics (LAVA) framework. The simulations are conducted using six different Cartesian octree meshes for the grid sensitivity analysis of the near-field and far-field numerical predictions, where the far-field noise results are computed with the Ffowcs Williams-Hawkings acoustic analogy. The effects of numerical tripping induced at the exact locations of the tripping devices in the experiments are also examined. In general, better comparison with the experimental results are shown for the near-field results obtained with the simulations under the effects of numerical tripping. The effects of tripping are not significant on the far-field noise calculations and the results have reasonable comparison with the experimental data in the low and medium frequency ranges when an impermeable formulation of the acoustic analogy is used.

This work is supported by NASA’s Advanced Air Transport Technology (AATT) and Transformational Tools and Technologies (T³) projects.

Biography

Man-Long Wong is a research scientist of the Science and Technology Corporation. He joined the Launch, Ascent and Vehicle Aerodynamics (LAVA) group of the Computational Aerosciences Branch of the NASA Advanced Supercomputing (NAS) Division at NASA Ames Research Center in April 2019. Man-Long worked on a wide range of projects, including the development of robust compressible multi-phase methods for launch environment simulations with the water-based sound suppression system, investigations on the use of wall-modeled large-eddy simulations for aerodynamics and aeroacoustics predictions, and refactoring of the Cartesian adaptive mesh refinement software for high-performance computing. He obtained a PhD degree in Aeronautics and Astronautics from the Stanford University in 2019 where his thesis work focused on the development of high-order shock-capturing methods and the study of shock-induced multi-species turbulent mixing using large-scale adaptive mesh refinement simulations.

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