The analytical model for leading-edge noise prediction formulated by Amiet, developed for a flat plate, relates the far-field acoustic pressure to the upstream inflow conditions, modeled by canonical turbulence spectra. The inaccurate results provided by this low-fidelity method when applied to thick airfoils has been attributed to the distortion experienced by turbulent structures when approaching the airfoil, not modeled in the original formulation of Amiet. The first attempts to account for the effects of this physical mechanism consisted of modifying the term representing the incoming turbulence by means of the analytical results of the rapid distortion theory, obtaining a promising improvement of the noise-prediction accuracy. This paper aims to set up the physical framework to investigate the relation between turbulence distortion and noise-generation mechanisms with the purpose of enhancing inflowturbulence noise modeling. A numerical database obtained for a rod-airfoil configuration has been chosen to allow the analysis of the vortex dynamics when interacting with a body. The analysis of the velocity field near the leading edge has highlighted that the extension of the region where turbulence distortion occurs depends on the size of the incoming turbulence structures. Furthermore, surface pressure fluctuations have been observed to peak at the same position along the airfoil where the pressure gradient in the streamwise direction is maximum. A novel approach has been proposed to account for turbulence distortion in Amiet’s model by using as input the turbulence spectrum directly sampled in this position. A satisfactory agreement with the prediction provided by the solid formulation of the Ffowcs-Williams and Hawkings analogy has been obtained.

Turbulence-distortion analysis for leading-edge noise-prediction enhancement / Piccolo, Andrea; Zamponi, Riccardo; Avallone, Francesco; Ragni, Daniele. - ELETTRONICO. - (2023). (Intervento presentato al convegno AIAA AVIATION 2023 Forum tenutosi a San Diego, CA and Online nel 12-16 June 2023) [10.2514/6.2023-3628].

Turbulence-distortion analysis for leading-edge noise-prediction enhancement

Avallone, Francesco;
2023

Abstract

The analytical model for leading-edge noise prediction formulated by Amiet, developed for a flat plate, relates the far-field acoustic pressure to the upstream inflow conditions, modeled by canonical turbulence spectra. The inaccurate results provided by this low-fidelity method when applied to thick airfoils has been attributed to the distortion experienced by turbulent structures when approaching the airfoil, not modeled in the original formulation of Amiet. The first attempts to account for the effects of this physical mechanism consisted of modifying the term representing the incoming turbulence by means of the analytical results of the rapid distortion theory, obtaining a promising improvement of the noise-prediction accuracy. This paper aims to set up the physical framework to investigate the relation between turbulence distortion and noise-generation mechanisms with the purpose of enhancing inflowturbulence noise modeling. A numerical database obtained for a rod-airfoil configuration has been chosen to allow the analysis of the vortex dynamics when interacting with a body. The analysis of the velocity field near the leading edge has highlighted that the extension of the region where turbulence distortion occurs depends on the size of the incoming turbulence structures. Furthermore, surface pressure fluctuations have been observed to peak at the same position along the airfoil where the pressure gradient in the streamwise direction is maximum. A novel approach has been proposed to account for turbulence distortion in Amiet’s model by using as input the turbulence spectrum directly sampled in this position. A satisfactory agreement with the prediction provided by the solid formulation of the Ffowcs-Williams and Hawkings analogy has been obtained.
2023
978-1-62410-704-7
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2979286