Numerical predictions of turbulence/cascade-interaction noise using computational aeroacoustics with a stochastic model - Archive ouverte HAL Access content directly
Journal Articles AIAA Journal Year : 2015

Numerical predictions of turbulence/cascade-interaction noise using computational aeroacoustics with a stochastic model

Abstract

Turbulent-flow interactions with the outlet guide vanes are known to mainly contribute to broadband-noise emission of aeroengines at approach conditions. This paper presents a three-dimensional computational aeroacoustics hybrid method aiming at simulating the aeroacoustic response of an annular cascade impacted by a prescribed homogeneous isotropic turbulent flow. It is based on a time-domain Euler solver coupled to a synthetic turbulence model implemented in the code by means of a suited inflow boundary condition. The fluctuating pressure over the airfoil surface provided by computational aeroacoustics is used as an input to a Ffowcs Williams and Hawkings integral method to calculate the radiated sound field. Euler computations are first validated against an academic computational aeroacoustics benchmark in the case of an harmonic gust interacting with an annular flat-plate cascade. Then, simulations are applied to turbulence–cascade interactions for annular configurations, in uniform and swirling mean flows, and numerical results in terms of sound power spectra in the outlet duct are compared to semi-analytical and numerical solutions, and to an available experiment.
Fichier principal
Vignette du fichier
Polacsek_AIAAJ_2015_postprint.pdf (2.53 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01296921 , version 1 (17-04-2019)

Identifiers

Cite

Cyril Polacsek, Vincent Clair, Thomas Le Garrec, Gabriel Reboul, Marc C. Jacob. Numerical predictions of turbulence/cascade-interaction noise using computational aeroacoustics with a stochastic model. AIAA Journal, 2015, 53 (12), pp.3551-3566. ⟨10.2514/1.J053896⟩. ⟨hal-01296921⟩
168 View
91 Download

Altmetric

Share

Gmail Facebook X LinkedIn More