Broadband Slat Noise Prediction Based on CAA and Stochastic Sound Sources from a Fast Random Particle-Mesh (RPM) Method

Abstract

The present paper studies the application of a low-cost CAA approach to a slat noise problem. A new fast and cheap stochastic approach is introduced to model the unsteady turbulent sound sources in the slat-cove of a high-lift airfoil. It is based on the spatial convolution of white-noise with a filter kernel and is capable to reproduce target distributions of turbulent kinetic energy and length scales, e.g., provided by a steady RANS solution to the turbulent flow problem. The computational method yields a perfectly solenoidal velocity field that resolves for homogeneous isotropic turbulence exactly the complete second-order two-point velocity correlation tensor. Results for the sound generation at the slat are given for the underlying RANS mean-flow field being based on a Menter SST turbulence model with and without Kato-Launder modification, respectively. Physical meaningful results are obtained for the acoustic characteristics of the slat-noise problem.