Numerical Jet Noise Prediction for Passive Noise Reduction Means

Abstract

Efficient prediction of passive noise reduction means for jet noise was the main objective in the joint research project OPTITHECK. Numerical nozzle design tools for industry require short computational time and must provide enough prediction sensitivity in noise level differences even for small geometry variations. Concerning these demands, a broadband jet noise prediction concept was recently successfully validated. The hybrid approach includes the following parts: Firstly a RANS computation which provides mean flow and statistical turbulence quantities. Secondly, fluctuating modal sound sources derived from the Tam & Auriault cross-correlation model, efficiently obtained from 2-D stochastic realizations using the Random Particle Method (RPM). Thirdly, a Computational Aeroacoustics (CAA) prediction step utilizing the fluctuating sound sources. In this step the near-field solution of jet noise is aquired, which is extrapolated into far-field by the Ffowcs-Williams and Hawkings method to obtain a spectrum that can be compared with far-field measurements.