Jet Mixing Noise from Single Stream Jets using Stochastic Source Modeling

Abstract

This work deals with the simulation of jet mixing noise in the time domain using stochastic source modeling. The sound sources are generated by means of the RPM (Random Particle M esh) method, which uses turbulence statistics gained from RANS data. The generated stochastic sound sources closely realize the two-point cross-correlation function proposed by Tam & Auriault (T&A) to describe the statistics of a fine-scale jet mixing noise source. By modeling the sound source in the time domain a direct (primal) prediction method of the T&A approach is realized. The methodology followed in this work allows to evaluate noise spectra at any position in the computational domain based on one CAA computation. The aspired goal is to prove numerically the ability of our time-domain model to give similar predictions as the genuine T&A approach. The realization of an eight power Mach number scaling law for the emitted sound is verified. Furthermore, it is demonstrated that jet similarity spectra are obtained with a time marching CAA code. At 90◦ to the jet axis good agreement with the G-Spectrum is found. The stochastic approach is slightly modified to enable Strouhal similarity for the peak level of the jet-noise spectra at different jet velocities. The appropriate scaling behavior is demonstrated numerically. The spectra are realized in a Mach number range between 0.3 and 0.9 for Strouhal numbers ranging from 0.01 to 10. The RANS solution to a single stream cold jet configuration is obtained for a set of subsonic Mach numbers using the DLR solver TAU.