Influence of turbulence statistics on stochastic jet-noise prediction with synthetic eddy method

The separation algorithms that solve a flowfield and a sound field separately are used to estimate the jet noise accurately while suppressing the increase of computational cost. In this study, the synthetic eddy method (SEM) was applied to a strong shear flow to simulate a major sound source of jet noise, and the influence of turbulence statistics used in the SEM on the predicted far-field jet noise was investigated. The divergence-free SEM was employed as a reconstruction method of the turbulent field, and the shear effect due to the background flow was provided to the generated velocity field. In addition, the dissipation effect of the turbulence in the shear layer was modeled by decorrelating the generated velocity fluctuations. The estimated noise using the linearized Euler simulations appeared larger than the experimental value when parameters of theSEMwere set based on actual flow conditions. In contrast, the sound pressure spectra agreed well with the experiment by increasing the time scale of the noise source. With the present stochastic noise prediction framework based on the SEM, turbulence statistics and far-field sound pressure spectra were correlated, which would help to modify parameters in stochastic noise generation methods to achieve desired far-field sound pressure spectra.