Anisotropic source modelling for turbulent jet noise prediction.

Area of Science:

• Aeroacoustics
• Computational Fluid Dynamics (CFD)
• Turbulence Modeling

Background:

• Jet noise prediction methods often rely on simplified turbulence models.
• Accurate modeling of anisotropic turbulence is crucial for precise jet noise prediction.
• Existing models may not fully capture the complex noise generation mechanisms in turbulent jets.

Purpose of the Study:

• To propose a novel anisotropic component for jet noise source modeling.
• To enhance the accuracy of Reynolds-averaged Navier-Stokes (RANS) equation-based jet noise prediction.
• To integrate fine-scale and large-scale turbulent noise sources within an anisotropic framework.

Main Methods:

• Utilized Goldstein's generalized acoustic analogy for noise source modeling.
• Employed the Reynolds stress tensor instead of turbulent kinetic energy to capture anisotropy.
• Applied the Launder-Reece-Rodi (LRR) model with a modified Menter's ω-equation for accurate flow and stress calculations.

Main Results:

• The proposed anisotropic source model demonstrated improved agreement with experimental acoustic data.
• Accurate calculation of mean flow velocities and Reynolds stresses was achieved.
• The model successfully incorporated both fine-scale and large-scale turbulent noise sources.

Conclusions:

• The developed anisotropic jet noise source model offers a more accurate approach to predicting jet noise.
• This advancement contributes to the field of aeroacoustics by refining turbulence-based noise prediction.
• The findings support further research in computational aeroacoustics for noise reduction strategies.