Evaluation of the SNGR method for efficient aerodynamic noise simulation.
The DSNG method reduces CPU costs by more than one order of magnitude compared to high-cost simulations like LES.
In aerodynamic noise prediction, applying a synthetic turbulence reconstruction model requires minimal computational resources, making it very attractive from an industrial perspective. In this case study, the SNGR (Stochastic Noise Generation and Radiation) method was evaluated for its application in various industries for robust and efficient aerodynamic noise simulation. The SNGR method has been found to lack convergence in that it does not model the continuous energy transport characteristic of turbulent cascades. Additionally, the energy transport process significantly reduces the temporal correlation of turbulent structures, which is particularly relevant to sound radiation.
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To solve this problem, we developed a modified model called Damped Stochastic Noise Generation (DSNG) and incorporated it into FINE/Acoustics. The DSNG method reduces CPU costs by more than one order of magnitude compared to high-cost simulations like LES. In particular, it reconstructs turbulent fields and related sound sources based on cost-effective steady RANS simulations. This allows designers to efficiently utilize this method using computational resources available in the industry. Convergence tests showed that the new formulation yields fully converged results, which align well with experimental results from jet noise cases and slotted wings.
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