Evaluation of jet noise prediction capabilities of stochastic and statistical models
Keywords:Acoustic noise, Acoustic noise measurement, Spectrum analysis, Stochastic systems, Turbulence models, Velocity, Aerodynamic properties, Conserve energy, Differentiability, Fourier modes, Jet noise, Jet noise prediction, Lighthill analogy, Probabilistic law, Random functions, Statistical models, Stochastic field, Stochastic noise generation and radiation methods, Turbulent fields, Turbulent velocity, Velocity field, Von karman spectrums
AbstractThe Stochastic Noise Generation and Radiation (SNGR) method is based on Kraichnan's turbulence model and expresses the turbulent velocity as a summation of Fourier modes for each turbulent scale. The von Karman spectrum is used to get the turbulent velocity of each scale and random functions following probabilistic laws determine the direction and phase. For the SNGR methods, both the one point spectral analysis and the rms statistics of the velocity field were verified. Two different methods based on RANS flow fields have been implemented and tested to predict jet noise. For the SNGR method, since the aerodynamic properties of the turbulent field have been validated and corrected to conserve energy, the noise over-prediction seen in the use of the Lighthill analogy is traced to the lack of differentiability of the stochastic field that will require some future regularization.
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