Development of Wavelet Stochastic Estimation for Identifying the Contribution of Turbulent Structures to the Sound Field of Shear Flows

Fundamental understanding of noise generation and the development of noise reduction technology requires the development of tools that can analyze simultaneously the relationship between the turbulent flow field and the pressure field both near and far. In this proposal we will demonstrate how Wavelet Stochastic Estimation (WSE) is the most optimal method for correlating the source region to the sound field when using a microphone array and Particle Image Velocimetry. WSE first transforms the far-field pressure signal into the wavelet domain which then enables both temporal and frequency information to be correlated with the flow field. By adding the frequency information to the correlations, it becomes easier to extract the contribution from the large-scale structures and thus relate their dynamics to noise generation. We also demonstrate how WSE can be used with flow structure identification methods, such as the Proper Orthogonal Decomposition (POD), to further improve the link between the sound field and the turbulent flow field. The proposed technology supports the Fundamental Aeronautics Program by improving noise prediction and measurement methods. The technology will be available for both subsonic and supersonic vehicles, with particular emphasis on noise sources generated from shear flows.