High-Resolution Source Characterization and Modeling for Efficient Prediction of Propulsion-Airframe Aeroacoustics

ATA Engineering, Inc. (ATA) and partners propose a Phase II SBIR project to further develop methods to efficiently characterize and predict noise performance of aircraft with substantial propulsion airframe aeroacoustics (PAA) effects. The methods utilize near-field surface source models informed by high-spatial-resolution acoustic measurements. Over the last decade, ATA has matured the multireference continuous-scan (CS) acoustic measurement technology that is needed to define and validate such source models. Previous demonstrations of CS measurements include beamforming, near-field acoustical holography, and turbofan tone order tracking. In Phase I, the team applied such measurements to canonical experiments along with a small-scale ducted fan using fixed and scanning sensors in the near, mid, and far field to define stochastic source models. These models supported novel acoustic shielding predictions by directly detecting the wavepacket-like nature of acoustic events that propagate to the far field as well as using this information to define surface-based source models to predict noise shielding/scattering from PAA using the boundary element method (BEM). Additionally, a 60-channel 2D rotating array characterized the sound field generated by a speaker with and without scattering bodies at an unprecedented high-resolution of nearly 10,000 virtual sensors. This provided a clear visualization of the interference patterns of a complex sound field in the presence of a rigid body and demonstrated the ability to couple isolated source characterization measurements to BEM for PAA problems. In Phase II, ATA proposes to extend the methods to more complex geometries in order to (1) define an efficient BEM-based noise prediction process that utilizes PAA sources derived from high-resolution CS measurements and/or state-of-the-art predictive tools, and (2) integrate the process into far-field noise prediction frameworks such as NASArsquo;s Aircraft NOise Prediction Program (ANOPP).