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

Community noise reduction is a critical focus of NASA ARMD, which is studying highly integrated airframe/propulsion systems such as the hybrid wing body (HWB) and the quiet supersonic technology (QueSST) demonstrator. Relative to conventional aircraft, these systems have closer coupling between sound generation and propagation, creating challenges for noise prediction but also opportunities for shielding. ATA Engineering, in collaboration with the University of California, Irvine, proposes to develop methods to efficiently characterize and ultimately predict aircraft noise associated with such propulsion airframe aeroacoustics (PAA).The methods will utilize near-field surface source models that are informed by high-spatial-resolution, fixed receiver and continuous-scan array acoustic measurements. In Phase I, the team will apply such measurements to canonical experiments and propulsion simulators (e.g, fan or jet exhaust noise) using fixed and scanning sensors to define stochastic source models. These models will support improved acoustic shielding predictions by directly detecting the mechanisms that propagate sound to the far field and using this information to define surface-based source models to predict noise shielding/scattering of integrated propulsion-airframe configurations using boundary element methods (BEM).Phase II will involve creating a database of surface-based engine source models based on both experiments and CFD to apply the models to an acoustic design and optimization trade study. Additionally, the team will pursue direct measurement of high-resolution source surface models for use in multi-fidelity noise prediction frameworks such as NASArsquo;s ANOPP/ANOPP2. The expected outcome is a novel, efficient means to quantify community noise from integrated airframe-propulsion systems containing significant PAA. This capability will allow NASA and industry to perform acoustic design and optimization of configurations like HWB and QueSST.