Auralization of Rotorcraft Periodic Flyover Noise from Design Predictions

Abstract

This paper combines the synthesis and propagation of rotorcraft sounds, or auralizations, with their noise predictions and design optimization to incorporate human response into the process of creating low-noise rotorcraft. The NASA Auralization Framework is described and used to auralize the sounds of an AS350 helicopter main rotor at a ground observer from predicted acoustic pressure time histories. This paper uses spherical phase interpolation as part of the sound synthesis. This interpolation is an improvement over the linear phase interpolation described in previous work, which can introduce artifacts in the auralized sound. The effects of noise prediction source resolution are discussed. Two AS350 helicopter main rotor planform optimizations using certification noise metrics as the objective function are performed. One objective function is the Effective Perceived Noise Level metric and the other is the A-weighted Sound Exposure Level metric. The planform optimization includes determining trim state, predicting and propagating source noise, and changing design variables to affect the noise metric. Auralizations of the rotor sounds are computed, and due to their time domain nature, enable a more thorough exploration of sound quality metrics applied to the rotor sounds than traditional 1/3-octave predictions. These other metrics can be used to assess community annoyance and help develop human perception metrics that can potentially be incorporated into the helicopter design cycle.