Predicting noise for full-scale distributed propulsion aircraft by synthesizing subscale data with existing prediction tools

Future projections of the economic impact of Urban Air Mobility (UAM) or Advanced Air Mobility (AAM) are bold with global GDP increases ranging from billions to trillions over the next few decades. While the potential opportunities are vast, there will be a paradigm shift in the sheer volume of flights at lower altitudes near populated communities. While there is still debate on the initial markets, there is a strong consensus that these aircraft must not appreciably contribute to the existing soundscape and ideally would be imperceptible in most cases. The novel designs of many distributed propulsion aircraft are challenging the existing noise prediction tools that were primarily built around propellers, jet aircraft, and rotorcraft. This paper will present a summary of recent research efforts on synthesizing subscale experimental acoustics measurement data with noise propagation tools to compute community noise exposure on the ground for different full-scale vehicle configurations and flight profiles. The benefits, drawbacks, and limitations of each data source will be discussed along with suggestions for how to improve the prediction methods and models by synthesizing multiple data sets.