Urban Air Mobility operations presents new challenges to ensure the safe and efficient operations for both commercial and military applications. Similar to manned aviation, these include communications assurance, navigation accuracy and surveillance (CNS) to ensure airspace safety but at altitudes that traditional CNS systems do not scale down to. Crucially, UAM needs to address the significant challenges related to aircraft noise which remains a high barrier for adoption of VTOL operations for commercial and military use. Modeling acoustic signatures of VTOL vehicles is a computationally intensive time consuming process. This research will apply an innovative model to quantify noise in near real time as an operational constraint along with CNS and other ground and air based risks. This will be applied to end-to-end mission modeling, operations design and mission planning in densely populated environments. The goal of this research is to demonstrate the feasibility of an integrated computationally efficient noise model for eVTOL aircraft in a robust commercial urban air mobility modeling, simulation and analysis tool to enable efficient, multiconstraint operations analysis and mission planning. The integrated noise model will provide the ability for optimization of low noise flight paths in multi-objective, diverse constraint mission planning with other operational and policy constraints like communications assurance, navigation dilution of precision, surveillance coverage, energy usage, air risks and ground-based risks. The simulation and planning tool will enable tradeoff analysis of these multiple constraints while planning for operations in highly complex urban environments. The Analytical Graphics Inc. (AGI) Systems Tool Kit (STK) software will be used to model the UAM platform, payloads (CNS) and operating environment with a high degree of fidelity to quantify and demonstrate the value of noise as a constraint. The semi analytical eVTOL noise prediction model will be used to characterize the changes in acoustic radiation for a representative eVTOL aircraft as the vehicles flight condition is varied. These predicted data will then be used to construct a surrogate representation of the vehicles acoustic state (frequency, amplitude, direction) as a function of its flight condition. Strategies will be developed to generate low noise path flights while minimizing the computational cost of evaluating the vehicles acoustic emissions.
