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Stefan J. Letica

Publications and source records attributed to Stefan J. Letica.

Prediction of Noise-Power-Distance Data for Urban Air Mobility Vehicles

In contrast to most commercial air traffic today, vehicles serving the urban air mobility (UAM) market are anticipated to operate within communities and be close to the public at large. The approved model for assessing environmental impact of air traffic actions in the United States, the Federal Aviation Administration (FAA) Aviation Environmental Design Tool (AEDT), does not directly support analysis of such operations due to a combined lack of UAM aircraft flight performance model data and aircraft noise data. This paper addresses the latter by offering two prediction-based approaches for generation of noise-power-distance (NPD) data for use within AEDT. One utilizes the AEDT fixed-wing aircraft modeling approach and the other utilizes the AEDT rotary-wing aircraft modeling approach.

noise-power-distance↗

Annoyance Model Assessments of Urban Air Mobility Vehicle Operations

As urban air mobility (UAM) vehicles begin to enter service, consideration must be given to vertiport siting and flight routing to help reduce community noise impact and promote adoption by the community. Notwithstanding environmental justice concerns, a possible early strategy is to operate out of existing heliports and fly along established helicopter routes, many of which follow uninhabited waterways and/or roadways (with the thought for the latter that an already noisy ambient environment will mask the sound of the aircraft). However, no prior annoyance model has been fielded that both takes audibility into account and that may be applied to such a real-world strategy. This paper reviews a recently developed annoyance model that includes audibility as a factor and applies it to two flight operations. A simple overflight case is first undertaken to demonstrate the approach. A point-to-point operation in the New York City area is then considered to demonstrate how annoyance varies across an urban soundscape. The cases considered use modeled UAM vehicle noise propagated to a set of ground observers as the signals and either recorded or modeled ambient acoustic data as the maskers.

aircraft community noise↗

Annoyance Model Assessments of Urban Air Mobility Vehicle Operations

As urban air mobility (UAM) vehicles begin to enter service, consideration must be given to vertiport siting and flight routing to help reduce community noise impact and promote adoption by the community. Notwithstanding environmental justice concerns, a possible early strategy is to operate out of existing heliports and fly along established helicopter routes, many of which follow uninhabited waterways and/or roadways (with the thought for the latter that an already noisy ambient environment will mask the sound of the aircraft). However, no prior annoyance model has been fielded that both takes audibility into account and that may be applied to such a real-world strategy. This paper reviews a recently developed annoyance model that includes audibility as a factor and applies it to two flight operations. A simple overflight case is first undertaken to demonstrate the approach. A point-to-point operation in the New York City area is then considered to demonstrate how annoyance varies across an urban soundscape. The cases considered use modeled UAM vehicle noise propagated to a set of ground observers as the signals and either recorded or modeled ambient acoustic data as the maskers.

aircraft community noise↗

A Synthesis Method to Auralize Rotor Noise During Transitional Flight

This paper describes a new capability to synthesize Urban Air Mobility (UAM) vehicle rotor/propeller loading and thickness noise and broadband self noise for transitional flight auralization. A vehicle smoothly transitioning between flight states is approximated here as transitioning in time through a series of trimmed steady-state flight conditions. To generate information that helps synthesize loading and thickness noise for transitional flight, this paper describes the development of a tool that combines blade loading, motion, and geometry data from a series of separate trimmed flight states. This paper also describes a method to combine broadband self noise acoustic predictions, each representing a separate trimmed flight state, to synthesize broadband self noise for transitional flight. Different flight profiles of the six-passenger NASA UAM Quadrotor reference vehicle demonstrate these capabilities, with each profile specified by a series of trimmed flight states.

Auralization↗