scholarly journals Selection of Vertiports Using K-Means Algorithm and Noise Analyses for Urban Air Mobility (UAM) in the Seoul Metropolitan Area

2021 ◽  
Vol 11 (12) ◽  
pp. 5729
Author(s):  
Junyoung Jeong ◽  
Minjun So ◽  
Ho-Yon Hwang
Keyword(s):  
Metropolitan Area ◽  
Noise Exposure ◽  
Satellite Image ◽  
Design Tool ◽  
Urban Air ◽  
Real World Data ◽  
Seoul Metropolitan Area ◽  
Shortest Distance ◽  
Selection Of ◽  
Environmental Design Tool

In this study, a combination of well-established algorithms and real-world data was implemented for the forward-looking problem of future vertiport network design in a large metropolitan city. The locations of vertiports were selected to operate urban air mobility (UAM) in the Seoul metropolitan area based on the population of commuters, and a noise priority route was created to minimize the number of people affected by noise using Aviation Environmental Design Tool (AEDT) software. Demand data were analyzed using survey data from the commuting population and were marked on a map using MATLAB. To cluster the data, the K-means algorithm function built in MATLAB was used to select the center of the cluster as the location of the vertiports, and the accuracy and reliability of the clustering were evaluated using silhouette techniques. The locations of the selected vertiports were also identified using satellite image maps to ensure that the location of the selected vertiports were suitable for the actual vertiport location, and if the location was not appropriate, final vertiports were selected through the repositioning process. A helicopter model was then used to analyze the amount of noise reduction achieved by the noise priority route, which is the route between the selected K-UAM vertiports compared to the shortest distance route. As a result, it was shown that the noise priority route that minimized the amount of noise exposure was more efficient than the business priority routes.

Comparison of two community noise models applied to a NASA urban air mobility concept vehicle

2021 ◽  
Vol 263 (6) ◽  
pp. 787-798
Author(s):  
Juliet Page ◽  
Stephen A. Rizzi ◽  
Rui Cheng
Keyword(s):  
Noise Exposure ◽  
Simulation Method ◽  
Design Tool ◽  
Noise Model ◽  
Urban Air ◽  
Noise Power ◽  
Modeling Tools ◽  
Community Noise ◽  
Propagation Modeling ◽  
Concept Vehicle

Predictions of community noise exposure from the NASA urban air mobility (UAM) concept vehicles have been conducted for representative operations using the FAA Aviation Environmental Design Tool (AEDT) in order to demonstrate modeling tool interoperability and assess applicability, capabilities and limitations of integrated noise modeling tools. To both quantify limitations and highlight other capabilities, a comparative analysis is performed using a time simulation method, in particular, using the Volpe Advanced Acoustic Model (AAM). Starting with the same source noise model, the 3D directivity of a UAM concept vehicle is predicted in terms of aeroacoustic pressure time histories at a sphere of observers near the vehicle. In addition to distilling those data to a set of noise-power-distance data for input to AEDT, the data are processed preserving directivity, into narrowband, one-twelfth and one-third octave bands for input to AAM. Results from AEDT and AAM modeling are provided for a variety of metrics to demonstrate the effect that source noise and propagation modeling fidelity have on predicted results at receptors over a study area.

Community noise assessment of urban air mobility vehicle operations using the FAA Aviation Environmental Design Tool

2021 ◽  
Vol 263 (6) ◽  
pp. 450-461
Author(s):  
Stephen Rizzi ◽  
Menachem Rafaelof
Keyword(s):  
Environmental Design ◽  
The United States ◽  
Air Traffic ◽  
Performance Model ◽  
Design Tool ◽  
Urban Air ◽  
Initial Development ◽  
Noise Data ◽  
Fleet Operations ◽  
Environmental Design Tool

In contrast to most commercial air traffic today, vehicles serving the urban air mobility (UAM) market are anticipated to operate in communities 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's Aviation Environmental Design Tool (AEDT), does not support analysis of such operations due to a combined lack of a UAM aircraft performance model and aircraft noise data. This paper discusses the initial development of a method to assess the acoustic impact of UAM fleet operations on the community using AEDT and demonstrates its use for representative UAM operations. In particular, methods were developed using fixed-point flight profiles and user-supplied noise data in a manner that avoids unwanted behavior in AEDT. A set of 32 routes in the Dallas-Ft. Worth area were assessed for single and multiple (fleet) operations for two concept vehicles.

Validation of the aviation environmental design tool's noise model using high fidelity weather

2021 ◽  
Vol 263 (2) ◽  
pp. 4810-4822
Author(s):  
Ana Gabrielian ◽  
Tejas Puranik ◽  
Mayank Bendarkar ◽  
Michelle Kirby ◽  
Dimitri Marvis
Keyword(s):  
Real World ◽  
Noise Exposure ◽  
Environmental Design ◽  
Design Tool ◽  
Noise Model ◽  
Weather Data ◽  
High Fidelity ◽  
Real World Data ◽  
Community Noise ◽  
Fuel Burn

To enable sustainable aviation growth, mitigation of environmental effects must be developed in parallel. To further this effort, these effects are modeled using capabilities such as the Aviation Environmental Design Tool (AEDT), a program that is able to model aircraft performance, fuel burn, emissions, and noise. Past and current projects are performed with the intent of improving the accuracy of the models within AEDT to capture various real-world effects. This paper targets the sensitivity of the noise prediction and propagation by varying multiple assumptions within AEDT. To validate the noise capabilities, multiple streams of real-world data will be used to accurately model actual flights to and from SFO airport. This data includes High-Fidelity weather data,detailed flight performance characteristics from airline flight data records and noise monitoring data obtained from stations around the airport. The results from this study are expected to offer recommendations and help users prioritize and more accurately quantify community noise exposure using AEDT.

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