Representative-in-class vehicles for fleet-level aviation noise analysis
Representative-in-class vehicles for fleet-level aviation noise analysis
Global air traffic demand is projected to nearly double by 2035 (7.2 billion passengers) compared to the 3.8 billion passengers in 2016. At such a growth rate, the aviation sector might cause an important detriment of the welfare of those living around airports via a substantial increase in noise. For addressing such a concern, the aviation industry is required to assess a significant number of aviation scenarios, involving different technology platforms and operational procedures, in order to define the strategies that ensure the higher reduction in aircraft noise impact. A common approach to reduce the combinatorial nature of fleet-level studies and enable more flexibility for exploring multiple aviation scenarios, is to simplify the fleet into a number of representative-in-class vehicles that capture the noise performance of the various classes within the fleet. In this paper, a statistical classification process is implemented for reducing the UK commercial fleet into a number of representative-in-class vehicles based on aircraft noise characteristics. The optimal number of representative-in-class aircraft is analysed for three airports in the UK (London Gatwick, Heathrow and Stansted), with significant differences in aircraft movements and fleet composition, on the basis of the accuracy vs. computational time when calculating noise contour areas. Finally, it is discussed the use of these representative-in-class vehicles as baseline models for projecting the reduction in aviation noise impact with future technology implementation.
Aviation Noise, Aviation noise impact, Noise modelling, Statistical classification
1264-1271
International Institute of Acoustics and Vibration
Torija, Antonio J.
6dd0d982-fcd6-42b6-9148-211175fd3287
Self, Rod H.
8b96166d-fc06-48e7-8c76-ebb3874b0ef7
2018
Torija, Antonio J.
6dd0d982-fcd6-42b6-9148-211175fd3287
Self, Rod H.
8b96166d-fc06-48e7-8c76-ebb3874b0ef7
Torija, Antonio J. and Self, Rod H.
(2018)
Representative-in-class vehicles for fleet-level aviation noise analysis.
In 25th International Congress on Sound and Vibration 2018, ICSV 2018: Hiroshima Calling.
vol. 2,
International Institute of Acoustics and Vibration.
.
Record type:
Conference or Workshop Item
(Paper)
Abstract
Global air traffic demand is projected to nearly double by 2035 (7.2 billion passengers) compared to the 3.8 billion passengers in 2016. At such a growth rate, the aviation sector might cause an important detriment of the welfare of those living around airports via a substantial increase in noise. For addressing such a concern, the aviation industry is required to assess a significant number of aviation scenarios, involving different technology platforms and operational procedures, in order to define the strategies that ensure the higher reduction in aircraft noise impact. A common approach to reduce the combinatorial nature of fleet-level studies and enable more flexibility for exploring multiple aviation scenarios, is to simplify the fleet into a number of representative-in-class vehicles that capture the noise performance of the various classes within the fleet. In this paper, a statistical classification process is implemented for reducing the UK commercial fleet into a number of representative-in-class vehicles based on aircraft noise characteristics. The optimal number of representative-in-class aircraft is analysed for three airports in the UK (London Gatwick, Heathrow and Stansted), with significant differences in aircraft movements and fleet composition, on the basis of the accuracy vs. computational time when calculating noise contour areas. Finally, it is discussed the use of these representative-in-class vehicles as baseline models for projecting the reduction in aviation noise impact with future technology implementation.
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Published date: 2018
Venue - Dates:
25th International Congress on Sound and Vibration 2018: Hiroshima Calling, ICSV 2018, , Hiroshima, Japan, 2018-07-08 - 2018-07-12
Keywords:
Aviation Noise, Aviation noise impact, Noise modelling, Statistical classification
Identifiers
Local EPrints ID: 427299
URI: http://eprints.soton.ac.uk/id/eprint/427299
PURE UUID: 9e1e887a-ac05-455a-a79d-f4ad0c48ab06
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Date deposited: 11 Jan 2019 17:30
Last modified: 17 Mar 2024 12:17
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Contributors
Author:
Antonio J. Torija
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