Aircraft classification for efficient modelling of environmental noise impact of aviation

Torija Martinez, AJ ORCID: https://orcid.org/0000-0002-5915-3736 and Self, RH 2018, 'Aircraft classification for efficient modelling of environmental noise impact of aviation' , Journal of Air Transport Management, 67 , pp. 157-168.

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Abstract

With the environmental externalities of civil aviation under unprecedented scrutiny, and with the projected significant increase in air traffic demand over the next few decades, fleet-level studies are required to assess the potential benefit of novel aircraft technologies and operational procedures for minimizing environmental impact of aviation. Using a statistical classification process, the UK commercial aircraft fleet is reduced to four representative- in-class aircraft on the basis of aircraft physical characteristics, and aircraft noise and engine exhaust emissions. These four representative aircraft, that appropriately capture the noise and emissions characteristics for each category within the UK commercial fleet, are also selected to be used as baseline cases for the high-level assessment of the environmental benefit of novel aircraft technologies. For the particular case of aviation noise, the modelling tools are highly sensitive to the number of aircraft types in the flight schedule. A reduction of about 80% in computational time with relatively minor decrease in accuracy (between −4% and +5%) is observed when the whole aircraft fleet is replaced with the four representative-in-class aircraft for computing noise contours. Therefore, the statistical classification and selection of representative-in-class aircraft presented in this paper is a valid approach for the rapid and accurate computation of a large number of exploratory cases to assess aviation noise reduction strategies.

Item Type: Article
Schools: Schools > School of Computing, Science and Engineering
Journal or Publication Title: Journal of Air Transport Management
Publisher: Elsevier
ISSN: 0969-6997
Related URLs:
Funders: Engineering and Physical Sciences Research Council (EPSRC), Innovate UK
Depositing User: Dr Antonio J Torija Martinez
Date Deposited: 02 Dec 2019 09:12
Last Modified: 03 Jan 2020 16:01
URI: http://usir.salford.ac.uk/id/eprint/53196

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