Latest revision as of 12:51, 16 February 2021

Abstract

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

Original document

The different versions of the original document can be found in:

• http://dx.doi.org/10.1016/j.jairtraman.2017.12.007

• http://usir.salford.ac.uk/id/eprint/53196/1/Torija-2018-Aircraft-classification-for-efficie.pdf

• https://eprints.soton.ac.uk/416748

• https://eprints.soton.ac.uk/416748/1/JATM_2017_301_Revision_2_V0.pdf

• https://api.elsevier.com/content/article/PII:S0969699717303861?httpAccept=text/xml,

https://api.elsevier.com/content/article/PII:S0969699717303861?httpAccept=text/plain,

http://dx.doi.org/10.1016/j.jairtraman.2017.12.007 under the license https://www.elsevier.com/tdm/userlicense/1.0/

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https://academic.microsoft.com/#/detail/2782215908