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Fuzzy modelling of fuel consumptions and emissions for optimal navigation of a BOEING-747 aircraft

  • Lohithaksha Maiyar
    ,
  • Olusayo Obajemu
    ,
  • Mahdi Mahfouf
    ,
  • Changjiang He
    ,
  • David J.A. Allerton
    ,
  • Jun Chen
Research Output:
Chapter in Book/Report/Conference proceeding
Conference contribution
 Peer-review

Abstract

Air traffic at many airports around the world is expected to grow, more often than not and at near capacity. Investing in new infrastructure is an option albeit relatively long-term but making a better use of existing facilities is an even better short and mid-term solution. Although Aircraft ground movement represents only a fraction of overall operations, optimal airport taxiing in terms of fuel burn and CO emission can contribute significantly to running costs and environment impact. Hence, with the view of optimising ground movement at busy airports, this research paper investigates a new framework for utilising model predictions to optimise the planning of taxiing operations of a BOEING-747 Aircraft. Research studies relating to how fuel consumption and emissions models (such as Carbon monoxide oxides of Nitrogen) are carried out. Specifically, the use of fuzzy-logic based models via quantitative data for the successful prediction of fuel consumption and CO emissions is explored in the paper. The fuzzy models are accurate, transparent but most importantly are capable of dealing with uncertainties, normally present in the system, intrinsically. These models and analyses will be integrated into a future study involving the development of optimal taxiing and navigation algorithms and to which the development of accurate models of aircraft fuel consumptions and emissions is crucial.

Publication Information

Output type

Research Output:
Chapter in Book/Report/Conference proceeding
Conference contribution
 Peer-review

Original language

English

Publication milestones

  • Published - 21/08/2020

Publication status

Published - 21/08/2020

Publisher

IEEE Computer Society, United States

ISBN (Print)

9781728127347

Publication IDs

  • handle.net: 10547/625018
  • Scopus: 85092582663

Host publication title

2020 IEEE Aerospace Conference

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