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Promet - Traffic&Transportation journal

Accelerating Discoveries in Traffic Science

Accelerating Discoveries in Traffic Science

PUBLISHED
10.08.2019
LICENSE
Copyright (c) 2024 Lisha Ye, Li Cao, Xuhui Wang

Evaluating Fuel Consumption for Continuous Descent Approach Based on QAR Data

Authors:

Lisha Ye
Nanjing University of Aeronautics and Astronautics

Li Cao
Nanjing University of Aeronautics and Astronautics

Xuhui Wang
China Academy of Civil Aviation Science and Technology

Keywords:fuel consumption, continuous descent approach, flight data

Abstract

Fuel savings are a significant aspect for evaluating the current and future technologies of civil aviation. Continuous-Descent Approach (CDA), as a representative of new concepts, requires a method for evaluating its fuel benefits. However, because of unavailability of the practical operational data, it is difficult to validate whether the previous fuel consumption mechanisms are suitable. This paper presents a unique method for quantifying potential fuel benefits. This permits an easy evaluation for the new procedures without modelling before implementing field tests. The proposed method is detailed in this paper. It derives from the inherent mechanical characteristic of aircraft engine, and utilizes historical flight data, rather than modelling, to predict fuel flow rates by matching flight conditions from Quick Access Recorder (QAR) data. The result has been shown to predict fuel consumption for conventional descent with the deviation of ±0.73%. To validate such method, a case study for our designed CDA procedure is presented. Fuel consumptions in baseline scenarios are estimated to analyse the variable impacts on fuel consumption. The estimated fuel benefits are consistent with the results in the previous field tests. This analysis helps support Air Traffic Management decisions on eventual field test by reducing the validation time and cost.

References

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How to Cite
Ye, L. (et al.) 2019. Evaluating Fuel Consumption for Continuous Descent Approach Based on QAR Data. Traffic&Transportation Journal. 31, 4 (Aug. 2019), 407-421. DOI: https://doi.org/10.7307/ptt.v31i4.2933.

SPECIAL ISSUE IS OUT

Guest Editor: Eleonora Papadimitriou, PhD

Editors: Marko Matulin, PhD, Dario Babić, PhD, Marko Ševrović, PhD


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