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Enhanced cruise range prediction for narrow-body turbofan commercial aircraft based on QAR data

Published online by Cambridge University Press:  05 November 2020

V.E. Atasoy Open the ORCID record for V.E. Atasoy [Opens in a new window]  and
C. Cetek
V.E. Atasoy*
Affiliation:
Eskişehir Technical UniversityEskişehirTurkey
*
[email protected]
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Abstract

Aircraft performance parameters play a critical role in maintaining economic and environmental sustainability in aviation. Furthermore, the ability to calculate aircraft performance parameters accurately for the cruise range contributes to aviation in areas such as the preliminary design of aircraft and air traffic management. This study is focused on cruise range performance, as this is critical to both the evaluation and understanding of the economic and environmental impacts of commercial aircraft. Quick Access Recorders (QAR) data were used for more accurate analysis of the cruise range. The QAR data used in this study included 6,574 short-distance domestic flights by narrow-body turbofan commercial aircraft between 31 different city pairs. To obtain a more accurate cruise range equation, parameters affecting the cruise range performance were determined and studied. First, the drag polar model was improved to take the cambered profile, compressibility effects and cruise airspeeds of commercial aircraft into consideration using the real flight data. Second, Thrust-Specific Fuel Consumption (TSFC) models were compared and the most suitable one for the cruise phase was selected. After these steps, cruise range values were calculated using the Breguet range equation with these improved parameters. When the results of this enhanced range model were compared with the real flight data, the mean absolute percentage error (MAPE) was found to be 2.5% for all the Aircraft and Engine Type Groups (AETGs) considered in the data. This figure corresponds to a 7.9% smaller error than provided by previous range models based on simple parabolic drag polar and TSFC models. According to these results, the application of a simple parabolic drag polar and TSFC is not appropriate for cruise range calculations.

Keywords

Aircraft performanceAircraft cruise rangeDrag polarThrust-specific fuel consumption
Type
Research Article
Information
The Aeronautical Journal , Volume 125 , Issue 1286 , April 2021 , pp. 672 - 701
Copyright
© The Author(s), 2020. Published by Cambridge University Press on behalf of Royal Aeronautical Society

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References

REFERENCES

ICAO, The annual review 2018 report, 2018. Available at: https://www.icao.int/annual-report-2018/Pages/the-world-of-air-transport-in-2018.aspx (accessed 11 January 2020).Google Scholar
IATA, The annual review 2019 report, 2019. Available at: https://www.iata.org/Contentassets/c81222d96c9a4e0bb4ff6ced0126f0bb/iata-annual-review-2019.pdf (accessed 3 January 2020).Google Scholar
EASA, European aviation environmental report 2019, 2019. Available at: https://ec.europa.eu/transport/sites/transport/files/2019-aviation-environmental-report.pdf (accessed 15 January 2020).Google Scholar
SESAR Consortium, 2006b. SESAR D2 Air transport framework: the performance target, DLM-0607-001-02-00, 2006.Google Scholar
FAA, FAA’s Destination 2025 Strategic Plan, 2019. Available at: https://www.faa.gov/about/plans_reports/media/Destination2025.pdf (accessed 1 January 2020).Google Scholar
Stolzer, A.J. Fuel consumption modeling of a transport category aircraft using flight operations quality assurance data: a literature review, J Air Transp, 2002, 7Google Scholar
Megan, S.R. and Kim, H. The impact of airline mergers and hub reorganization on aviation fuel consumption, J Clean Prod, 2014, 85, pp 395–407.Google Scholar
Turgut, E.T., Cavcar, M., Usanmaz, O., Canarslanlar, A.O., Dogeroglu, T., Armutlu, K. and Yay, O.D. Fuel flow analysis for the cruise phase of commercial aircraft on domestic routes, Aerosp Sci Technol, 2014, 37, pp 1–9.CrossRefGoogle Scholar
Cavcar, A. and Cavcar, M. Impact of aircraft performance differences on fuel consumption of aircraft in air traffic management environment, Aircr Eng Aerosp Technol, 2004, 76, (5), pp 502–515.CrossRefGoogle Scholar
Peckham, D.H. Range performance in cruising flight, Royal Aircraft Establishment Farnborough, No RAE-TR-73164, 1974.Google Scholar
Torenbeek, E. Cruise performance and range prediction reconsidered, Prog Aerosp Sci, 1997, 33, pp 285–321.CrossRefGoogle Scholar
Torenbeek, E. Synthesis of Subsonic Airplane Design, Delft University Press, 1982, Delft, NL.CrossRefGoogle Scholar
Roskam, J. Airplane Design. Part I: Preliminary Sizing of Airplanes, Roskam Aviation, 1985, Ottawa, Kansas.Google Scholar
Raymer, D. Aircraft Design: A Conceptual Approach. American Institute of Aeronautics and Astronautics, 2012, Washington, USA.CrossRefGoogle Scholar
Hale, F.J., Introduction to Aircraft Performance, Selection, and Design, John Wiley & Sons, 1984, New York, USA.Google Scholar
Anderson, J.D. Aircraft Performance and Design, McGraw-Hill, 1999, New York, USA.Google Scholar
Torenbeek, E. and Wittenberg, H. Flight Physics: Essentials of Aeronautical Disciplines and Technology, With Historical Notes. Springer Science & Business Media, 2009.CrossRefGoogle Scholar
Cavcar, A. and Cavcar, M. Approximate solutions of range for constant altitude–constant high subsonic speed flight of transport aircraft, Aerosp Sci Technol, 2004, 8, (6), pp 557–567.CrossRefGoogle Scholar
Gur, O., Mason, W.H. and Schetz, J.A. Full-configuration drag estimation, J Aircr, 2010, 47, (4), pp 1356–1367.CrossRefGoogle Scholar
Bridges, D.H. Thrust and drag models for performance calculations for high-speed aircraft, J Aircr, 2008, 45, (4), 1358–1366.CrossRefGoogle Scholar
El-Sayed, A.F. Aircraft Propulsion and Gas Turbine Engines, CRC Press, 2017, Florida, USA.Google Scholar
Mattingly, J.D., William, H.H. and David, T.P. Aircraft Engine Design, American Institute of Aeronautics and Astronautics, 2002, Virginia, USA.CrossRefGoogle Scholar
Howe, D. Aircraft Conceptual Design Synthesis, Professional Engineering Publishing Limited, 2002, London and Bury St Edmunds, UK.Google Scholar
Roux, E. Pour Une Approche Analytique De La Dynamique Du Vol, PhD thesis November 2005. l’Ecole Nationale Superieure de l’Aeronautique et de l’Espace SUPAERO-ONERA. Toulouse, France.Google Scholar
Torenbeek, E. Synthesis of Subsonic Airplane Design, Springer, 1982, NL, Delft.CrossRefGoogle Scholar
Martinez-Val, R. and Perez, E. Optimum cruise lift coefficient initial design of jet aircraft, J Aircr, 1991, 29, (4), pp 712–714.CrossRefGoogle Scholar
Randle, W.E., Hall, C.A. and Vera-Morales, M. Improved range equation based on aircraft flight data, J Aircr, 2011, 48, (4), pp 1291–1298.CrossRefGoogle Scholar
Poll, D.I.A. The optimum aeroplane and beyond, Aeronaut J, 2009, 113, (1141), pp 151–164.CrossRefGoogle Scholar
Hale, F.J. Aircraft Performance Selection and Design, Wiley, 1984, New York, USA.Google Scholar
Wang, L., Ren, Y. and Wu, C. Effects of flare operation on landing safety: a study based on ANOVA of real flight data, Saf Sci, 2018, 102, pp 14–25.CrossRefGoogle Scholar
Wang, Q., Wu, K., Zhang, T., Kong, Y. and Qian, W. Aerodynamic modeling and parameter estimation from QAR data of an airplane approaching a high-altitude airport, Chin J Aeronaut, 2012, 25, (3), pp 361–371.CrossRefGoogle Scholar
Geng, H. and Jie, J. Fuel flow regression model of aircraft cruise based on QAR data, Aeroengine, 2008, 34, (4), pp 46–50.Google Scholar
Saarlas, M. Aircraft Performance, John Wiley & Sons, 2006, New Jersey, USA.CrossRefGoogle Scholar
Corda, S. Introduction to Aerospace Engineering with A Flight Test Perspective, John Wiley & Sons, 2017.Google Scholar
ATAG. Aviation Benefits Beyond Borders, ATAG, 2014, Geneva, Switzerland.Google Scholar
Eurocontrol, User Manual for the Base of Aircraft Data (BADA), Revision 3.11, Eurocontrol Experimental Center, Brussels, EEC Note No. 10/04, 2004.Google Scholar
Brandt, S.A., Bertin, J.J., Stiles, R.J. and Whitford, R. Introduction to Aeronautics: A Design Perspective, American Institute of Aeronautics and Astronautics, USA, 2004.CrossRefGoogle Scholar
Young, T.M. Performance of The Jet Transport Airplane: Analysis Methods, Flight Operations, and Regulations, John Wiley & Sons, Hoboken, NJ, 2018.Google Scholar
Airfleets, https://www.airfleets.net/home/ (accessed 24 January 2020).Google Scholar
Roux, E. Turbofan and Turbojet Engines: Database Handbook, Elodie Roux, 2007, France.Google Scholar
De Myttenaere, A., Golden, B., Le Grand, B. and Rossi, F. Mean absolute percentage error for regression models. Neurocomputing, 2016, 192, pp 38–48.CrossRefGoogle Scholar
Hagquist, C. and Stenbeck, M. Goodness of fit in regression analysis – R2 and G2 reconsidered, Qual Quan, 1998, 32, pp 229–245.CrossRefGoogle Scholar