Hostname: page-component-745bb68f8f-d8cs5 Total loading time: 0 Render date: 2025-02-04T16:50:30.075Z Has data issue: false hasContentIssue false
  • English
  • Français

New altitude optimisation algorithm for the flight management system CMA-9000 improvement on the A310 and L-1011 aircraft

Published online by Cambridge University Press:  27 January 2016

R. S. Félix Patrón ,
R. M. Botez  and
D. Labour
R. S. Félix Patrón
Affiliation:
ETS, University of Quebec, Automated Production Engineering, Montreal, Quebec, Canada
R. M. Botez*
Affiliation:
ETS, University of Quebec, Automated Production Engineering, Montreal, Quebec, Canada
D. Labour
Affiliation:
CMC Electronics-Esterline, 600 Dr Frederik Philips Boulevard, Saint Laurent, Quebec, Canada
*
Ruxandra.Botez@etsmtl.ca
Get access Rights & Permissions [Opens in a new window]

Abstract

The current flight management system (FMS), CMA-9000, from CMC Electronics-Esterline, only optimises the vertical flight profile in terms of the speed of the aircraft. This article defines a methodology that optimises the speeds and altitudes for the vertical profile, obtaining a trajectory that reduces the global flight cost.

The performance database (PDB) provided by CMC Electronics-Esterline is presently used on aircraft such as the Lockheed L-1011, the Airbus A310 and the Sukhoi Superjet 100 Russian regional jet. The PDB is used as the reference to design different trajectory optimisation algorithms to obtain the altitude where the aircraft fuel efficiency is the best. These algorithms are compared with the part-task trainer (PTT), simulator that represents the FMS CMA-9000, supplied by CMC Electronics-Esterline as well.

To validate the results, the FlightSIM® software is used, which considers a complete aircraft aerodynamic model for its simulations, giving accurate results and very close to reality.

Type
Research Article
Information
The Aeronautical Journal , Volume 117 , Issue 1194 , August 2013 , pp. 787 - 805
Copyright
Copyright © Royal Aeronautical Society 2013 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Avery, D. The evolution of fight management systems, IEEE Software, 2011, 28, (1), pp 11–13.Google Scholar
2. Herndon, A.A., Cramer, M. and Nicholson, T. Analysis of advanced fight management systems (FMS), fight management computer (FMC) feld observations trials; Lateral and vertical path integration, 28th Digital Avionics Systems Conference (DASC): Modernization of Avionics and ATM-Perspectives from the Air and Ground, 2009, 25-29 October 2009, pp 1.C.21–1.C.216, Institute of Electrical and Electronics Engineers, Orlando, FL, USA.Google Scholar
3. Schoemig, E.G. ET AL. 3D path concept and fight management system (FMS) trades, 2006, IEEE/AIAA 25th Digital Avionics Systems Conference, 15-19 October 2006, p 12, IEEE, Piscataway, NJ, USA.Google Scholar
4. Clarke, J.-P.B. ET AL. Continuous descent approach: Design and fight test for Louisville International Airport, J Aircr, 2004, 41, (5), pp 1054–1066.Google Scholar
5. Tong, K.-O. ET AL. Descent profile options for continuous descent arrival procedures within 3D path concept, 2007, 26th DASC Digital Avionics Systems Conference — 4-Dimensional Trajectory-Based Operations: Impact on Future Avionics and Systems, 21-25 October 2007, pp 3A31–3A311, Institute of Electrical and Electronics Engineers, Dallas, TX, USA.Google Scholar
6. Reynolds, T.G., Ren, L. and Clarke, J.-P.B. Advanced noise abatement approach activities at Nottingham East Midlands Airport, 2007, UK, USA/Europe Air Traffc Management R&D Seminar, pp 1–10.Google Scholar
7. Stell, L. Analysis of fight management system predictions of idle-thrust descents, 2010, 29th Digital Avionics Systems Conference: Improving Our Environment through Green Avionics and ATM Solutions, DASC 2010, 3-9 October 2010 pp 1.E.21–1.E.213, Institute of Electrical and Electronics Engineers, Salt Lake City, UT, USA,.Google Scholar
8. Lovegren, J. Estimation of potential aircraft fuel burn reduction in cruise via speed and altitude optimization strategies, 2011, Department of Aeronautics and Astronautics, p 97, Massachusetts Institute of Technology, Cambridge, MA, USA.Google Scholar
9. Campbell, S. Multi-scale path planning for reduced environmental impact of aviation, 2010, p 190, University of Illinois at Urbana-Champaign, Illinois, USA.Google Scholar
10. Visintini, A.L. ET AL. Monte Carlo optimization for confict resolution in air traffc control, Intelligent Transportation Systems, IEEE Transactions, 2006, 7, (4), pp 470–482.Google Scholar
11. Kouba, G. Calcul des trajectoires utilisant les algorithmes genetiques en trois dimensions pour un avion modelise en six dimensions, 2010 p 93, Ecole de Technologie Superieure (Canada): Canada.Google Scholar
12. Venkataraman, P. Applied optimization with MAtLAB programming. Second edition, 2009, xvi, p 526, John Wiley & Sons, Hoboken, NJ, USA.Google Scholar
13. ICAO. Aviation’s contribution to climate change, Environmental Report, 2010, p 260, International Civil Aviation Organization: Montreal, Canada.Google Scholar
14. Langlet, B. Étude d’Optimisation du Profl Vertical de Vol du FMS CMA-9000 de CMC Electronics, Langlet, B. Editor, 2011, École de technologie supérieure, Montréal, Canada.Google Scholar