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Aero-propulsive modelling for climb and descent trajectory prediction of transport aircraft using genetic algorithms

Published online by Cambridge University Press:  27 January 2016

T. Baklacioglu*
Affiliation:
Anadolu University, Faculty of Aeronautics and Astronautics, Eskisehir, Turkey
M. Cavcar
Affiliation:
Anadolu University, Faculty of Aeronautics and Astronautics, Eskisehir, Turkey

Abstract

In this study, a new aero-propulsive model (APM) was derived from the flight manual data of a transport aircraft using Genetic Algorithms (GAs) to perform accurate trajectory predictions. This new GA-based APM provided several improvements to the existing models. The use of GAs enhanced the accuracy of both propulsive and aerodynamic modelling. The effect of compressible drag rise above the critical Mach number, which was not included in previous models, was considered along with the effects of compressibility and profile camber in the aerodynamic model. Consideration of the thrust dependency with respect to Mach number and the altitude in the propulsive model expression was observed to be a more practical approach. The proposed GA model successfully predicted the trajectory for the descent phase, as well, which was not possible in previous models. Close agreement was observed when comparing the time to climb and time to descent values obtained from the model with the flight manual data.

Type
Research Article
Copyright
Copyright © Royal Aeronautical Society 2014 

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References

1. Cavcar, M. and Cavcar, A. Aero-propulsive modeling of transport aircraft for air traffic management applications, AIAA Guidance, Navigation and Control Conference and Exhibit, AIAA Paper 2004-4792, Providence, Rhode Island, USA, 16-19 August 2004.Google Scholar
2. Nuic, A., Poinsot, C., Iagaru, M.G., Gallo, E., Navarro, F.A. and Querejeta, C. Advanced aircraft performance modeling for ATM: Enhancements to the BADA model, 24th Digital Avionics System Conference, Washington DC, USA, 30 October – 3 November 2005.Google Scholar
3. Gong, C. and Chan, W.N. Using flight manual data to derive aero-propulsive models for predicting aircraft trajectories, AIAA Aircraft Technology, Integration and Operations Conference, Los Angeles, USA, 2002.Google Scholar
4. Bukkapatnam, S.T.S. and Sadananda, K. A genetic algorithm for unified approach-based predictive modeling of fatigue crack growth, Int J Fatigue, 2005, 27, pp 13541359.Google Scholar
5. Zalzala, A.M.S. and Fleming, P.J. Genetic Algorithms in Engineering Systems, IEE Control Engineering Series 55, London, UK, 1997.Google Scholar
6. Gen, M. and Cheng, R. Genetic Algorithms and Engineering Design, John Wiley & Sons, New York, USA, 1997.Google Scholar
7. Sakawa, M. Genetic Algorithms and Fuzzy Multiobjective Optimization, Kluwer Academic Publishers, Massachusetts, USA, 2002.Google Scholar
8. Pal, S.K. and Wang, P.P. Genetic Algorithms for Pattern Recognition, CRC Press, Boca Raton, Florida, USA, 1996.Google Scholar
9. User Manual for the Base of Aircraft Data (BADA), Revision 3.11, 2013, URL: http://www.eurocontrol.int/sites/default/fles/content/documents/sesar/bada-revision-summary-3.11.pdf (cited 30 August 2013).Google Scholar
10. Mccormick, B.W. Aerodynamics, Aeronautics and Flight Mechanics, John Wiley & Sons, New York, 1979.Google Scholar
11. Cavcar, A. Constant altitude-constant Mach number cruise range of transport aircraft with compressibility effects, J Aircraft, 2006, 43, (1), pp 125131.Google Scholar
12. Boeing 737-400 Operations Manual, Boeing Commercial Airplane Group, Seattle, Washington, USA, 1988.Google Scholar
13. CFM56-3 Technology, CFM International, URL: http://www.cfmaeroengines.com/engines/cfm56-3#technology (cited 30 Aug. 2013)Google Scholar
14. Getting to Grips with Aircraft Performance, Airbus, 2002.Google Scholar