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Fuel flow-rate modelling of transport aircraft for the climb flight using genetic algorithms

Published online by Cambridge University Press:  27 January 2016

T. Baklacioglu
T. Baklacioglu*
Affiliation:
Department of Aircraft Airframe and Engine Maintenance, Faculty of Aeronautics and Astronautics, Anadolu University, Eskisehir, Turkey
*
[email protected]
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Abstract

In this study, development of a new fuel flow rate model for the climbing phase of flight was achieved using a genetic algorithm (GA) method. Two modelling approaches were performed using real flight data records (FDRs) from a medium-weight transport-category aircraft. The first model considered the dependency of fuel consumption only with respect to altitude, whereas the effects of both altitude and true airspeed (TAS) were included in the second model. The proposed models are improvements on existing models because the relationship between fuel flow rate, flight altitude, and TAS can be deduced using the derived formulations. Both modelling approaches were found to provide accurate results after performing an error analysis for fuel flow rate values. It was clear that incorporating the TAS effect into the second model enhanced the accuracy of the model, but the first model was also found to be appropriate for practical usage.

Type
Research Article
Information
The Aeronautical Journal , Volume 119 , Issue 1212 , February 2015 , pp. 173 - 183
Copyright
Copyright © Royal Aeronautical Society 2015

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References

1.Cavcar, M. and Cavcar, A.Aero-propulsive modelling 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.McCormick, B.W.Aerodynamics, Aeronautics and Flight Mechanics, 2nd ed, John Wiley & Sons, New York, USA, 1995.Google Scholar
3. Boeing, Fuel Conservation Strategies: Takeoff and Climb, Aero Q408, URL: http://www.boeing.com/com mercial/aeromagazine/articles/qtr_4_08/pdfs/AERO_Q408_article05.pdf (cited 8 September 2014)Google Scholar
4.Turgut, E.T.Estimating aircraft fuel flow for a three-degree flight-path-angle descent, J Aircraft, 2011, 48, (3), pp 1099–1106.CrossRefGoogle Scholar
5. Airbus, Getting to Grips with Fuel Economy, Issue 3, July 2004.Google Scholar
6.Collins, B.Estimation of aircraft fuel consumption, J Aircr, 1982, 19, (11), pp 969–975.CrossRefGoogle Scholar
7.Trani, A, Wing-Ho, F, Schilling, G, Baik, H and Seshadri, A.A neural network model to estimate aircraft fuel consumption, 4th AIAA Aviation Technology, Integration and Operations (ATIO) Forum, AIAA Paper 2004-6401, Chicago, USA, 20–22 September 2004.CrossRefGoogle Scholar
8.Lathasree, P and Sheethal, R.M.Estimation of aircraft fuel consumption for a mission profile using neural networks, International Conference on Aerospace Science and Technology, INCAST Paper 2008-115, Bangaloire, India, 26-28 June 2008.Google Scholar
9.Bartel, M and Young, T.M.Simplified thrust and fuel consumption models for modern two-shaft turbofan engines, J Aircr, 2008, 45, (4), pp 1450–1456.CrossRefGoogle Scholar
10.Senzig, D.A., Fleming, G.G. and Iovinelli, R.J.Modelling of terminal-area airplane fuel consumption, J Aircr, 2009, 46, (4), pp 1089–1093.CrossRefGoogle Scholar
11.Turgut, E.T. and Rosen, M.A.Relationship between fuel consumption and altitude for commercial aircraft during descent: Preliminary assessment with a genetic algorithm, Aerospace Science and Technology, 2012, 17, pp 65–73.CrossRefGoogle Scholar
12. User Manual for the Base of Aircraft Data (BADA), Revision 3.12, 2014, URL: https://www.eurocontrol.int/sites/default/fles/feld_tabs/content/documents/sesar/user-manual-bada-3-12.pdf (cited 8 September 2014)Google Scholar
13.De, S, Ghosh, A and Pal, S.K.Fitness evaluation in genetic algorithms with ancestors’ infuence, Genetic Algorithms for Pattern Recognition, edited by Pal, S.K. and Wang, P.P., CRC Press, Boca Raton, Florida, USA, 1996.Google Scholar
14.Cantu-Paz, E.Efficient and Accurate Parallel Genetic Algorithms, Kluwer Academic Publishers, Massachusetts, USA, 2001.CrossRefGoogle Scholar
15.Sakawa, M.Genetic Algorithms and Fuzzy Multiobjective Optimization, Kluwer Academic Publishers, Massachusetts, USA, 2002.CrossRefGoogle Scholar
16.Saxena, A and Saad, A.Evolving an artifcial neural network classifer for condition monitoring of rotating mechanical systems, J Applied Soft Computing, 2007, 7, (1), pp 441–454.CrossRefGoogle Scholar
17.Michalewicz, Z.Genetic Algorithms + Data Structures = Evolution Programs, 3rd ed, Springer-Verlag Berlin Heidelberg, New York, USA, 1999.Google Scholar
18.Deb, K.Multi-Objective Optimization Using Evolutionary Algorithms, John Wiley & Sons, West Sussex, UK, 2001.Google Scholar
19. Boeing 737-800 Technical Characteristics, URL: http://www.boeing.com/boeing/commercial/737family/pf/pf_800tech.page (cited 8 September 2014)Google Scholar
20.Bukkapatnam, S.T.S. and Sadananda, K.A genetic algorithm for unifed approach-based predictive modelling of fatigue crack growth, Int J Fatigue, 2005, 27, pp 1354–1359.CrossRefGoogle Scholar