An Approach to Estimate Aircraft Fuel Consumption for the Descent Phase

Year 2018, Volume: 14 Issue: 4, 379 - 383, 28.12.2018

Özlem Şahin , Çerkez Ağayeva

https://doi.org/10.18466/cbayarfbe.405224

Abstract

In this study, the static
(based on Base of Aircraft Data) and the dynamic aircraft fuel consumption
models are constructed for the descent phase. Therefore, two flight
trajectories, from Istanbul to Frankfurt (LTBA- EDDF) and from Los Angeles to
New York (KLAX-KJFK), are chosen in order to analyze the appropriate
relationships between the different parameters such as altitude, speed and
time. A medium range aircraft is taken into consideration while developing
linear and quadratic models. For LTBA- EDDF, fuel consumption as 200 kg, 193,89
kg and 193,58 kg are found for BADA, linear and quadratic models, respectively.
It corresponds to about a 3 % difference from BADA. Furthermore, for KLAX-KJFK,
approximately a 5% difference is obtained.

Keywords

Aircraft, fuel consumption, descent phase, static and dynamic models

References

• 1. Senzig, D.A, Fleming, G.G, Iovinelli, R.J, Modeling of terminal area airplane fuel consumption” Journal of Aircraft, 2009, 46(4), 1089-1093.
• 2. Collins, B., Estimation of aircraft fuel consumption, Journal of Aircraft, 1982, 19(11), 969–975. DOI:10.2514/3.44799.
• 3. Trani, A., Wing-Ho, F, Schilling, G, Baik, H, and Seshadri, A, A Neural network model to estimate aircraft fuel consumption, in 4th AIAA Aviation Technology, Integration and Operations (ATIO) Forum, AIAA, Chicago, USA, 2004.
• 4. Baklacıoglu, T, Modeling the fuel flow-rate of transport aircraft during flight phases using genetic algorithm-optimized neural networks, Aerospace Science and Technology, 2015, 49, 52–62.
• 5. Bartel, M., Young, T.M, Simplified thrust and fuel consumption models for modern two-shaft turbofan engines, Journal Aircraft, 2008, 45 (4), 1450–1456.
• 6. Patterson, J., Noel, G., Senzig, D., Roof, C., Fleming, G. Analysis of departure and arrival profiles using real-time aircraft data, Journal of Aircraft, 2009, 46(4), 1094-1103.
• 7. Malwitz, A, Kim, B, Fleming, G., Lee, J., Balasubramanian, S., Waitz, I., Validation assessment, model assumptions and uncertainties, , SAGE, Version 1.5, FAA, 2005.
• 8. Lee, J., Waitz I., Kim B., Fleming G., Maurice L., and Holsclaw C., System for assessing aviation’s global emissions, part 2: uncertainty assessment, Transportation Research Part D: Transport and Environment, 2007, 12(6), 381–395.
• 9. Hill P, Petersen C., Mechanics and thermodynamics of propulsion, 2nd edn., 1992, Addison-Wesley, Reading, MA.
• 10. Turgut, E.T, Rosen, M.A, Relationship between fuel consumption and altitude for commercial aircraft during descent: preliminary assessment with a genetic algorithm, Aerospace Science Technology, 2012, 17(1), 65–73.
• 11. Stolzer, A.J., Fuel consumption modeling of a transport category aircraft: a flight operations quality assurance (FOQA) analysis, Journal of Air Transportation, 2003, 8(2), 3-18.
• 12. 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, 2002, Los Angeles, USA.
• 13. Allaire, D.L, A Physics-Based Emissions Model for Aircraft Gas Turbine Combustors, Master of Science in Aerospace Engineering at the Massachusetts Institute of Technology, 2006.
• 14. Spencer, K.S, Fuel Consumption Optimization Using Neural Networks and Genetic Algorithms, 2011, Dissertation for the Degree of Master of Aerospace Engineering.
• 15. Ryerson, M.S., Hansen M, Bonn J, Validating Aircraft Performance Models with Airline Data, International Conference on Research in Air Transportation, 2012, University of California, Berkeley.
• 16. Chati, Y.S, Balakrishnan, H., Aircraft Engine Performance Study Using Flight Data Recorder Archives, AIAA Aviation Conference, 2013, Los Angeles, California.
• 17. www.flightaware.com (accessed 10.08.2016).