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Estimating Aircraft Fuel Consumption using Radar Tracks Data

Volume 14, Number 10, October 2018, pp. 2249-2260
DOI: 10.23940/ijpe.18.10.p1.22492260

Fangzi Liua, Chao Wangb, and Lei Wangc

aCivil Aviation College, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China
bCollege of Air Traffic Management, Civil Aviation University of China, Tianjin, 300300, China
cHuabei Air Traffic Management Authority, Tianjing, 300450, China

(Submitted on May 26, 2018; Revised on July 8, 2018; Accepted on August 16, 2018)

Abstract:

For accurately measuring the energy-saving contribution of air traffic management technology on air transportation, this paper proposed a calculation method of fuel consumption in the air traffic control area based on radar tracks. This paper firstly analyzed nine influencing factors, including aircraft type, flight state, true airspeed, and altitude, that could affect aircraft fuel consumption. Taking air traffic trajectory data as input, a fuel flow time series prediction model based on echo state network was built. The predicted approximate error of the model can reach 0.032%, 1.79%, and -1.11% in level flight, climbing state, and descending state, respectively. Due to aircraft weight and missed calibrated airspeed data in radar tracks, a key influencing factors extraction method for fuel consumption based on sensitivity analysis has been further explored. Input parameters of the ESN fuel flow time series approximate model have been simplified reasonably. The Xiamen ATC area was taken as an example, and the total fuel consumption of 1021 flights on a specific day within the Xiamen control area was calculated to be 1044.84 tons. Research results in this paper will construct a technical foundation for measuring air traffic control system performance through implementation of the ASBU plan.

 

References: 19

              1. Air Traffic Management Bureau of CAAC, “Special Development Plan of Energy Conservation for Air Traffic Management System,” 2015
              2. B. P. Collins, “Estimation of Aircraft Fuel Consumption,” Journal of Aircraft, Vol. 19, No. 11, pp. 969-975, 1982
              3. P. Lathasree and R. M. Sheethal, “Estimation of Aircraft Fuel Consumption for a Mission Profile Neural Networks,” in Proceedings of the Sixth World Congress on Intelligent Control and Automation, pp. 8687-8691, 2006
              4. D. A. Senzig, G. G. Fleming, and R. J. Iovinelli, “Modeling of Terminal-Area Airplane Fuel Consumption,” Journal of Aircraft, Vol. 46, No. 4, pp. 1089-1093, 2009
              5. Z. Q. Wei and C. Wang, “Estimating of Pollution Emissions for Scheduled Flight in Different Phases,” Journal of Traffic and Transportation Engineering, Vol. 10, No. 6, pp. 48-52, 2010
              6. E. T. Turgut and M. A. Rosen, “Relationship Between Fuel Consumption and Altitude for Commercial Aircraft During Descent: Preliminary Assessment with a Genetic Algorithm,” Aerospace Science & Technology, Vol. 17, No. 1, pp. 65-73, 2012
              7. T. Baklacioglu, “Fuel Flow-Rate Modelling of Transport Aircraft for the Climb Flight using Genetic Algorithms,” Aeronautical Journal-New Series, Vol. 119, No. 1212, pp. 173-183, 2015
              8. ICAO, “Environmental Protection, Volume 2: Aircraft Engine Emissions. International Standards and Recommended Practices,” 2008
              9. Eurocontrol Experimental Center, “User Manual for the Base of Aircraft Data (BADA),” 2008
              10. J. J. Lee, I. A. Waitz, B. Y. Kim, G. G. Fleming, L. Maurice, and C. A. Holsclaw, “System for Assessing Aviation’s Global Emissions (SAGE), Part 2: Uncertainty Assessment,” Transportation Research Part D Transport & Environment, Vol. 12, No. 6, pp. 381-395, 2007
              11. A. S. David and G. Gregg, “Fleming Modeling of Terminal-Area Airplane Fuel Consumption,” Journal of Aircraft, Vol. 46, No. 6, pp. 1089-1093, 2009
              12. H. Jaeger and H. Haas, “Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Telecommunication,” Science, Vol. 308, No. 5667, pp. 78-80, 2004
              13. Z. W. Shi and M. Han, “Support Vector Echo-State Machine for Chaotic Time-Series Prediction,” IEEE Transactions on Neural Networks, Vol. 18, No. 2, pp. 359-372, 2007
              14. F. Zhai, X. Lin, Z. Yang, and Y. Song, “Notice of Retraction Financial Time Series Prediction based on Echo State Network,” in Proceedings of International Conference on Natural Computation, pp. 3983-3987, IEEE, 2010
              15. S. Varshney and T. Verma, “Half Hourly Electricity Load Prediction Using Echo State Network,” International Journal of Science and Research, Vol. 3, No. 6, pp. 885-888, 2014
              16. F. M. Bianchi, E. de Santis, and A. Rizzi, “Short-Term Electric Load Forecasting Using Echo State Networks and PCA Decomposition,” Access IEEE, Vol. 3, pp. 1, 2015
              17. D. Q. Zhao, J. N. Chen, and H. Z. Wang, “Local Sensitivity Analysis for Pollution Simulation of Urban Rainfall-Runoff,” Acta Scientiae Circumstantiae, Vol. 29, No. 6, pp. 1170 -1177, 2009
              18. J. Zador, I. G. Zsely, and T. Turanyi, “Local and Global Uncertainty Analysis of Complex Chemical Kinetic Systems,” Reliability Engineering System Safety, Vol. 91, pp. 1232-1240, 2006
              19. F. H. Hao, X. Y. Ren, and X. S. Zhang, “Uncertain Affecting Factor of the Non-Point Source Pollution Load,” China Environmental Science, Vol. 24, No. 3, pp. 270-274, 2004

                           

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