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Flight Performance Optimization Considering Environmental Impact Under Multi-RTA Constraints

  • Runping Gu
  • Jie YuanEmail author
  • Xiaolan Han
  • Zhiqiang Wei
  • Na Li
Original Paper
  • 39 Downloads

Abstract

To optimize the flight performance under multi-waypoint required time of arrival constraints, characteristics of the vertical flight trajectory during the en route descent process are studied. A multi-constraint segment sequence model including flight distance, flight altitude and arrival time is constructed. To ensure the rationality of constraints, the rationality detection model for multi-constraint is established. The control variables and their variation ranges are determined by analyzing the optimization process. Mathematical models of optimization objectives are studied from three aspects: fuel economy, greenhouse effect and variation of flight speed. Based on the multi-objective genetic algorithm, the optimization solution model is established. Effects of speed on the objectives are analyzed. The results indicate that the optimization model can effectively optimize the flight parameters with multiple RTA constraints. The impact of aviation on the environment can be effectively reduced. And the optimization method has a good trade-off between fuel consumption and temperature rise by changing weighted factors. Optimization results of the flight parameters are mainly affected by the maximum RTA time constraint. The proposed optimization method provides a reference for the optimization of flight parameters and trajectory under multi-RTA constraints in four dimensions.

Keywords

Flight performance Required time of arrival Multi-objective genetic algorithm Vertical profile 

List of Symbols

\( {\mathbf{R}}_{{{\mathbf{con}}}} \)

Constraints matrix of level flight distance

\( {\mathbf{T}}_{{{\mathbf{con}}}} \)

Constraints matrix of arrival time

\( {\mathbf{H}}_{{{\mathbf{con}}}} \)

Constraints matrix of final altitude

\( {\mathbf{R}} \)

Flight distance matrix

\( {\mathbf{T}} \)

Flight time matrix

\( {\mathbf{H}} \)

Final flight altitude matrix

\( {\mathbf{T}}_{{\mathbf{L}}} \)

Speed-limited flight time matrix

\( F \)

Fuel consumption

\( T \)

Temperature rise

\( F_{\text{r}} \)

Reference fuel consumption

\( T_{\text{r}} \)

Reference temperature rise

\( I \)

Warming index

Notes

Acknowledgements

This study was co-supported by the National Natural Science Foundation of China (nos. U1533116 and U1633125) and the State Key Laboratory of Air Traffic Management System and Technology (no. SKLATM201704).

References

  1. 1.
    Camilleri W, Chircop K, Zammitmangion D et al (2012) Design and validation of a detailed aircraft performance model for trajectory optimisation. In: AIAA modeling and simulation technologies conference, Minneapolis, MinnesotaGoogle Scholar
  2. 2.
    Singh R, Gologan C, Pornet C et al (2014) Cost-based flight technique optimization for hybrid energy aircraft. Aircr Eng Aerosp Technol 86(6):591–598.  https://doi.org/10.1108/aeat-05-2014-0075 CrossRefGoogle Scholar
  3. 3.
    Mendoza AM, Botez R (2013) Vertical navigation trajectory optimization algorithm for a commercial aircraft. In: AIAA/3AF aircraft noise and emissions reduction symposium, Atlanta, GAGoogle Scholar
  4. 4.
    Mendoza AM, Mugnier P, Botez RM (2017) Vertical and horizontal flight reference trajectory optimization for a commercial aircraft. In: AIAA guidance, navigation, and control conference, Grapevine, TexasGoogle Scholar
  5. 5.
    Sang GP, John PC (2015) Optimal control based vertical trajectory determination for continuous descent arrival procedures. J Aircr 52(5):1469–1480.  https://doi.org/10.2514/1.c032967 CrossRefGoogle Scholar
  6. 6.
    Roberto SFP, Aniss K, Ruxandra MB (2013) Flight trajectories optimization under the influence of winds using genetic algorithms. In: AIAA guidance, navigation, and control (GNC) conference, Boston, MAGoogle Scholar
  7. 7.
    Patrón RSF, Botez RM (2015) Flight trajectory optimization through genetic algorithms for lateral and vertical integrated navigation. J Aerosp Inf Syst 49(2):73–74.  https://doi.org/10.2514/1.i010348 CrossRefGoogle Scholar
  8. 8.
    Ulfbratt E, Mcconville J (2008) Comparison of the SESAR and NextGen concepts of operations. NCOIC-Aviation IPT 1:1–26Google Scholar
  9. 9.
    Liden S (1992) Optimum 4-D guidance for long flights. AIAA In: Proceedings of the digital avionics systems conference, pp 262–267Google Scholar
  10. 10.
    de Jong PMA, de Vos K, Borst C et al (2011) Time-based spacing for 4D approaches using speed-profiles. In: AIAA guidance, navigation, and control conference, Portland, OregonGoogle Scholar
  11. 11.
    Higuchi Y, Kitazume N, Tamura K et al (2017) Optimal arrival time assignment and control analysis using air traffic data for Tokyo international airport. In: AIAA guidance, navigation, and control conference, Grapevine, TexasGoogle Scholar
  12. 12.
    Vaddi VV, Bai X, Sang GP (2013) Evaluation of time arrival uncertainties associated with NextGen FMS capabilities. In: AIAA guidance, navigation, and control conference, Kissimmee, FloridaGoogle Scholar
  13. 13.
    David BG, Sgorcea RM, Symionow W (2013) Air-ground trajectory predictions during required time of arrival operation. In:AIAA aviation technology, integration, and operations conference, Los Angeles, CAGoogle Scholar
  14. 14.
    Ramon D, Justinas A, Prats X (2017) Combining the assignment of pre-defined routes and RTAs to sequence and merge arrival traffic. In: AIAA aviation technology, integration, and operations conference, Denver, ColoradoGoogle Scholar
  15. 15.
    Adrian P, Ramon D, Piotr L, Xavier P (2017) Assessment of arrival traffic synchronisation with RTAs and fuel-efficient trajectories. In: AIAA aviation technology, integration, and operations conference, Denver, ColoradoGoogle Scholar
  16. 16.
    Prins JD, Gomez R, Mulder M (2011) Towards time-based continuous descent operations with mixed 4D FMS equipage. In: AIAA aviation technology, integration, and operations conference, Virginia Beach, VAGoogle Scholar
  17. 17.
    García-Heras J, Soler M, Sáez FJ (2014) A Comparison of optimal control methods for minimum fuel cruise at constant altitude and course with fixed arrival time. Procedia Eng 80:231–244.  https://doi.org/10.1016/j.proeng.2014.09.083 CrossRefGoogle Scholar
  18. 18.
    García-Heras J, Soler M, Sáez FJ (2016) Collocation methods to minimum-fuel trajectory problems with required time of arrival in ATM. J Aerosp Inf Syst 13(7):243–264.  https://doi.org/10.2514/1.i010401 CrossRefGoogle Scholar
  19. 19.
    Alejandro MM, Audric B, Ruxandra MB (2016) Aircraft vertical reference trajectory optimization with a RTA constraint using the ABC algorithm. In: 16th AIAA aviation technology, integration, and operations (ATIO) conference, Washington, DCGoogle Scholar
  20. 20.
    Wuebbles D, Rodriguez J, Kärcher B et al (2006) A report of findings and recommendations. In: Workshop on the impacts of aviation on climate change, MIT, rept. no. Partner-coe-2006-004Google Scholar
  21. 21.
    Scot EC, Michael BB, Natasha AN (2013) Fuel-optimal trajectory generation for persistent contrail mitigation. J Guid Control Dyn 36(6):1741–1750.  https://doi.org/10.2514/1.55969 CrossRefGoogle Scholar
  22. 22.
    Sustainable Aviation Clear Quieter Smarter. Climate impacts of aviation’s non-CO2 emissions. (2014-05). http://www.sustainableaviation.co.uk/contact/. Accessed 1 Feb 2018
  23. 23.
    Ali E, Richard C (2015) Vertically curved runways for reducing airport environmental impact and increasing aircraft productivity. J Aircr 52(5):1681–1691.  https://doi.org/10.2514/1.c033052 CrossRefGoogle Scholar
  24. 24.
    Wei ZQ, Zhang WX, Han B (2016) Optimization Method of aircraft cruise performance parameters considering pollution emissions. Acta Aeronaut Astronaut Sin 37(11):3485–3493.  https://doi.org/10.7527/s1000-6893.2016.0119(Chinese) CrossRefGoogle Scholar
  25. 25.
    Alfonso V, Damián R (2014) Optimization of aircraft cruise procedures using discrete trajectory patterns. J Aircr 51(51):1632–1640.  https://doi.org/10.2514/1.c032041 CrossRefGoogle Scholar
  26. 26.
    Sang GP, John-Paul C (2016) Vertical trajectory optimization to minimize environmental impact in the presence of wind. J Aircr 53(3):725–737.  https://doi.org/10.2514/1.c032974 CrossRefGoogle Scholar
  27. 27.
    Wang CK, Luo XZ, Zhang H (2013) Differences between the shares of greenhouse gas emissions calculated with GTP and GWP for major countries. Clim Change Res 9(1):49–54.  https://doi.org/10.3969/j.issn.1673-1719.2013.01.008(Chinese) CrossRefGoogle Scholar
  28. 28.
    ACCRI Theme 7 (2008) Metrics for comparison of climate impacts from well mixed greenhouse gases and inhomogeneous forcing such as those from UT/LS ozone, contrails and contrail-cirrus. Federal Highway Administration, Washington, DC. https://rosap.ntl.bts.gov/view/dot/39398#tabs-2
  29. 29.
    European Aviation Safety Agency. ICAO Engine Exhaust Emissions Databank. (2016-12-02) [2018-02-01]. http://easa.europa.eu/document-library/icao-aircraft-engineemissions-databank. Accessed 10 Nov 2017
  30. 30.
    Steven LB, Terrance GT, Stephen CH et al (1996) Scheduled Civil Aircraft Emission Inventories for 1992: database development and analysis. NASA Center for AeroSpace Information & National Technical Information Service 116–124Google Scholar
  31. 31.
    User manual for the base of aircraft data (BADA), Rev. 3.6 (2004) EUROCONTROL Experimental CentreGoogle Scholar

Copyright information

© The Korean Society for Aeronautical & Space Sciences 2019

Authors and Affiliations

  1. 1.College of Air Traffic ManagementCivil Aviation University of ChinaTianjinChina
  2. 2.China Academy of Civil Aviation Science and TechnologyBeijingChina

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