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Small aircraft flight trajectory optimisation using a multidisciplinary approach

Published online by Cambridge University Press:  16 December 2024

M. Rostami Open the ORCID record for M. Rostami [Opens in a new window] ,
J. Bardin ,
D. Neufeld  and
J. Chung Open the ORCID record for J. Chung [Opens in a new window]
M. Rostami
Affiliation:
Department of Aerospace Engineering, Toronto Metropolitan University, Toronto, ON, Canada
J. Bardin
Affiliation:
Department of Aerospace Engineering, Toronto Metropolitan University, Toronto, ON, Canada
D. Neufeld
Affiliation:
Department of Aerospace Engineering, Toronto Metropolitan University, Toronto, ON, Canada
J. Chung*
Affiliation:
Department of Aerospace Engineering, Toronto Metropolitan University, Toronto, ON, Canada
*
Corresponding author: J. Chung; Email: j3chung@torontomu.ca
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Abstract

In a competitive market, airlines continually seek solutions that can reduce their operational costs. Flight path optimisation is a commonly pursued approach to this but requires a large amount of data about the flight environment including the weather information, the aircraft performance and the air traffic control (ATC) requirements. Existing programmes require the user to provide this aircraft performance data in advance and are incapable of generating the information on their own. In this study, using a multidisciplinary approach and numerical optimisations, a novel standalone flight path optimiser (SAFPO) solution is proposed and developed to choose the best flight path for a flight between two points in accordance with the cost objectives. SAFPO uses its own performance calculator, predefined ATC routes, and known weather information to find the optimum flight path which minimises fuel consumption and/or flight time. The aerodynamic characteristics of the aircraft are determined using a validated semi-empirical programme called MAPLA, previously developed for light aircraft analysis. Furthermore, the optimisation process consists of a multidisciplinary-feasible (MDF) framework that employs a genetic algorithm (GA) optimiser. The resulting performance characteristics of the aircraft and the optimisation process are compared with the actual information provided within the flight manual of a Beechcraft Baron G58 aircraft. The optimisation results show that SAFPO can be used to make advances in the daily operations of small and local airlines suffering from a lack of aircraft performance data and help them to choose the scenario that best accomplishes their cost objectives.

Keywords

aircraft performanceflight trajectory optimisationsmall aircraftmultidisciplinary approachOperative cost reduction

Information

Type
Research Article
Information
The Aeronautical Journal , Volume 129 , Issue 1333 , March 2025 , pp. 671 - 689
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Copyright
© The Author(s), 2024. Published by Cambridge University Press on behalf of Royal Aeronautical Society

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References

Dharmaseelan, A. and Adistambha, K.D. Flight plan optimization, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2015, Vol. 949807, 2015. https://doi.org/10.1117/12.2178144 CrossRefGoogle Scholar
Tian, Y., He, X., Xu, Y., Wan, L. and Ye, B. 4D trajectory optimization of commercial flight for green civil aviation, IEEE Access, 2020, 8, pp 62815–62829. https://doi.org/10.1109/ACCESS.2020.2984488 CrossRefGoogle Scholar
Altus, S. Effective Flight Plans Can Help Airlines Economize. Aero Q. – Boeing, 27–30, 2009. Retrieved from http://www.boeing.com/commercial/aeromagazine/articles/qtr_03_09/pdfs/AERO_Q309_article08.pdf Google Scholar
Uçan, F. and Altılar, D.T. Using genetic algorithms for navigation planning in dynamic environments, Appl. Comput. Intell. Soft Comput., 2012, pp 1–16. https://doi.org/10.1155/2012/560184 Google Scholar
Al-Jarrah, M.A. and Hasan, M.M. HILS setup of dynamic flight path planning in 3D environment with flexible mission planning using Ground Station, J. Franklin Inst., 2011, 348, pp 45–65. https://doi.org/10.1016/j.jfranklin.2009.02.012 CrossRefGoogle Scholar
Ramée, C., Kim, J., Deguignet, M., Justin, C., Briceno, S. and Mavris, D. Aircraft flight plan optimization with dynamic weather and airspace constraints, Int. Conf. Res. Air Transp., 2020, pp 1–8.Google Scholar
Ghazi, G., Botez, R.M., Bourrely, C. and Turculet, A.-A. Method for calculating aircraft flight trajectories in presence of winds, J. Aerosp. Inf. Syst., 2021, 18, pp 442–463. https://doi.org/10.2514/1.i010879 Google Scholar
Li, C. and Hansman, R.J. Preliminary development of a cruise altitude and speed optimization decision support tool, 2018 Aviation Technology, Integration, and Operations Conference, 2018, pp 1–13. https://doi.org/10.2514/6.2018-3513.CrossRefGoogle Scholar
Rosenow, J., Strunck, D. and Fricke, H. Trajectory optimization in daily operations, CEAS Aeronaut. J., 2018, 11, pp 333–343.Google Scholar
Wang, Y., Sun, H., Zhu, J. and Zhu, B. Optimization model and algorithm design for airline fleet planning in a multiairline competitive environment, Math. Probl. Eng., 2015. https://doi.org/10.1155/2015/783917 Google Scholar
Dancila, B.D., Beulze, B. and Botez, R.M. Flight phase and altitude-dependent geometrical vertical flight plan optimization minimizing the total number of vertical plan segments, Proc. Inst. Mech. Eng. Part G J. Aerosp. Eng., 2019, 233, pp 4825–4838. https://doi.org/10.1177/0954410019832127 CrossRefGoogle Scholar
Thayer, R.H. and Bjorke, P. Concept of operations for the next generation air transportation system, Encycl. Softw. Eng., 2011. https://doi.org/10.1002/0471028959.sof055 CrossRefGoogle Scholar
Degarmo, M.T. (2004). Issues Concerning Integration of Unmanned Aerial Vehicles in Civil Airspace.Google Scholar
Weibel, R.E. and Hansman, R.J. Safety considerations for operation of unmanned aerial vehicles in the national airspace system, Transportation (Amst), 2005, 37, pp 26–30.Google Scholar
Woods, S.E., Vivona, R.A., Henderson, J., Wing, D.J. and Burke, K.A Traffic aware planner for cockpit-based trajectory optimization, 16th AIAA Aviation Technology, Integration, and Operations Conference, 2016, pp 1–13. https://doi.org/10.2514/6.2016-4067 CrossRefGoogle Scholar
Lewis, T.A., Burke, K.A., Underwood, M.C. and Wing, D.J. Weather design considerations for the tasar traffic aware planner, AIAA Aviation 2019 Forum, 2019, pp 1–16. https://doi.org/10.2514/6.2019-3616 CrossRefGoogle Scholar
Underwood, M.C., Lewis, T.A. and Barney, T.L. In-flight evaluation of the traffic aware planner on the NASA hu-25a guardian aircraft, AIAA Aviation 2019 Forum, 2019, pp 1–13. https://doi.org/10.2514/6.2019-3615 CrossRefGoogle Scholar
Barney, T.L., Underwood, M.C. and Buck, B.K. Simulation and flight test environments for the tasar traffic aware planner, AIAA Aviation 2019 Forum, 2019, pp 1–10. https://doi.org/10.2514/6.2019-3614 CrossRefGoogle Scholar
Woods, S.E., Vivona, R.A., Roscoe, D.A., LeFebvre, B.C., Wing, D.J. and Ballin, M.G. A cockpit-based application for traffic aware trajectory optimization, AIAA Guidance, Navigation, and Control Conference, 2013, pp 1–14. https://doi.org/10.2514/6.2013-4967 CrossRefGoogle Scholar
NASA’s Technology Transfer Program. Flight Optimization System (FLOPS) Software, LAR-18934-1, 2021. https://software.nasa.gov/software/LAR-18934-1 Google Scholar
Wells, D.P., Horvath, B.L. and McCullers, L.A. The Flight Optimization System Weight Estimation Method. NASA Tech. Memo. 219627. TM-2017-21, 2017.Google Scholar
Martins, J.R.R.A. and Lambe, A.B. Multidisciplinary design optimization: A survey of architectures, AIAA J., 51, (9), 2013, pp 2049–2075. https://doi:10.2514/1.J051895 CrossRefGoogle Scholar
Simpson, T.W., Mauery, T.M., Korte, J.J. and Mistree, F. Comparison of response surface and kriging models for multidisciplinary design optimization, 7th AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, 1998, pp 381–391. https://doi:10.2514/6.1998-4755 CrossRefGoogle Scholar
Yi, S.I., Shin, J.K. and Park, G.J. Comparison of MDO methods with mathematical examples, Struct. Multidiscip. Optim., 2008, 35, pp 391–402. https://doi.org/10.1007/s00158-007-0150-2 CrossRefGoogle Scholar
NavCanada. NavCanada website, n.d. Retrieved November, 2021, from https://flightplanning.navcanada.ca/cgi-bin/CreePage.pl?Langue=anglais&NoSession=NS_Inconnu&Page=forecast-observation&TypeDoc=html Google Scholar
SkyVector. SkyVector Aeronautical Charts, n.d. Retrieved November, 2021, from https://skyvector.com/ Google Scholar
Rostami, M. and Bagherzadeh, S.A. Development and validation of an enhanced semi-empirical method for estimation of aerodynamic characteristics of light, propeller-driven airplanes, Proc. Inst. Mech. Eng. Part G J. Aerosp. Eng., 2018, 232, pp 638–648. https://doi.org/10.1177/0954410016683415 CrossRefGoogle Scholar
Rostami, M. and Chung, J. Multidisciplinary Analysis Program for Light Aircraft (MAPLA), Proceedings of the Canadian Society for Mechanical Engineering International Congress, Charlottetown, PE, Canada, 2021, pp 27–30. https://doi.org/10.32393/csme.2021.92 CrossRefGoogle Scholar
Rostami, M., Chung, J. and Neufeld, D. Vertical tail sizing of propeller-driven aircraft considering the asymmetric blade effect, Proc. Inst. Mech. Eng. Part G J. Aerospace Eng., 2022, 236, (6), pp 1184–1195. https://doi.org/10.1177/09544100211029450 CrossRefGoogle Scholar
Rostami, M., Chung, J. and Park, H.U. Design optimization of multi-objective proportional–integral–derivative controllers for enhanced handling quality of a twin-engine, propeller-driven airplane, Advances in Mechanical Engineering, 2020, 12, (6). https://doi.org/10.1177/1687814020923178 CrossRefGoogle Scholar
Rostami, M., Bardin, J., Neufeld, D. and Chung, J. A multidisciplinary possibilistic approach to size the empennage of multi-engine propeller-driven light aircraft, Aerospace, 2022, 9, (3), p 160. https://doi.org/10.3390/aerospace9030160 CrossRefGoogle Scholar
Rostami, M., Bardin, J., Neufeld, D. and Chung, J. EVTOL tilt-wing aircraft design under uncertainty using a multidisciplinary possibilistic approach, Aerospace, 2023, 10, (8), p 718. https://doi.org/10.3390/aerospace10080718 CrossRefGoogle Scholar
Oyama, Y., Rostami, M. and Chung, J. Conceptual design and analysis of a box fan-in-split-wing tiltrotor EVTOL aircraft, Aircraft Eng. Aerospace Technol., 2024. https://doi.org/10.1108/AEAT-06-2023-0167 CrossRefGoogle Scholar
Cavcar, A. Climb performance of piston-propeller airplane with cambered wing and variable propeller efficiency, J. Aircraft, 2011, 48, (5), pp 1701–1707.CrossRefGoogle Scholar
Cavcar, M. The International Standard Atmosphere, n.d. Retrieved from http://fisicaatmo.at.fcen.uba.ar/practicas/ISAweb.pdf Google Scholar
Massachusetts Institute of Technology. Aircraft Range: The Breguet Range Equation, n.d. Retrieved from https://web.mit.edu/16.unified/www/FALL/thermodynamics/notes/node98.html Google Scholar
Lan, C.-T.E. and Roskam, J. Airplane Aerodynamic and Performance, DARcorporation, 2011, Lawrence, Kansas. Google Scholar
Lambe, A.B. and Martins, J.R.R.A. Extensions to the design structure matrix for the description of multidisciplinary design, analysis, and optimization processes, Struct. Multidiscip. Optim., 2012, 46, (2), pp 273–284. https://doi.org/10.1007/s00158-012-0763-y.CrossRefGoogle Scholar
Hawker Beechcraft Corporation. Model G58 Baron, Pilot’s Operating Handbook and FAA Approved Airplane Flight Manual, P/N 58-590000-67, Original Issue - November, 2005. Google Scholar
Textron Aviation Inc. Model G58 Baron, n.d. Retrieved from https://beechcraft.txtav.com/en/baron-g58 Google Scholar