AIRCRAFT DRAG POLAR ESTIMATION USING CUCKOO SEARCH ALGORITHM

Abstract

Effective management of air traffic will result in aircraft spending less time in the air and on the ground, thus reducing fuel consumption, fuel-related emissions, and noise levels, and increasing customer satisfaction. Aircraft performance models form the basis of an efficient air traffic management (ATM) system. In addition, drag polars are one of the most important inputs of aircraft performance models, which are important for aircraft trajectory prediction, optimization, and air traffic modeling. In this study, a simple drag polar and a Mach number dependent drag polar were obtained for six different aircraft types that are among the most used in today’s commercial air transportation. Automatic Dependent Surveillance–Broadcast (ADS-B) and Mode-S surveillance data were used for the analysis of the drag polars obtained for the climb profile. The cuckoo search algorithm (CSA) method was preferred for modeling. The algorithm estimated the calculated CD values for the simple drag polar and the Mach-based drag polar with margins of error between 1.78 x 10-6 and 5.59 x 10-6, and 4.04 x 10-8 and 4.29 x 10-5, respectively. Due to the accuracy of the proposed dataset, as indicated by the low mean squared error (MSE) values, it constitutes an important resource for subsequent aircraft performance studies.

Keywords

• Drag polar
• Cuckoo search algorithm
• Aircraft
• Climb

References

1. R. Oruc and T. Baklacioglu, “Cruise range modeling of different flight strategies for transport aircraft using genetic algorithms and particle swarm optimization,” Energy, vol. 294, p. 130917, 2024, doi: 10.1016/j.energy.2024.130917.
2. Boeing, “Commercial Market Outlook 2024-2043.” Accessed: Dec. 16, 2024. [Online]. Available: https://www.boeing.com/content/dam/boeing/boeingdotcom/market/assets/downloads/2024-cmo-executive-summary.pdf
3. A. Nuic, C. Poinsot, M. Iagaru, E. Gallo, F. A. Navarro, and C. Querejeta, “Advanced Aircraft Performance Modeling for ATM: Enhancements to the Bada Model,” in 24th Digital Avionics System Conference, Washington, DC: AIAA/IEEE, 2005, pp. 1–14. doi: 10.1109/dasc.2005.1563320.
4. R. Oruc and T. Baklacioglu, “Modeling of energy maneuverability based specific excess power contours for commercial aircraft using metaheuristic methods,” Energy, vol. 269, no. 126819, 2023, doi: https://doi.org/10.1016/j.energy.2023.126819.
5. R. Oruc and T. Baklacioglu, “Modeling of aircraft performance parameters with metaheuristic methods to achieve specific excess power contours using energy maneuverability method,” Energy, vol. 259, p. 125069, Nov. 2022, doi: 10.1016/j.energy.2022.125069.
6. J. Sun, J. Ellerbroek, and J. M. Hoekstra, “WRAP: An open-source kinematic aircraft performance model,” Transp. Res. Part C Emerg. Technol., vol. 98, pp. 118–138, 2019, doi: 10.1016/j.trc.2018.11.009.
7. D. Poles, A. Nuic, and V. Mouillet, “Advanced aircraft performance modeling for atm: Analysis of BADA model capabilities,” in AIAA/IEEE Digital Avionics Systems Conference - Proceedings, Salt Lake City, UT, USA: IEEE, 2010, pp. 1-D. doi: 10.1109/DASC.2010.5655518.
8. J. Sun, J. M. Hoekstra, and J. Ellerbroek, “Aircraft drag polar estimation based on a stochastic hierarchical model,” in SESAR Innovation Days, 2018.

9. V. E. Atasoy, “Developing The Range Equation Depending On Drag Polar and Specific Fuel Consumption Using Real Flight Data,” Eskişehir Technical University, 2020.
10. R. I. Dancila and R. M. Botez, “New flight trajectory optimisation method using genetic algorithms,” Aeronaut. J., vol. 125, no. 1286, 2021, doi: 10.1017/aer.2020.138.
11. N. Schimpf, Z. Wang, S. Li, E. J. Knoblock, H. Li, and R. D. Apaza, “A Generalized Approach to Aircraft Trajectory Prediction via Supervised Deep Learning,” IEEE Access, vol. 11, 2023, doi: 10.1109/ACCESS.2023.3325053.
12. Z. Yang, X. Kang, Y. Gong, and J. Wang, “Aircraft trajectory prediction and aviation safety in ADS-B failure conditions based on neural network,” Sci. Rep., vol. 13, no. 1, 2023, doi: 10.1038/s41598-023-46914-2.
13. T. M. Young, Performance of the Jet Transport Airplane: Analysis Methods, Flight Operations and Regulations. John Wiley & Sons, 2018.
14. A. Filippone, Advanced aircraft flight performance. USA: Cambridge University Press, 2012.
15. J. D. Anderson, Introduction to flight, Seventh ed. New York, America: McGraw-Hill Education, 2011.
16. X. S. Yang, Nature-Inspired Optimization Algorithms. 2014. doi: 10.1016/C2013-0-01368-0.
17. R. Oruc and T. Baklacioglu, “Modelling of fuel flow-rate of commercial aircraft for the climbing flight using cuckoo search algorithm,” Aircr. Eng. Aerosp. Technol., vol. 92, no. 3, pp. 495–501, 2020, doi: 10.1108/AEAT-10-2019-0202.
18. X. S. Yang and S. Deb, “Engineering optimisation by cuckoo search,” Int. J. Math. Model. Numer. Optim., 2010, doi: 10.1504/IJMMNO.2010.035430.
19. A. H. Gandomi, X. S. Yang, and A. H. Alavi, “Cuckoo search algorithm: A metaheuristic approach to solve structural optimization problems,” Eng. Comput., vol. 29, no. 1, pp. 17–35, 2013, doi: 10.1007/s00366-011-0241-y.
20. A. S. Joshi, O. Kulkarni, G. M. Kakandikar, and V. M. Nandedkar, “Cuckoo Search Optimization- A Review,” Mater. Today Proc., vol. 4, no. 8, pp. 7262–7269, 2017, doi: 10.1016/j.matpr.2017.07.055.
21. X. S. Yang and S. Deb, “Cuckoo search via Lévy flights,” in 2009 World Congress on Nature and Biologically Inspired Computing, NABIC 2009 - Proceedings, IEEE, 2009, pp. 210–214. doi: 10.1109/NABIC.2009.5393690.
22. “OpenSky.” Accessed: May 12, 2024. [Online]. Available: https://opensky-network.org/
23. M. Schäfer, M. Strohmeier, V. Lenders, I. Martinovic, and M. Wilhelm, “Bringing up OpenSky: A large-scale ADS-B sensor network for research,” in IPSN 2014 - Proceedings of the 13th International Symposium on Information Processing in Sensor Networks (Part of CPS Week), 2014. doi: 10.1109/IPSN.2014.6846743.
24. T. Baklacioglu, “Aircraft performance modeling (PhD Thesis),” Eskisehir, Turkey: Anadolu University,Graduate School of Natural and Applied Sciences, 2011.
25. E. T. Turgut, “Estimating aircraft fuel flow for a three-degree flight-path-angle descent,” J. Aircr., vol. 48, no. 3, 2011, doi: 10.2514/1.C031260.
26. Boeing, “Fuel conservation strategies: takeoff and climb.” Accessed: Jul. 15, 2019. [Online]. Available: www.boeing.com/commercial/aeromagazine/%0Aarticles/qtr_02_10/pdfs/AERO_FuelConsSeries.pdf
27. M. Battel and T. M. Young, “Simplified thrust and fuel consumption models for modern two-shaft turbofan engines,” in Journal of Aircraft, 2008. doi: 10.2514/1.35589.
28. T. Baklacioglu and M. Cavcar, “Aero-propulsive modelling for climb and descent trajectory prediction of transport aircraft using genetic algorithms,” Aeronaut. J., vol. 118, no. 1199, 2014, doi: 10.1017/S0001924000008939.
29. EASA, “TYPE-CERTIFICATE DATA SHEET.” Accessed: Dec. 10, 2024. [Online]. Available: https://www.easa.europa.eu