Method for determining of aircraft landing performance by the simulation modeling

The paper considers the method for determining of aircraft landing performance, the basic of which is landing roll depending on the type and tire inflation pressure, runway surface condition, aircraft weight, availability of brake parachutes. The given results are received by the simulation modeling of aircraft spatial movement on a landing mode. The model of aircraft dynamics includes modules of aircraft movement kinematic parameters calculation, engine thrust, landing gear ground reaction, retarding force in wheel brakes and brake parachutes. Adequacy and reliability of the designed model of aircraft movement is confirmed by comparison of values of movement kinematic parameters obtained as a result of simulation modeling, and the parameters received from a real flight of the maneuverable aircraft. The designed simulation model allows us to analyze change of aircraft movement kinematic parameters, defining its flight mode. By the results of the conducted study, it has been defined that halving of normal operational pressure in MLG wheels decreases aircraft landing roll by over forty per cent. Installation of КТ-163D wheels instead of КТ-251А reduces landing roll by approximately a factor of one and a half times, use of brake parachutes reduces aircraft landing roll almost twice at landing on an icy runway. The introduced method is recommended to be used while studying aircraft landing performance during its design or modernization. It is also suggested to integrate the designed method for determining landing performance as a part of on-board information and control system with the view of immediate aircraft landing performance determination in real-time operation in specific flight conditions.

1. Efremov, A.V. (2017). Plane – pilot system. Regularities and mathematical simulation of pilot behavior. Moscow: Izdatelstvo MAI, 196 p. (in Russian)

2. Robinson, T. (2017). Train virtual, fight easy. AERO SPACE. Royal aeronautical society, no. 6 (44), pp. 16–19. Available at: https://www.aerosociety.com/news/train-virtual-fight-easy (accessed: 23.11.2021).

3. Myshkin, L.V. (2006). Forecasting of aviation technics development: theory and practice. Moscow: FIZMATLIT, 304 p. (in Russian)

4. Efremov, A.V., Zaharchenko, V.F., Ovcharenko, V.N. & Sukhanov, V.L. (2011). Dynamics of flight: Textbook for Universities. Moscow: Mashinostroyeniye, 220 p. (in Russian)

5. Kostin, P.S., Vereshchagin, J.O. & Voloshin, V.A. (2015). Programming and modelling complex for seminatural simulation of dynamics of the maneuverable plane. Trudy MAI, no. 81, 30 p. Available at: http://trudymai.ru/published.php?ID=57735 (accessed: 23.11.2021). (in Russian)

6. Ikryannikov, E.D., Is’kuo, A.S., Levitskii, S.V. et al. (2015). Plane Jak-130UbS. Aerodynamics and flight performance, in Podobedov V.A., Popovich K.F. (Ed.). Moscow: Mashinostroyeniye, 346 p. (in Russian)

7. Heinemann, S., Müller, H.A. & Suleman, A. (2018). Toward smarter auto flight control system infrastructure. Journal of Aerospace Information Systems, vol. 15, no. 6, 21 p. DOI: 10.2514/1.I010565 (accessed: 23.11.2021).

8. Borisov, V.G., Nachinkina, G.N. & Shevchenko, А.М. (2011). A design technique of flight control algorithms for agile aircraft. Trudy MIEA. Navigatsiya i Upravleniye Letatelnymi Apparatami, no. 3, pp. 48–56. (in Russian)

9. Vereshchikov, D.V., Zhuravskii, K.A. & Kostin, P.S. (2021). Motion control quality assessment of maneuverable aircraft. Aerospace MAI Journal, vol. 28, no. 2, pp. 191–205. DOI: 10.34759/vst-2021-2-191-205 (in Russian)

10. Efremov, A.V., Tjaglik, M.S., Tiumentzev, U.V. & Wenqian, T. (2016). Pilot behavior modeling and its application to manual control tasks. IFAC-PapersOnLine, vol. 49, no. 32, pp. 159–164. DOI: 10.1016/j.ifacol.2016.12.207 (accessed: 23.11.2021).

11. Jirgl, M., Jalovecky, R. & Bradac, Z. (2016). Models of pilot behavior and their use to evaluate the state of pilot training. Journal of Electrical Engineering, vol. 67, no. 4, pp. 267–272. DOI: 10.1515/jee-2016-0039

12. Wang, T., Xie, W. & Zhang, Y. (2015). Sliding mode reconfigurable fault tolerant control for nonlinear aircraft systems. Journal of Aerospace Engineering, vol. 28, 17 p. DOI: 10.1061/(ASCE)AS.1943-5525.0000420

13. Majka, A. (2018). Remotely piloted aircraft system with optimum avoidance maneuvers. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, vol. 232, no. 7, pp. 1247–1257. DOI: 10.1177/0954410017697997 (accessed: 23.11.2021).

14. Skoog, M.A. & Less, J.L. (2014). Development and flight demonstration of a variable autonomy ground collision avoidance system. American Institute of Aeronautics and Astronautics, 22 p. Available at: https://technologyafrc.ndc.nasa.gov/documents/features/DR-0005DRC-012-033_iGCAS-paper_2014-06-28.pdf (accessed: 23.11.2021).

15. Okamoto, K. & Tsuchiya, T. (2015). Optimal aircraft control in stochastic severe weather conditions. Journal of Guidance, Control, and Dynamics, vol. 39, no. 1, pp. 77–85. DOI: 10.2514/1.G001105

16. Suzuki, S., Sakamoto, Y., Sanematsu, Y. & Takahara, H. (2006). Analysis of human pilot control inputs using neural network. Application Interrupt and Reset Control Register, vol. 43, no. 3, pp. 793–798. DOI: 10.2514/1.16898