A mathematical model for identifying military training flights
Abstract
The main tasks of the Training Air Base concern the practical training of cadets in piloting techniques as well as maintaining and improving the piloting skills of the instructors. It is essential to maintain the infrastructure of the airfield and the Base as a whole ready for operation. This allows for fulfilling the fundamental mission of such military units, which is to provide effective operations for the defence of the state. Therefore, measures to support and improve the operation of such military facilities are extremely important and also became the genesis of this article. It analyses and evaluates the number of flights carried out over seven years (2016–2022) at the studied training base using mathematical modelling, allowing to assess the variability of the studied series. The phase trends method was used for this purpose, preceded by a seasonality study. It allowed the identification of periods in which the number of flights performed varies significantly. Such knowledge enables better regulation of the airport’s operation, adjustment of activities to the needs, and the determination of further directions for airport development and the justification of potential investments. An additional value of the article is the presentation of a mathematical modelling method specifically designed for seasonal time series, along with their diagnostics. It also provides an opportunity for other institutions to carry out tasks while upholding the highest standards.
Keyword : military training flights, seasonality, phase trends method, cadet training, military airport
How to Cite
Borucka, A., Jabłoński, P., Patrejko, K., & Patrejko, Łukasz. (2024). A mathematical model for identifying military training flights. Aviation, 28(1), 9–15. https://doi.org/10.3846/aviation.2024.20988
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Banerjee, N., Morton, A., & Akartunalı, K. (2020). Passenger demand forecasting in scheduled transportation. European Journal of Operational Research, 286(3), 797–810. https://doi.org/10.1016/j.ejor.2019.10.032
Bauranov, A., & Rakas, J. (2021). Designing airspace for urban air mobility: A review of concepts and approaches. Progress in Aerospace Sciences, 125, Article 100726. https://doi.org/10.1016/j.paerosci.2021.100726
Borucka, A. (2023). Seasonal methods of demand forecasting in the supply chain as support for the company’s sustainable growth. Sustainability, 15(9), Article 7399. https://doi.org/10.3390/su15097399
Borucka, A., & Sobecki, G. (2023). A road safety evaluation model in the context of legislative changes. Transport Problems, 18(3), 40–51. https://doi.org/10.20858/tp.2023.18.3.04
Chen, H., Fan, D., Huang, J., Huang, W., Zhang, G., & Huang, L. (2020). Finite element analysis model on ultrasonic phased array technique for material defect time of flight diffraction detection. Science of Advanced Materials, 12(5), 665–675. https://doi.org/10.1166/sam.2020.3689
Czyż, Z., Jakubczak, P., Podolak, P., Skiba, K., Karpiński, P., Droździel-Jurkiewicz, M., & Wendeker, M. (2023). Deformation measurement system for UAV components to improve their safe operation. Eksploatacja i Niezawodność – Maintenance and Reliability, 25(4), Article 172358. https://doi.org/10.17531/ein/172358
Ellis, K. K., Krois, P., Koelling, J., Prinzel, L. J., Davies, M., & Mah, R. (2021). A Concept of Operations (ConOps) of an in-time aviation safety management system (IASMS) for Advanced Air Mobility (AAM). In AIAA Scitech 2021 Forum (p. 1978). American Institute of Aeronautics and Astronautics. https://doi.org/10.2514/6.2021-1978
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Gui, G., Liu, F., Sun, J., Yang, J., Zhou, Z., & Zhao, D. (2019). Flight delay prediction based on aviation big data and machine learning. IEEE Transactions on Vehicular Technology, 69(1), 140–150. https://doi.org/10.1109/TVT.2019.2954094
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Kanavos, A., Kounelis, F., Iliadis, L., & Makris, C. (2021). Deep learning models for forecasting aviation demand time series. Neural Computing and Applications, 33(23), 16329–16343. https://doi.org/10.1007/s00521-021-06232-y
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Kosacki, K., & Tomczyk, A. (2022). Application of analytical redundancy of measurements to increase the reliability of aircraft attitude control. Aviation, 26(3), 138–144. https://doi.org/10.3846/aviation.2022.17555
Kozłowski, E. (2015). Time series analysis and identification. Lublin University of Technology.
Kozłowski, E., Borucka, A., Oleszczuk, P., & Jałowiec, T. (2023). Evaluation of the maintenance system readiness using the semi-Markov model taking into account hidden factors. Eksploatacja i Niezawodność – Maintenance and Reliability, 25(4), Article 172857. https://doi.org/10.17531/ein/172857
Kumar, S., & Zymbler, M. (2019). A machine learning approach to analyze customer satisfaction from airline tweets. Journal of Big Data, 6, Article 62. https://doi.org/10.1186/s40537-019-0224-1
Lambelho, M., Mitici, M., Pickup, S., & Marsden, A. (2020). Assessing strategic flight schedules at an airport using machine learning-based flight delay and cancellation predictions. Journal of Air Transport Management, 82, Article 101737. https://doi.org/10.1016/j.jairtraman.2019.101737
Leško, J., Andoga, R., Bréda, R., Hlinková, M., & Fözö, L. (2023). Flight phase classification for small unmanned aerial vehicles. Aviation, 27(2), 75–85. https://doi.org/10.3846/aviation.2023.18909
Liu, H., & Xiao, N. (2022). Global non-probabilistic reliability sensitivity analysis based on surrogate model. Eksploatacja i Niezawodność – Maintenance and Reliability, 24(4), 612–616. https://doi.org/10.17531/ein.2022.4.2
Lyu, H., Wang, S., Zhang, X., Yang, Z., & Pecht, M. (2021). Reliability modeling for dependent competing failure processes with phase-type distribution considering changing degradation rate. Eksploatacja i Niezawodność – Maintenance and Reliability, 23(4), 627–635. https://doi.org/10.17531/ein.2021.4.5
Mínguez Barroso, C., & Muñoz-Marrón, D. (2023). Major air disasters: Accident investigation as a tool for defining eras in commercial aviation safety culture. Aviation, 27(2), 104–118. https://doi.org/10.3846/aviation.2023.19244
Parolin, G., Borges, A. T., Santos, L. C., & Borille, A. V. (2021). A tool for aircraft eco-design based on streamlined Life Cycle Assessment and uncertainty analysis. Procedia CIRP, 98, 565–570. https://doi.org/10.1016/j.procir.2021.01.152
Pavli, A., Smeti, P., Hadjianastasiou, S., Theodoridou, K., Spilioti, A., Papadima, K., & Maltezou, H. C. (2020). In-flight transmission of COVID-19 on flights to Greece: An epidemiological analysis. Travel Medicine and Infectious Disease, 38, Article 101882. https://doi.org/10.1016/j.tmaid.2020.101882
Shaw, D. M., Cabre, G., & Gant, N. (2021). Hypoxic hypoxia and brain function in military aviation: Basic physiology and applied perspectives. Frontiers in Physiology, 12, Article 665821. https://doi.org/10.3389/fphys.2021.665821
Sheridan, K., Puranik, T. G., Mangortey, E., Pinon-Fischer, O. J., Kirby, M., & Mavris, D. N. (2020). An application of DBSCAN clustering for flight anomaly detection during the approach phase. AIAA Scitech 2020 Forum, Article 1851. https://doi.org/10.2514/6.2020-1851
Soltani, M., Ahmadi, S., Akgunduz, A., & Bhuiyan, N. (2020). An eco-friendly aircraft taxiing approach with collision and conflict avoidance. Transportation Research Part C: Emerging Technologies, 121, Article 102872. https://doi.org/10.1016/j.trc.2020.102872
Su, S., Sun, Y., Peng, C., & Wang, Y. (2023). Aircraft bleed air system fault prediction based on encoder-decoder with attention mechanism. Eksploatacja i Niezawodność – Maintenance and Reliability, 25(3). https://doi.org/10.17531/ein/167792
Villafaina, S., Fuentes-García, J. P., Gusi, N., Tornero-Aguilera, J. F., & Clemente-Suárez, V. J. (2021). Psychophysiological response of military pilots in different combat flight maneuvers in a flight simulator. Physiology & Behavior, 238, Article 113483. https://doi.org/10.1016/j.physbeh.2021.113483
Wang, Z., & Song, W.-K. (2020). Sustainable airport development with performance evaluation forecasts: A case study of 12 Asian airports. Journal of Air Transport Management, 89, Article 101925. https://doi.org/10.1016/j.jairtraman.2020.101925
Wei, K., Zhang, T., & Zhang, C. (2023). Research on resilience model of UAV swarm based on complex network dynamics. Eksploatacja i Niezawodność – Maintenance and Reliability, 35(4). https://doi.org/10.17531/ein/173125
Yu, B., Guo, Z., Asian, S., Wang, H., & Chen, G. (2019). Flight delay prediction for commercial air transport: A deep learning approach. Transportation Research Part E: Logistics and Transportation Review, 125, 203–221. https://doi.org/10.1016/j.tre.2019.03.013
Zhang, Y., & Zhao, M. (2023). An integrated approach to estimate storage reliability with masked data from series system. Eksploatacja i Niezawodność – Maintenance and Reliability, 25(4). https://doi.org/10.17531/ein/172922
Ziółkowski, J., Żurek, J., Małachowski, J., Oszczypała, M., & Szkutnik-Rogoż, J. (2022). Method for calculating the required number of transport vehicles supplying aviation fuel to aircraft during combat tasks. Sustainability, 14(3), Article 1619. https://doi.org/10.3390/su14031619