OPTIMIZING EUROFIGHTER TYPHOON REPAIRS THROUGH DIGITAL TWIN SIMULATIONS
Keywords:
digital twin technology, eurofighter typhoon, predictive maintenance, maintenance optimization, aerospace engineeringAbstract
The advancement of digital twin technology presents transformative potential in optimizing the repair and maintenance of complex aerospace systems, such as the Eurofighter Typhoon. This paper investigates the implementation of digital twin simulation to enhance maintenance procedures, aiming to improve efficiency, cost-effectiveness, and operational readiness. By creating a comprehensive virtual replica of the Eurofighter Typhoon, maintenance teams can leverage real-time data and predictive analytics to anticipate and address potential failures, streamline maintenance schedules, and ensure compliance with industry standards. This approach not only reduces unexpected downtimes but also enhances safety for pilots and ground crews. The research employs advanced software tools to develop digital twins, incorporating real-time data from multiple sensors, and utilizes statistical methods such as regression analysis and structural equation modeling to analyze data and validate the effectiveness of the proposed model. The findings demonstrate significant improvements in maintenance efficiency, cost savings, and operational safety. This study underscores the necessity of integrating digital twin technology across various industries to optimize maintenance processes and enhance overall system performance. Future research should focus on expanding the applications of digital twins to other areas, exploring new methodologies for data analysis, and continuously improving the accuracy and reliability of these simulations.
References
Allerton, D.J. (2010): The impact of flight simulation in aerospace. – The Aeronautical Journal 114(1162): 747-756.
Ben Amor, S., Elloumi, N., Eltaief, A., Louhichi, B., Alrasheedi, N.H., Seibi, A. (2024): Digital Twin Implementation in Additive Manufacturing: A Comprehensive Review. – Processes 12(6): 15p.
Brunton, S.L., Nathan Kutz, J., Manohar, K., Aravkin, A.Y., Morgansen, K., Klemisch, J., Goebel, N., Buttrick, J., Poskin, J., Blom-Schieber, A.W., Hogan, T. (2021): Data-driven aerospace engineering: reframing the industry with machine learning. – AIAA Journal 59(8): 2820-2847.
Cimino, C., Negri, E., Fumagalli, L. (2019): Review of digital twin applications in manufacturing. – Computers in Industry 113: 15p.
Grieves, M. (2014): Digital twin: manufacturing excellence through virtual factory replication. – White Paper 1: 1-7.
Kilic, U., Yalin, G., Cam, O. (2023): Digital twin for Electronic Centralized Aircraft Monitoring by machine learning algorithms. – Energy 283: 15p.
Li, C., Mahadevan, S., Ling, Y., Choze, S., Wang, L. (2017): Dynamic Bayesian network for aircraft wing health monitoring digital twin. – Aiaa Journal 55(3): 930-941.
Liljaniemi, A., Paavilainen, H. (2020): Using digital twin technology in engineering education–course concept to explore benefits and barriers. – Open Engineering 10(1): 377-385.
Li, L., Aslam, S., Wileman, A., Perinpanayagam, S. (2021): Digital twin in aerospace industry: A gentle introduction. – IEEE Access 10: 9543-9562.
Liu, Z., Meyendorf, N., Mrad, N. (2018): The role of data fusion in predictive maintenance using digital twin. – In AIP Conference Proceedings, AIP Publishing 7p.
Millwater, H., Ocampo, J., Crosby, N. (2019): Probabilistic methods for risk assessment of airframe digital twin structures. – Engineering Fracture Mechanics 221: 24p.
Stanton, I., Munir, K., Ikram, A., El‐Bakry, M. (2023): Predictive maintenance analytics and implementation for aircraft: Challenges and opportunities. – Systems Engineering 26(2): 216-237.
Tao, F., Zhang, H., Liu, A., Nee, A.Y. (2018): Digital twin in industry: State-of-the-art. – IEEE Transactions on Industrial Informatics 15(4): 2405-2415.
Xiong, M., Wang, H., Fu, Q., Xu, Y. (2021): Digital twin–driven aero-engine intelligent predictive maintenance. – The International Journal of Advanced Manufacturing Technology 114(11): 3751-3761.