Havacılık Alanında Dijital İkiz Uygulamaları

Öz

Dijital İkiz, en temel hali ile fiziksel varlık, sanal varlık (dijital ikiz) ve bu iki varlık arasındaki bağlantıdan oluşmaktadır. Bağlantı çift yönlü ve anlık veri iletimi sağlamaktadır. Bu kabiliyet sayesinde fiziksel ürün meydana gelmeden, dijital ürün üzerinden fiziksel ürünün tüm özellik ve davranışları gözlemlenebilmektedir. Bu kabiliyet bize maliyet düşürme, süreci kısaltma ve üretim öncesi hata yapabilme olanağı tanımaktadır. Buradan da anlaşılabileceği gibi nedeni farketmeksizin üründe yapılacak değişikliklerin (örn. bir hatayı düzeltmek ya da optimizasyon yapmak gibi) sürecin en başı olan tasarım aşamasında yapılabilmesi sonraki süreçlerin daha sağlıklı ilerlemesine olanak sağlayacaktır. Bu nedenle tasarımın da ilk aşamalarında Dİ kullanılarak tüm süreci daha doğru, güvenli, hızlı ve düşük maliyetli bir şekilde tamamlamak mümkün olabilir. Bu bilgiler ışığında, bu çalışmada tasarım aşamasında Dİ kullanımına daha detaylı değinilmiş ve bu çalışmanın daha sonra yapılacak tasarım çalışmaları için bir altyapı sağlaması amaçlanmıştır. Ayrıca bu çalışmada “Dİ nedir?”, “Ne için kullanılır?”,” Avantajları nelerdir?” soruları yanıtlanacak olup havacılık alanındaki uygulamaları üzerinden temel işlevleri, sanal alanda modelleme ve modelleme çeşitleri ile veri işlemleri detaylandırılmaktadır.

Anahtar Kelimeler

• Dijital İkiz
• Tasarım
• Modelleme

Digital Twin Applications In Aviation Field

Öz

Digital Twin, in its most basic form, consists of physical entities, virtual entities (digital twin) and the connection between these two entities. The connection provides bi-directional and instantaneous data transmission. This capability gives us the option to reduce costs, shorten the process and make mistakes before production. As can be understood from here, making changes to the product (e.g. fixing a bug or optimizing) at the design stage, which is the very beginning of the process, regardless of the reason, will allow subsequent processes to progress more smoothly. For this reason, it may be possible to complete the entire process more accurately, safely, quickly and cost-effectively by using DT in the early stages of design. In the light of this information, the use of DT in the design phase is discussed in more detail in this study, and it is aimed to provide an infrastructure for later design studies. Also in this study ”what is DT?”,” What is it used for?”,” What are the advantages?” Their questions will be answered, and their basic functions, modeling and modeling types in the virtual field, and data processing through applications in the aviation field are detailed.

Anahtar Kelimeler

• Digital Twin
• aviation modeling
• design

Kaynakça

1. Alexopoulos, K., Nikolakis, N., & Chryssolouris, G. (2020). Digital twin-driven supervised machine learning for the development of artificial intelligence applications in manufacturing. International Journal of Computer Integrated Manufacturing, 33(5), 429-439. https://doi.org/10.1080/0951192X.2020.1747642
2. Allen, B. D. (2021). Digital twins and living models at NASA. Digital Twin Summit.
3. Andrade, P., Silva, C., Ribeiro, B., & Santos, B. F. (2021). Aircraft maintenance check scheduling using reinforcement learning. Aerospace, 8(4), 113.
4. Attaran, M., & Celik, B. G. (2023). Digital Twin: Benefits, use cases, challenges, and opportunities. Decision Analytics Journal, 100165.
5. Baheti, R., & Gill, H. (2011). Cyber-physical systems. The impact of control technology, 12(1), 161-166. BENGÜ, H., & FİDANCAN, C. (2021). MALİYET DÜŞÜRME YÖNTEMİ OLARAK DİJİTAL İKİZ VE OTOMOTİV ENDÜSTRİSİNDEKİ YERİ. Niğde Ömer Halisdemir Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 3(2), 205-221.
6. Gao, Z., Paul, A., & Wang, X. (2022). Guest editorial: Digital twinning: Integrating AI-ML and big data analytics for virtual representation. IEEE Transactions on Industrial Informatics, 18(2), 1355-1358.
7. Glaessgen, E., & Stargel, D. (2012). The digital twin paradigm for future NASA and US Air Force vehicles. 1818.
8. Grieves, M. (2014). Digital twin: Manufacturing excellence through virtual factory replication. White paper, 1(2014), 1-7.

9. Grose, D. (1994, Eylül 7). Reengineering the aircraft design process. 5th Symposium on Multidisciplinary Analysis and Optimization. 5th Symposium on Multidisciplinary Analysis and Optimization, Panama City Beach,FL,U.S.A. https://doi.org/10.2514/6.1994-4323
10. Hribernik, K., Wuest, T., & Thoben, K.-D. (2013). Towards product avatars representing middle-of-life information for improving design, development and manufacturing processes. 85-96.
11. Kadlec, P., Gabrys, B., & Strandt, S. (2009). Data-driven soft sensors in the process industry. Computers & chemical engineering, 33(4), 795-814.
12. Li, L., Aslam, S., Wileman, A., & Perinpanayagam, S. (2021). Digital twin in aerospace industry: A gentle introduction. IEEE Access, 10, 9543-9562.
13. Liu, Z., Meyendorf, N., & Mrad, N. (2018). The role of data fusion in predictive maintenance using digital twin. 020023. https://doi.org/10.1063/1.5031520
14. Liu, Z., & Mrad, N. (2014). Data fusion for the diagnostics, prognostics, and health management of aircraft systems. 389-399.
15. Lo, C. K., Chen, C. H., & Zhong, R. Y. (2021). A review of digital twin in product design and development. Advanced Engineering Informatics, 48, 101297. https://doi.org/10.1016/j.aei.2021.101297
16. Mayda, M., & Börklü, H. R. (2008). YENİ BİR KAVRAMSAL TASARIM İŞLEM MODELİ.
17. Meng, T., Jing, X., Yan, Z., & Pedrycz, W. (2020). A survey on machine learning for data fusion. Information Fusion, 57, 115-129. https://doi.org/10.1016/j.inffus.2019.12.001
18. Monostori, L. (2003). AI and machine learning techniques for managing complexity, changes and uncertainties in manufacturing. Engineering Applications of Artificial Intelligence, 16(4), 277-291. https://doi.org/10.1016/S0952-1976(03)00078-2
19. Patrikeev, A., Tarasov, A., Borovkov, A., Aleshin, M., & Klyavin, O. (t.y.). NVH ANALYSIS OF OFFROAD VEHICLE FRAME. EVALUATION OF MUTUAL INFLUENCE OF BODY-FRAME SYSTEM COMPONENTS.
20. Perera, Y. S., Ratnaweera, D. A. A. C., Dasanayaka, C. H., & Abeykoon, C. (2023). The role of artificial intelligence-driven soft sensors in advanced sustainable process industries: A critical review. Engineering Applications of Artificial Intelligence, 121, 105988. https://doi.org/10.1016/j.engappai.2023.105988
21. Qiu, S., Liu, S., Kong, D., & He, Q. (2019). Three-dimensional virtual-real mapping of aircraft automatic spray operation and online simulation monitoring. Virtual Reality & Intelligent Hardware, 1(6), 611-621.
22. Rasheed, A., San, O., & Kvamsdal, T. (2020). Digital Twin: Values, Challenges and Enablers From a Modeling Perspective. IEEE Access, 8, 21980-22012. https://doi.org/10.1109/ACCESS.2020.2970143
23. Segovia, M., & Garcia-Alfaro, J. (2022). Design, Modeling and Implementation of Digital Twins. Sensors, 22(14), 5396. https://doi.org/10.3390/s22145396
24. Seshadri, B. R., & Krishnamurthy, T. (2017). Structural health management of damaged aircraft structures using digital twin concept. 1675.
25. Tao, F., Sui, F., Liu, A., Qi, Q., Zhang, M., Song, B., Guo, Z., Lu, S. C.-Y., & Nee, A. Y. (2019). Digital twin-driven product design framework. International Journal of Production Research, 57(12), 3935-3953.
26. Tomiyama, T., Gu, P., Jin, Y., Lutters, D., Kind, C., & Kimura, F. (2009). Design methodologies: Industrial and educational applications. CIRP annals, 58(2), 543-565.
27. Uzun, M., Demirezen, M. U., Koyuncu, E., & Inalhan, G. (2019). Design of a hybrid digital-twin flight performance model through machine learning. 1-14.
28. Wang, J., Li, Y., Gao, R. X., & Zhang, F. (2022). Hybrid physics-based and data-driven models for smart manufacturing: Modelling, simulation, and explainability. Journal of Manufacturing Systems, 63, 381-391. https://doi.org/10.1016/j.jmsy.2022.04.004
29. Wang, L. (2020). Application and development prospect of digital twin technology in aerospace. IFAC-PapersOnLine, 53(5), 732-737.
30. Wu, J., Yang, Y., Cheng, X., Zuo, H., & Cheng, Z. (2020). The Development of Digital Twin Technology Review. 2020 Chinese Automation Congress (CAC), 4901-4906. https://doi.org/10.1109/CAC51589.2020.9327756
31. Xiong, M., & Wang, H. (2022). Digital twin applications in aviation industry: A review. The International Journal of Advanced Manufacturing Technology, 121(9-10), 5677-5692.