PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Digital Twin test-bench Performance for marine diesel engine applications

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The application of Digital Twins is a promising solution for enhancing the efficiency of marine power plant operation, particularly their important components – marine internal combustion engines (ICE). This work presents the concept of applying a Performance Digital Twin for monitoring the technical condition and diagnosing malfunctions of marine ICE, along with its implementation on an experimental test-bench, based on a marine diesel-generator. The main principles of implementing this concept involve data transmission technologies, from the sensors installed on the engine to a server. The Digital Twin, also operating on the server, is used to automatically process the acquired experimental data, accumulate statistics, determine the current technical state of the engine, identify possible malfunctions, and make decisions regarding changes in operating programs. The core element of the Digital Twin is a mathematical model of the marine diesel engine’s operating cycle. In its development, significant attention was devoted to refining the fuel combustion model, as the combustion processes significantly impact both the engine’s fuel efficiency and the level of toxic emissions of exhaust gases. The enhanced model differs from the base model, by considering the variable value of the average droplets’ diameter during fuel injection. This influence on fuel vapourisation, combustion, and the formation of toxic components is substantial, as shown. Using the example of calibrating the model to the test results of a diesel engine under 27 operating modes, it is demonstrated that the application of the improved combustion model allows better adjustment of the Digital Twin to experimental data, thus achieving a more accurate correspondence to a real engine.
Rocznik
Tom
Strony
81--91
Opis fizyczny
Bibliogr. 25 poz., rys., tab.
Twórcy
  • Odesa National Maritime University, Odesa, Ukraine
  • Odesa National Maritime University, Odesa, Ukraine
  • Odesa National Maritime University, Odesa, Ukraine
  • Danube Institute of the National University „Odesa Maritime Academy”, Ukraine
  • Odesa National Maritime University, Odesa, Ukraine
  • Odesa National Maritime University, Odesa, Ukraine
  • DPATET Research Group, Ho Chi Minh City University of Transport, Ho Chi Minh City, Viet Nam
Bibliografia
  • 1. M. Grieves and J. Vickers, “Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems,” Transdisciplinary perspectives on complex systems: New findings and approaches. 2017, pp. 85-113, https://www.researchgate.net/profile/Michael-Grieves/publication/306223791_Digital_Twin_Mitigating_ Unpredictable_Undesirable_Emergent_Behavior_in_ Complex_Systems/links/5aa54e1ea6fdccd544bc386f/ Digital-Twin-Mitigating-Unpredictable-UndesirableEmerge.
  • 2. S. Evans, C. Savian, A. Burns and C. Cooper, “Digital Twins for the Built Environment: An Introduction to the Opportunities,” Built Environmental News. 2019, https:// www.theiet.org/media/8762/digital-twins-for-the-builtenvironment.pdf.
  • 3. D. Botín-Sanabria, A.-S. Mihaita, R. Peimbert-García, M. Ramírez-Moreno, R. Ramírez-Mendoza and J. LozoyaSantos, “Digital Twin Technology Challenges and Applications: A Comprehensive Review,” Remote Sensing. 2022, vol. 14(6), no. 1335, doi: 10.3390/rs14061335.
  • 4. M. Singh, E. Fuenmayor, E. Hinchy, Y. Qiao, N. Murray and D. Devine, “Digital Twin: Origin to Future,” Appl. Syst. Innov. 2021, vol. 4, no. 36, doi: 10.3390/asi4020036.
  • 5. L. Li, S. Aslam, A. Wileman and S. Perinpanayagam, “Digital Twin in Aerospace Industry: A Gentle Introduction,” IEEE Access. 2022, vol. 10, pp. 9543-9562, doi: 10.1109/ ACCESS.2021.3136458.
  • 6. M. Xia, H. Shao, D. Williams, S. Lu, L. Shu and C.W. de Silva, “Intelligent fault diagnosis of machinery using digital twinassisted deep transfer learning,” Reliability Engineering & System Safety. 2021, vol. 215, doi:10.1016/j.ress.2021.107938.
  • 7. S. Choi, J. Woo, J. Kim and J. Lee, “Digital Twin-Based Integrated Monitoring System: Korean Application Cases,” Sensors. 2022, vol. 22, no. 5450, doi: 10.3390/s22145450.
  • 8. D. Zhong, Z. Xia, Y. Zhu and J. Duan, “Overview of predictive maintenance based on digital twin technology,” Heliyon. 2023, vol. 9, no. 4, doi: 10.1016/j.heliyon.2023.e14534.
  • 9. A.T. Hoang, A.M. Foley, S. Nižetić, Z. Huang, H.C. Ong, A.I. Ölçer, V.V. Pham and X.P. Nguyen, “Energy-related approach for reduction of CO2 emissions: A critical strategy on the port-to-ship pathway,” Journal of Cleaner Production. 2022, vol. 355, doi:10.1016/j.jclepro.2022.131772.
  • 10. O. Melnyk, O. Sagaydak, O. Shumylo and O. Lohinov, “Modern Aspects of Ship Ballast Water Management and Measures to Enhance the Ecological Safety of Shipping,” in Systems, Decision and Control in Energy V. Studies in Systems, Decision and Control, Springer ed. 2023, vol. 481, Cham, doi: 10.1007/978-3-031-35088-7_39.
  • 11. O. Onishchenko, A. Bukaros, O. Melnyk, V. Yarovenko, A. Voloshyn and O. Lohinov, “Ship Refrigeration System Operating Cycle Efficiency Assessment and Identification of Ways to Reduce Energy Consumption of Maritime Transport,” in Systems, Decision and Control in Energy V. Studies in Systems, Decision and Control, 2023, vol 481. Springer, Cham., doi: 10.1007/978-3-031-35088-7_36.
  • 12. S. Hautala, M. Mikulski, E. Söderäng, X. Storm and S. Niemi, “Toward a digital twin of a mid-speed marine engine: From detailed 1D engine model to real-time implementation on a target platform,” International Journal of Engine Research. 2022, doi: 10.1177/14680874221106168.
  • 13. S. Stoumpos, G. Theotokatos, C. Mavrelos and E. Boulougouris, “Towards Marine Dual Fuel Engines Digital Twins — Integrated Modelling of Thermodynamic Processes and Control System Functions,” J. Mar. Sci. Eng. 2020, vol. 8, no. 3(200), doi: 10.3390/ jmse8030200.
  • 14. I. Asimakopoulos, L. Avendaño-Valencia, M. Lützen and N. Rytter, “Data-driven condition monitoring of two-stroke marine diesel engine piston rings with machine learning,” Ships and Offshore Structures. 2023, doi: 10.1080/17445302.2023.2237302.
  • 15. O. Bondarenko and T. Fukuda, “Development of a diesel engine’s digital twin for predicting propulsion system dynamics,” Energy. 2020, vol. 196, doi:10.1016/j.energy.2020.117126.
  • 16. R. Varbanets, O. Fomin, V. Píštěk, V. Klymenko, D. Minchev, A. Khrulev, V. Zalozh and P. Kučera, “Acoustic method for estimation of marine low-speed engine turbocharger parameters,” Journal of Marine Science and Engineering. 2021, vol. 3, no. 9, doi: 10.3390/jmse9030321.
  • 17. R. Varbanets, O. Shumylo, A. Marchenko, D. Minchev, V. Kyrnats, V. Zalozh, N. Aleksandrovska, R. Brusnyk and K. Volovyk, “Concept of vibroacoustic diagnostics of the fuel injection and electronic cylinder lubrication systems of marine diesel engines,” Polish Maritime Research. 2022, vol. 29, no. 4, pp. 88-96, doi: 10.2478/pomr-2022-0046.
  • 18. S. Neumann, R. Varbanets, D. Minchev, V. Malchevsky and V. Zalozh, “Vibrodiagnostics of marine diesel engines in IMES GmbH systems,” Ships and Offshore Structures. 2022, doi: 10.1080/17445302.2022.2128558.
  • 19. O. Yeryganov and R. Varbanets, “Features of the fastest pressure growth point during compression stroke,” Diagnostyka. 2018, vol. 19, no. 2, pp. 71-76, doi: 10.29354/diag/89729.
  • 20. D. Minchev, R. Varbanets, N. Alexandrovskaya and L. Pisintsaly, “Marine diesel engines operating cycle simulation for diagnostics issues,” Acta Polytechnica. 2021, vol. 61, no. 3, pp. 428-440, doi: 10.14311/ap.2021.61.0435.
  • 21. D. Minchev, O. Gogorenko, R. Varbanets, Y. Moshentsev, V. Píštěk, P. Kučera, O. Shumylo and V. Kyrnats, “Prediction of centrifugal compressor instabilities for internal combustion engines operating cycle simulation,” Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering. 2023, vol. 237, no. 2-3, pp. 572584, doi: 10.1177/09544070221075419.
  • 22. Н. Ф. Разлейцев, Моделирование и оптимизация процесса сгорания в дизелях, Харьков: Вища школа, 1980, p. 169.
  • 23. А. Ф. Шеховцов, Ф. И. Абрамчук and В. И. и. д. Крутов, Процессы в перспективных дизелях, Харьков: Основа, 1992, p. 352.
  • 24. L. Grekhov, K. Mahkamov and A. Kuleshov, “Optimization of Mixture Formation and Combustion in Two-Stroke OP Engine Using Innovative Diesel Spray Combustion Model and Fuel System Simulation Software,” SAE. 2015, 2015-011859, doi: 10.4271/2015-01-1859.
  • 25. A. Kuleshov, K. Mahkamov, A. Kozlov and Y. Fadeev, “Simulation of dual-fuel diesel combustion with multi-zone fuel spray combustion model,” Proceedings of the ASME 2014 Internal Combustion Engine Division Fall Technical Conference. 2014, pp. 1-13, doi: 10.1115/ICEF2014-5700.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-6982dd73-74df-4ba5-8d5f-1bcbead4228c
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.