IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v196y2020ics0360544220302334.html
   My bibliography  Save this article

Development of a diesel engine’s digital twin for predicting propulsion system dynamics

Author

Listed:
  • Bondarenko, Oleksiy
  • Fukuda, Tetsugo

Abstract

A digital twin is the essential part of a recent and unavoidable trend in ship operation digitalisation. The digital twin is a virtual replica of real ship or a particular system that coexists with its physical counterpart and maps the dynamic behaviour in real-time. Thus, the digital twin combines physical space real-time data with a set of dynamic models representing the physical counterpart in the cyberspace. The problem of digital twin development is a trade-off between insight into the dynamic process and real-time execution constraint. This paper describes a modelling approach that combines continuous time-domain cycle-mean value engine model with the crank-angle resolved phenomenological combustion model, satisfying the real-time execution constraint. The set of conservation laws, notably energy and mass, supplemented with the phenomenological Wiebe combustion model, is treated in the integral form allowing transformation into a set of nonlinear algebraic equations. The solution of the resulting system exhibits fast speed and accuracy as compared with the traditional approach combining differential equations and Runge-Kutta solver.

Suggested Citation

  • Bondarenko, Oleksiy & Fukuda, Tetsugo, 2020. "Development of a diesel engine’s digital twin for predicting propulsion system dynamics," Energy, Elsevier, vol. 196(C).
    • Handle: RePEc:eee:energy:v:196:y:2020:i:c:s0360544220302334
    DOI: 10.1016/j.energy.2020.117126
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544220302334
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2020.117126?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Baldi, Francesco & Theotokatos, Gerasimos & Andersson, Karin, 2015. "Development of a combined mean value–zero dimensional model and application for a large marine four-stroke Diesel engine simulation," Applied Energy, Elsevier, vol. 154(C), pages 402-415.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Semeraro, Concetta & Aljaghoub, Haya & Abdelkareem, Mohammad Ali & Alami, Abdul Hai & Olabi, A.G., 2023. "Digital twin in battery energy storage systems: Trends and gaps detection through association rule mining," Energy, Elsevier, vol. 273(C).
    2. Huang, Yufeng & Tao, Jun & Sun, Gang & Wu, Tengyun & Yu, Liling & Zhao, Xinbin, 2023. "A novel digital twin approach based on deep multimodal information fusion for aero-engine fault diagnosis," Energy, Elsevier, vol. 270(C).
    3. Semeraro, Concetta & Aljaghoub, Haya & Abdelkareem, Mohammad Ali & Alami, Abdul Hai & Dassisti, Michele & Olabi, A.G., 2023. "Guidelines for designing a digital twin for Li-ion battery: A reference methodology," Energy, Elsevier, vol. 284(C).
    4. Wang, Kai & Xue, Yu & Xu, Hao & Huang, Lianzhong & Ma, Ranqi & Zhang, Peng & Jiang, Xiaoli & Yuan, Yupeng & Negenborn, Rudy R. & Sun, Peiting, 2022. "Joint energy consumption optimization method for wing-diesel engine-powered hybrid ships towards a more energy-efficient shipping," Energy, Elsevier, vol. 245(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Di Battista, D. & Cipollone, R., 2016. "Experimental and numerical assessment of methods to reduce warm up time of engine lubricant oil," Applied Energy, Elsevier, vol. 162(C), pages 570-580.
    2. Zhu, Sipeng & Gu, Yuncheng & Yuan, Hao & Ma, Zetai & Deng, Kangyao, 2020. "Thermodynamic analysis of the turbocharged marine two-stroke engine cycle with different scavenging air control technologies," Energy, Elsevier, vol. 191(C).
    3. La Xiang & Enzhe Song & Yu Ding, 2018. "A Two-Zone Combustion Model for Knocking Prediction of Marine Natural Gas SI Engines," Energies, MDPI, vol. 11(3), pages 1-23, March.
    4. Theotokatos, Gerasimos & Guan, Cong & Chen, Hui & Lazakis, Iraklis, 2018. "Development of an extended mean value engine model for predicting the marine two-stroke engine operation at varying settings," Energy, Elsevier, vol. 143(C), pages 533-545.
    5. Ling-Chin, Janie & Roskilly, Anthony P., 2016. "Investigating the implications of a new-build hybrid power system for Roll-on/Roll-off cargo ships from a sustainability perspective – A life cycle assessment case study," Applied Energy, Elsevier, vol. 181(C), pages 416-434.
    6. Sapra, Harsh & Godjevac, Milinko & Visser, Klaas & Stapersma, Douwe & Dijkstra, Chris, 2017. "Experimental and simulation-based investigations of marine diesel engine performance against static back pressure," Applied Energy, Elsevier, vol. 204(C), pages 78-92.
    7. Tang, Yuanyuan & Zhang, Jundong & Gan, Huibing & Jia, Baozhu & Xia, Yu, 2017. "Development of a real-time two-stroke marine diesel engine model with in-cylinder pressure prediction capability," Applied Energy, Elsevier, vol. 194(C), pages 55-70.
    8. Wang, Yuhua & Wang, Guiyong & Yao, Guozhong & Shen, Qianqiao & Yu, Xuan & He, Shuchao, 2023. "Combining GA-SVM and NSGA-Ⅲ multi-objective optimization to reduce the emission and fuel consumption of high-pressure common-rail diesel engine," Energy, Elsevier, vol. 278(PA).
    9. Geertsma, R.D. & Visser, K. & Negenborn, R.R., 2018. "Adaptive pitch control for ships with diesel mechanical and hybrid propulsion," Applied Energy, Elsevier, vol. 228(C), pages 2490-2509.
    10. Geertsma, R.D. & Negenborn, R.R. & Visser, K. & Loonstijn, M.A. & Hopman, J.J., 2017. "Pitch control for ships with diesel mechanical and hybrid propulsion: Modelling, validation and performance quantification," Applied Energy, Elsevier, vol. 206(C), pages 1609-1631.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:196:y:2020:i:c:s0360544220302334. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.