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

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  • 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
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    References listed on IDEAS

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    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.
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    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).

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