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Physical and Data-Driven Models Hybridisation for Modelling the Dynamic State of a Four-Stroke Marine Diesel Engine

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Engine Modeling and Simulation

Part of the book series: Energy, Environment, and Sustainability ((ENENSU))

Abstract

Accurate, reliable, and computationally inexpensive models of the dynamic state of combustion engines are a fundamental tool to investigate new engine designs, develop optimal control strategies, and monitor their performance. The use of those models would allow to improve the engine cost-efficiency trade-off, operational robustness, and environmental impact. To address this challenge, two state-of-the-art alternatives in literature exist. The first one is to develop high fidelity physical models (e.g., mean value engine, zero-dimensional, and one-dimensional models) exploiting the physical principles that regulate engine behaviour. The second one is to exploit historical data produced by the modern engine control and automation systems or by high-fidelity simulators to feed data-driven models (e.g., shallow and deep machine learning models) able to learn an accurate digital twin of the system without any prior knowledge. The main issues of the former approach are its complexity and the high (in some case prohibitive) computational requirements. While the main issues of the latter approach are the unpredictability of their behaviour (guarantees can be proved only for their average behaviour) and the need for large amount of historical data. In this work, following a recent promising line of research, we describe a modelling framework that is able to hybridise physical and data driven models, delivering a solution able to take the best of the two approaches, resulting in accurate, reliable, and computationally inexpensive models. In particular, we will focus on modelling the dynamic state of a four-stroke diesel engine testing the performance (both in terms of accuracy, reliability, and computational requirements) of this solution against state-of-the-art physical modelling approaches on real-world operational data.

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Acknowledgements

This project is supported by the Royal Netherlands Navy supplying the operational measurement data from one Holland Class Oceangoing Patrol Vessel and Damen Schelde Naval Shipbuilding.

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Authors and Affiliations

  1. Department of Naval Architecture, Ocean and Marine Engineering, University of Strathclyde, Glasgow, UK

    Miltiadis Kalikatzarakis & Gerasimos Theotokatos

  2. Department of Maritime and Transport Technology, Delft University of Technology, Delft, The Netherlands

    Andrea Coraddu & Rinze Geertsma

  3. DIBRIS, University of Genova, Genoa, Italy

    Luca Oneto

  4. Department of Military Systems & Technology, Netherlands Defence Academy, Breda, The Netherlands

    Rinze Geertsma

Authors
  1. Andrea Coraddu
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  2. Miltiadis Kalikatzarakis
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  3. Gerasimos Theotokatos
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  4. Rinze Geertsma
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  5. Luca Oneto
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Corresponding author

Correspondence to Andrea Coraddu .

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Editors and Affiliations

  1. Department of Mechanical Engineering, Indian Institute of Technology Kanpur, Kanpur, India

    Avinash Kumar Agarwal

  2. Department of Mechanical Engineering, Indian Institute of Technology Kanpur, kanpur, India

    Dhananjay Kumar

  3. Department of Mechanical Engineering, Malaviya National Institute of Technology Jaipur (MNIT), Jaipur, India

    Nikhil Sharma

  4. Department of Mechanical Engineering, Indian Institute of Technology Kanpur, Kanpur, India

    Utkarsha Sonawane

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Coraddu, A., Kalikatzarakis, M., Theotokatos, G., Geertsma, R., Oneto, L. (2022). Physical and Data-Driven Models Hybridisation for Modelling the Dynamic State of a Four-Stroke Marine Diesel Engine. In: Agarwal, A.K., Kumar, D., Sharma, N., Sonawane, U. (eds) Engine Modeling and Simulation. Energy, Environment, and Sustainability. Springer, Singapore. https://doi.org/10.1007/978-981-16-8618-4_6

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  • DOI: https://doi.org/10.1007/978-981-16-8618-4_6

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