Physical and data-driven models hybridisation for modelling the dynamic state of a four-stroke marine diesel engine

Andrea Coraddu, Miltiadis Kalikatzarakis, Gerasimos Theotokatos, Rinze Geertsma, Luca Oneto

Research output: Chapter in Book/Report/Conference proceedingChapter

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.

Original languageEnglish
Title of host publicationEngine Modeling and Simulation
EditorsAvinash Kumar Agarwal, Dhananjay Kumar, Utkarsha Sonawane
Place of PublicationSingapore
Chapter6
Pages145-193
Number of pages49
Edition1
ISBN (Electronic)9789811686184
DOIs
Publication statusPublished - 2022

Publication series

NameEnergy, Environment, and Sustainability
PublisherSpinger Singapore
ISSN (Print)2522-8366

Keywords

  • marine diesel engine
  • physical models
  • data-driven models
  • hybridisation

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