A Ship Digital Twin for Safe and Sustainable Ship Operations

Spyros Hirdaris, Mingyang Zhang, Nikolaos Tsoulakos, Pentti Kujala

Research output: Chapter in Book/Report/Conference proceedingChapterScientificpeer-review

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Abstract

Shipping is responsible for over 90% of global trade. Although it is generally considered a safe and clean mode of transportation, it still has a significant impact on the environment. Thus, state-of-the-art models that may contribute to the sustainable management of the life cycle of shipping operations without compromising safety standards are urgently needed. This chapter discusses the potential of artificial intelligence (AI) based digital twin models to monitor ship safety and efficiency. A paradigm shift is introduced in the form of a model that can predict ship motions and fuel consumption under real operational conditions using deep learning models. A bi-directional long short-term memory (LSTM) network with attention mechanisms is used to predict ship fuel consumption and a transformer neural network is employed to capture ship motions in realistic hydrometeorological conditions. By comparing the predicted results with available full scale measurement data, it is suggested that following further testing and validation, these models could perform satisfactorily in real conditions. Accordingly, they could be integrated into a framework for safe and sustainable ship operations.
Original languageEnglish
Title of host publicationState-of-the-Art Digital Twin Applications for Shipping Sector Decarbonization
EditorsBill Karakostas, Takis Katsoulakos
PublisherIGI Global
Chapter9
Pages192-220
Number of pages29
ISBN (Electronic)978-1-6684-9849-1
ISBN (Print)978-1-6684-9848-4
DOIs
Publication statusPublished - 2024
MoE publication typeA3 Book section, Chapters in research books

Publication series

NameAdvances in Logistics, Operations, and Management Science (ALOMS)
PublisherIGI Global
ISSN (Print)2327-350X
ISSN (Electronic)2327-3518

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