A Ship Digital Twin for Safe and Sustainable Ship Operations

Spyros Hirdaris, Mingyang Zhang, Nikolaos Tsoulakos, Pentti Kujala

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaChapterScientificvertaisarvioitu

23 Lataukset (Pure)

Abstrakti

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.
AlkuperäiskieliEnglanti
OtsikkoState-of-the-Art Digital Twin Applications for Shipping Sector Decarbonization
ToimittajatBill Karakostas, Takis Katsoulakos
KustantajaIGI Global
Luku9
Sivut192-220
Sivumäärä29
ISBN (elektroninen)978-1-6684-9849-1
ISBN (painettu)978-1-6684-9848-4
DOI - pysyväislinkit
TilaJulkaistu - 2024
OKM-julkaisutyyppiA3 Kirjan tai muun kokoomateoksen osa

Julkaisusarja

NimiAdvances in Logistics, Operations, and Management Science (ALOMS)
KustantajaIGI Global
ISSN (painettu)2327-350X
ISSN (elektroninen)2327-3518

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