Ship owners should comply with the forthcoming IMO legislations that mandates a reduction of ship emissions of at least 40% by 2030 compared with the 2008 baseline. However, it is unlikely that the shipping industry will be able to achieve its 2030 and 2050 emission reduction targets relying only on existing vessel technologies. Hence, the required green ship technologies that relate to industrial digitalization and AI applications should be utilized onboard vessels to achieve these emission reduction targets. This study proposes to analyze a hybrid engine-propeller combinator diagram from both theoretical calculations, i.e. from the vessel hull design, as well as data driven calculations, i.e. from ship performance and navigation data sets, to compare their performance in a single model framework. That would consist of various machine learning applications to create AI. It is expected that such combinations will support to understand the variations among system-model uncertainties in vessels and ship systems as a system of systems and that can also support industrial digitalization in shipping. Furthermore, the hybrid engine-propeller combinator diagram can be utilized to establish the basis for advanced data analytics that will be used to identify optimal vessel navigation and ship system operational conditions.

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