Shipping Digital Twin Data Management With the Use of Knowledge Graphs

Abstract

This chapter explores the data management challenges inherent in a digital twin framework and elucidates the pivotal role of knowledge graphs in meeting these demands. The author introduces a functional metamodel designed to semantically convert scenarios related to the green transition into a standardized format. This metamodel emerges as a vital instrument for both business and technical stakeholders, streamlining the intricate interplay among operational requisites, environmental factors, optimization strategies, and decarbonization technologies. It adeptly encapsulates the ship's environmental and economic performance indicators. In synergy with the metamodel, a knowledge graph (KG) encodes industry-specific parameters, laying a robust groundwork that depicts the vessel's functionalities, variables, and operational processes, and serves as a repository for model metadata. This facilitates the automation of shortest path computations, effectively bridging the divide between overarching platform operations and the complex nuances of maritime assets and activities. The utility of the metamodel and knowledge graph in translating real-world issues into a digital twin-compatible format is exemplified through a case study focusing on the application of variable frequency drives for enhancing the efficiency of the high-temperature cooling system. The chapter concludes by summarizing the data management hurdles encountered in maritime digital twins and offering a perspective on future developments.