ABSTRACT
ABSTRACT A digital twin is a dynamic virtual representation of a physical system that may be used to provide support for life-cycle management decisions. Fundamental exploration of algorithms and approaches to link, compare, and fuse numerical models used in digital twins with real-world sensor data is still needed for ship performance prediction, condition assessment, and ultimately, significant implementation of digital twin technology in the marine industry. In this work, a preliminary digital twin framework for surface ships has been developed to yield time-and-place specific predictions of vessel motions and structural responses given weather forecast or hindcast data for a selected route. Cumulative fatigue damage was predicted and compared for four simulated routes in the Pacific Ocean, and the predicted motions for the simulation with the greatest fatigue damage were analyzed to investigate the possible causes of this increased damage. The notable increase in cumulative damage seen for this route stresses the importance of the ability to track and balance fatigue damage among ships in a fleet. Moreover, this information would further support educated maintenance and deployment scheduling decisions to ensure fatigue damage equality among ships in a fleet, while real-time implementation of this digital twin technology would furnish operators with a greater understanding of a weather forecast's implications, and thereby provide in-mission decision support.
