Session: 12-07-01 Blue Economy VII: Floating Offshore Wind and Green Shipping Technologies

Submission Number: 181723

Charting Smarter Marine Operations With Digital Twin Technology 

This paper presents the development and validation of a digital twin for a ship’s propulsion system, aimed at enhancing operational efficiency and maintenance strategies for ocean-going vessels. The propulsion system of most vessels engaged in international trade—typically comprising a slow-speed, reversible two-stroke diesel engine directly driving a large propeller—is subject to performance variability due to varying sea conditions, hull and propeller fouling, and engine torque limitations.

A comprehensive mathematical model was constructed using MATLAB Simulink, incorporating more than 50 coupled equations to simulate the translational and rotational dynamics of the propulsion system. Notably, the model accounts not only for steady-state conditions but also for fluctuating environmental loads and main engine characteristics—such as the interaction between wave-induced resistance and the main engine’s torque limit—which are often overlooked in conventional approaches. This inclusion enables a more realistic representation of propulsion behavior under actual sea states.

The digital twin accurately predicts the ship’s speed–power relationship across a range of operational scenarios. Validation was performed by comparing model outputs with measured data under real sea conditions, including wave height and direction. This resulted in a high-fidelity match between the model and observed performance, moving beyond qualitative agreement to demonstrate quantitative accuracy.

Its predictive capabilities support three key applications: (1) Operational limit prediction—The model identifies discrepancies between commanded and actual engine speeds, particularly near the engine’s torque limit, where performance degradation is most critical. This is especially relevant for ships built in the past decade, which are often equipped with lower-powered engines relative to displacement to improve greenhouse gas emission indices. The model helps avoid fuel inefficiencies, potentially saving up to $70,000 annually for Panamax bulk carriers operating on North Atlantic routes. (2) Leg-based speed optimization—By forecasting sea conditions and adjusting speed allocation daily, the digital twin supports voyage planning that minimizes fuel consumption while maintaining schedule integrity, yielding up to 3% fuel savings. (3) Fouling management—The model estimates hull and propeller fouling levels based on performance deviations, enabling strategic cleaning decisions that balance maintenance costs and fuel efficiency, with potential savings of up to 15% over a vessel’s operational period.

In addition to these applications, the digital twin provides insight into the coupled dynamics of translational thrust and rotational torque, emphasizing their interdependence. This holistic modeling approach supports more informed decision-making in propulsion management, particularly under challenging environmental conditions.

This research demonstrates the potential of digital twin technology to support data-driven decision-making in ship propulsion, contributing to fuel efficiency, emissions reduction, and cost-effective fleet operations. The approach is adaptable across vessel types and trading routes, offering significant value to ship operators, marine engineers, and fleet managers seeking to optimize performance in increasingly complex maritime environments.

Presenting Author: Rei Miratsu Nippon kaiji kyokai (ClassNK)

Presenting Author Biography: Rei Miratsu is a researcher at the Research Institute of Nippon Kaiji Kyokai (ClassNK). He earned his Master’s degree in Environmental Studies from the Department of Ocean Technology, Policy, and Environment at the University of Tokyo in 2016 and commenced his career at the Research Institute of ClassNK in the same year. His research is dedicated to advancing safety and environmental protection in the maritime industry. Specifically, his work encompasses the analysis of ship operations through the application of big data, involvement in multiple full-scale ship measurement projects, and the development of fleet prediction simulations for alternative fuel vessels.

Authors:

Takuya Wako Nippon kaiji kyokai (ClassNK)
Rei Miratsu Nippon kaiji kyokai (ClassNK)
Yuzhong Song Nippon kaiji kyokai (ClassNK)
Michio Takagi Nippon kaiji kyokai (ClassNK)
Junshi Takashina Nippon kaiji kyokai (ClassNK)