A data-driven digital twin framework to support early-stage ship design
A case study on data-driven engine configuration selection integrating operational data
M.T. Windmeijer (TU Delft - Mechanical Engineering)
B.O.M.N. Noble – Mentor (TU Delft - Ship Design, Production and Operations)
Y. Pang – Graduation committee member (TU Delft - Transport Engineering and Logistics)
Austin Kana – Graduation committee member (TU Delft - Ship Design, Production and Operations)
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Abstract
The maritime industry faces increasing pressure to cut greenhouse gas emissions, yet traditional ship design methods rely on static assumptions and rarely exploit the growing availability of operational data. This paper proposes a Digital Twin (DT)-aided design framework to integrate such data into early-stage ship design. The framework covers data acquisition, modeling, and verification, ensuring that operational insights inform decision-making.
A case study on bulk carrier engine room configurations demonstrates the approach. Using industrial engine datasets and operational profiles derived from Bunker Delivery Notes, a rule-based model generates feasible configurations that are assessed for fuel consumption and CO2 emissions. Results indicate that operational data enables insights into performance forecasts and more informed configuration selection compared to traditional methods.
While the application remains a digital model rather than a full twin, the study shows the potential of DT-aided frameworks to support IMO decarbonization goals and guide future ship design.