Recent Advances in Digital Twin Technologies in the Maritime Industry

Dear Colleagues,

The rise of the Internet of Things (IoT) has caused digital twin technologies to become more affordable and popular in the industrial sector, especially regarding complex equipment. The aerospace and defense industries have been at the forefront of embracing digital twin technologies as part of the Industry 4.0 revolution. By bridging the gap between the virtual and physical worlds, they enable data analysis, minimize downtime, and support future planning. In line with this trend, the maritime sector has prioritized the implementation of data-driven approaches.

The integration of digital twins (DTs) in the shipping industry has significantly enhanced the operational efficiency and prolonged the lifespan of vessels. Ongoing research is focused on developing ship-specific models for maintenance, predictive maintenance, and fault detection; improving the performance of installed energy systems and hull condition monitoring; and reducing costs and emissions. However, the full-scale adoption of digital twins faces many challenges. Among these are the proper combination of data-driven and multi-physics models (which could be used to exploit the most advantageous features of each approach for certain applications), the lack of open and scalable data fusion architectures, cyber security measures, real-time data handling capabilities, and data integrity. Furthermore, extensive research is needed in the areas of sensor technology, signal processing, and machine learning architectures.

This Special Issue on the “Recent Advances in Digital Twin Technologies in the Maritime Industry” is currently accepting submissions in this innovative and promising field within the maritime sector. We are seeking papers that explore various aspects of digital twin technology, from data-driven applications to integrated lifecycle management strategies. We also welcome submissions that address the challenges, boundaries, ethical considerations, and legislative limitations of this sector.

Recommended topics include the following:

• Real case studies;
• Cyber infrastructure;
• IoT and data fusion architectures;
• Green digital twin;
• AI/ML algorithms for vessel operations;
• DT-based vessel or asset lifecycle management;
• Integrated digital twin platforms;
• Cyclic economy of vessels based on DTs;
• Financial and societal aspects of a vessel digital twin.

Dr. Christos Spandonidis
Dr. Efthimios Pariotis
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• maritime industry
• digital twins
• simulation models
• data-driven models
• big data analysis
• sensors
• data acquisition and management
• cyber security
• ship digitalization
• vessel lifecycle management