TwinShip was presented at SMM Hamburg 2026

🚢 TwinShip Consortium Project, Advancing Digital Twins for Greener and Smarter Shipping was presented at SMM Hamburg by Anders Öster and Alexander Ozersky (Wärtsilä Marine), Konstantinos Voutzoulidis (American Bureau of Shipping (ABS), and Ruihua LU (Stena Line and Stena Bulk).

The TwinShip Horizon Europe project is developing an integrated Digital Twin (DT) framework that combines real-time vessel data, physics-based models, domain knowledge, and AI-driven decision support to improve ship efficiency, safety, and environmental performance. The key takeaways:

🌊 Key capabilities include weather routing and voyage optimization, retrofit analysis, life-cycle cost and environmental impact assessment, what-if simulations, emissions budgeting, and advanced data/AI services.

🔗 Building on the VesselAI platform, TwinShip is also progressing toward integration with commercial fleet-management and onboard navigation solutions, enabling digital models and operational data to support real-world voyage planning and fleet operations.

⚓ Through pilot demonstrations, data-quality assurance, validation methodologies, and industry collaboration, TwinShip aims to provide practical digital pathways toward the decarbonization and digitalization of shipping.
smm1

smm3

 

events

TwinShip Consortium Podcast 1 with Spiros Mouzakitis

🚢 TwinShip: Turning Maritime Data into Trustworthy Decisions with AI & Digital Twins

Shipping is one of the foundations of the global economy 🌍 — but the industry is now facing a major transformation: becoming cleaner, smarter and more efficient while maintaining the highest levels of safety and reliability.

In a recent TwinShip interview, Dr. Spiros Mouzakitis from the Institute of Communication and Computer Systems (ICCS) discussed how Digital Twins, Artificial Intelligence, Machine Learning and secure data platforms can support this transformation.

💡 One important message stood out:

👉 “More data doesn’t necessarily mean better decisions.”

Reliable digitalisation depends not only on collecting large volumes of data, but also on ensuring that the data is accurate, contextualised, secure and trustworthy.

🔹 Digital Twins can create virtual representations of vessels and their systems, allowing operators to test scenarios, evaluate performance and investigate future operational conditions with lower cost and risk.

🔹 Machine Learning & AI can identify patterns in vessel data and support predictions related to vessel behaviour, performance, fuel consumption and operational efficiency.

🔹 Open-source platforms can accelerate maritime innovation by enabling collaboration while still protecting companies’ proprietary data, models and intellectual property. 🔐

🔹 Explainability and trustworthiness are essential. In safety-critical industries such as shipping, AI must do more than provide an answer — users must understand the uncertainty, reliability and reasoning behind the recommendation.

⚠️ And when it comes to Generative AI and Large Language Models, the message is equally important:

We should not follow the hype blindly.

Instead, the maritime sector should gradually introduce AI into appropriate applications, carefully assess risks and benefits, and focus on developing Trustworthy AI.

🤝 TwinShip is bringing together shipowners, technology providers, researchers, software developers and classification experts to bridge the gap between research and real maritime operations.

🌱 The ultimate objective is clear: provide evidence-based and trustworthy digital decision support that can help accelerate shipping’s digital and green transition.

🚢 Better data. Better models. Better decisions. A smarter and greener future for shipping.


The newspaper article: https://www.linkedin.com/pulse/twinship-charts-smarter-course-shipping-trustworthy-ai-digital-xmxie/

video

TwinShip Supported the 7th Data Science & AI Summer School!

The TwinShip Horizon Europe project was proud to support the 7th Data Science and AI Summer School, organised by DataPACT, held in Predeal, Romania, from 18–26 July 2026.

The event brought together students, researchers, and experts to exchange knowledge and explore the latest developments in Data Science, Artificial Intelligence, and data-driven innovation.

Organised by the Bucharest Business School (BBS @ ASE), GATE Institute, and the European projects DataPACT project, INTEND, and CauseFinder: Causality in the Era of Big Data and AI, the summer school featured an engaging combination of lectures, expert discussions, and hands-on sessions.

Through initiatives like this, TwinShip Consortium is pleased to contribute to strengthening knowledge exchange, skills development, and collaboration across the European research and innovation community. 🌍🤝
111
222

workshop

TwinShip Data Management Plan – Version 2 Published

We are pleased to present the second version of the annually updated TwinShip Data Management Plan, which defines how data will be collected, generated, processed, stored, shared, and protected throughout the project. The first version is available on ResearchGate. 

📊 A comprehensive project data ecosystem

TwinShip is expected to manage approximately 5–6 TB of data, depending on the vessel type, operational route, and data-collection activities. The datasets are organised into five main categories:

🚢 Ship performance data
📈 Business constraints data
🌱 Green fuel and technology data
⚓ Online port data
🌦️ Weather data

These datasets are fundamental to developing and validating TwinShip’s digital-twin technologies and achieving the project’s research and innovation objectives.

🔓 Open science with responsible data governance

TwinShip aims to make project data publicly available whenever possible. However, datasets containing commercially sensitive or business-confidential information will remain protected, based on decisions made by the project consortium.

💻 Project-generated software will be released as open source.
📚 Scientific publications will be made available through open-access channels.
🔎 Supporting the FAIR principles

All project data will be managed according to the FAIR principles:

✅ Findable: Supported by globally unique and persistent identifiers.
✅ Accessible: Available through standardised communication protocols, with session- and API-key-based authentication and authorisation.
✅ Interoperable: Structured using recognised vocabularies and ontologies, including the VesselAI ontology.
✅ Reusable: Documented and governed to support future scientific, industrial, and technological applications.

☁️ The datasets will be hosted through the VesselAI platform, while data security will be maintained through encryption, access controls, data-space restrictions, and auditing mechanisms.

The Data Management Plan follows the European Commission’s Guidelines on FAIR Data Management in Horizon 2020, supporting responsible research, open science, and secure data sharing.

deliverables

ICE 2026 - Conference Paper

A conference paper is published under the TwinShip Consortium Horizon Europe Project with the title of 'Enabling Trustworthy Maritime AI Decision Support: An Integrated Platform Architecture' by the authors of Afroditi Blika, Anastasia Askouni, Theodoros Florakis, Ariadni Michalitsi-Psarrou, Georgios Grigorios Klavdianos, Giannis Xidias, Loukas Ilias, Spiros Mouzakitis. The paper was presented by Afroditi Blika at the 32nd IEEE International Conference on Engineering, Technology and Innovation (ICE) 2026 At: Porto, Portugal.  cp2

conference

Page 1 of 10