The DNV Nordic Maritime Universities Workshop is organized as a collaboration between DNV and universities in the Nordic region with a maritime related education or research line. The workshop covers all research topics related to naval architecture, maritime engineering and maritime transport, including safety, energy efficiency and environmental performance, environmental pressures, new technologies and digitalization. The 25th Nordic Maritime Universities Workshop was held on 30-31 January 2025 at the Technical University of Denmark (DTU), Lyngby Campus. The workshop has been organized and hosted by the Maritime Group at the Department of Civil and Mechanical Engineering (DTU Construct). In total we received 77 abstracts from 7 countries. This includes 23 abstracts from Denmark, 23 from Sweden, 16 from Norway, 10 from Germany, 3 from Finland, 1 from The Netherlands, and 1 from Poland. The presentation of the abstracts and the talks is carried out over two days of the workshop and in 10 sessions, distributed over 7 topics:
• Maritime Safety & Risk Reduction (17 talks)
• Structures & Ship Design (8 talks)
• Numerical Methods & Marine Hydrodynamics (14 talks)
• Ship Operations & Navigation (14 talks)
• Autonomous Shipping & Digitalization (8 talks)
• Alternative Marine Fuels (8 talks)
• Wind Assisted & Alternative Propulsion (8 talks)
This year a special issue has been initiated in International Shipbuilding Progress to commemorate the 25th Nordic Maritime Universities Workshop. All abstract presenters have been invited to submit a full paper, to be considered for publication in this journal after a peer-review process. This compendium includes the workshop program, the session details and the 77 abstracts arranged in alphabetical order.
Our research highlights the current state and trends of artificial intelligence (AI) adoption in Denmark’s chartering, particularly in the dry bulk and tanker segments. Companies in the dry bulk sector are leading AI adoption, with the tanker segment closely following and adoption rates in our sample appear higher than national averages reported by consultancies. Most firms are in either the experimental phase or transitioning toward more integrated AI systems, often opting for hybrid models that allow them to maintain internal control over key processes. Factors such as company size and maturity also influence the pace and approach to AI adoption.AI is seen as a tool to enhance rather than replace jobs in the early stages of shipping operations, especially in pre-fixture activities. However, there is greater potential for automation and job substitution in the post-fixture phase, particularly in tasks such as contract (CP) management.
On the supply side, the market for maritime AI and software solutions is highly competitive and fragmented, with many providers offering diverse products. Recent consolidation trends reflect different strategies: some companies, like are specializing in core offerings, while others, like are diversifying into both SaaS and pure software models. These consolidations are not only intensifying competition but also fostering partnerships between rivals—a dynamic known as coopetition. Interestingly, some shipping firms are entering the software market themselves, signaling innovation in business models. Machine learning (ML) technologies are primarily used in pre-fixture tools (like email management and tracking), while generative AI is increasingly applied in post-fixture functions, particularly contract management.
The rapid growth of e-commerce applications has promoted the establishment of shipping e-commerce channels by many liner companies in addition to their existing traditional Non-vessel operating common carrier (NVOCC) channel. Unlike NVOCC channels, shipping e-commerce channels guarantee shippers the availability of contracted container slots. However, some problems arise, including the competition with NVOCC channels, shipping slot sales’ risk, and the increasing liner companies’ costs. Therefore, this paper addresses optimal sales strategy selection in the liner transportation industry, including a single traditional NVOCC channel (TN) strategy, and a dual channel with both e-commerce and NVOCC channels (EN) strategy. Two contract scheme models are constructed considering the channel competition on canvassing ability, overselling behavior, demand fluctuation, and the limited liner vessel capacity. Findings show that the impact of overselling behavior on the profit under the EN and TN is not always negative, which is related to the shipping capacity and probability of the high canvassing ability. Comparative analyses reveal that the EN is dominant if the unit overselling compensation cost varies small. Meanwhile, the TN is profitable if the unit overselling compensation cost increases and the canvassing cost of e-commerce channel exceeds a certain value. Otherwise, the selection of sales strategy relies on the arrival rate, the canvassing cost of the e-commerce channel and shipping capacity. The results offer new insights to both theoretical research on container slot sales and the practical selection of sales strategy since shipping e-commerce has changed the slot selling mode in the container shipping industry, which could also enhance the competitiveness of liner companies in the container shipping industry.
The oceans, covering approximately 70% of Earth's surface, play a pivotal role in climate regulation, biodiversity, and biogeochemical processes. The large and growing volume and complexity of ocean data, spanning diverse disciplines and formats, and dispersed across a wide range of sources, presents opportunities and challenges for advancing scientific research, informing policy, and addressing societal needs.
In this review paper we aim to create an easy-to-navigate map of the field of ocean data, enabling the reader to establish a broad understanding of the ocean data sector, and bridging gaps between different disciplines and levels of familiarity with ocean data. This is done through the concept of the "data ecosystem", which is used to describe the actors, organisations, and infrastructures involved in all aspects of the data value chain. We propose a structured ocean data ecosystem model as a method for comprehensive mapping of the ocean data market landscape. The proposed model consists of five key elements: stakeholders, societal elements, data sources and product offering, standards and best practices, and emerging technologies. We provide an up-to-date analysis of ocean data sources and emerging solutions and a summary of relevant data standardization efforts such as marine standards, vocabularies, and ontologies. All this will promote the development of needs-based solutions, components, products, services, and technologies, thus contributing to the evolution of the ocean data ecosystem and promoting data-based ocean research.
Due to limited access to domain knowledge and domain-relevant benchmark data, the Container Stowage Planning Problem (CSPP) is notably under-researched. In particular, previous models of the CSPP have lacked two key aspects of the problem: lashing forces and paired block stowage. The former may reduce vessel capacity by up to 10%, and the latter is NP-hard. The Representative CSPP (RCSPP), which captures all critical aspects of the problem is formulated. The presented RCSPP incorporates overlooked constraints such as paired block stowage and lashing, along with an innovative method for estimating lashing forces, all while maintaining simplicity. A heuristic method, STOW, has been developed to identify solutions for the RCSPP using a specially designed benchmark suite based on real-world scenarios. STOW algorithm is an advanced search heuristic employing a diverse range of solution modification strategies, each tailored to address specific aspects of stowage optimization. Feasible solutions were successfully identified for all instances within the benchmark suite. Our initial findings emphasize the importance of accurately modeling lashing forces and employing paired block stowage. Results show that removing the lashing constraint can increase the number of containers stowed by over 7% on average, while disabling paired block stowage can result in nearly a 5% increase.
Since the outbreak of COVID-19, its impacts on the maritime transportation and logistics field have been multi-dimensional. In addition to the green shipping corridor proposed by the Clydebank Declaration in the United Kingdom in 2021, port digitalisation and decarbonisation of the maritime industry have become focal issues in the field. The industry needs a new framework to offset the negative impacts of the pandemic and to accommodate integrated technologies comprising of artificial intelligence (AI), blockchain, cloud systems, internet of things (IoT) and others, which have been applied to the industry. Having considered these circumstances, this paper aims to propose the 6th-generation ports model with smart port (6GP) as a new framework for the port logistics industry in the post-COVID-19 period. The proposed 6GP contributes to providing business development strategy and port development policy for stakeholders in the industry in the post-pandemic era reflecting focal challenges such as digitalisation, decarbonisation, sustainability and smart transformation. It also contributes to expanding port devolution theory from the fifth-generation ports (5GP) to 6GP.
Each year maritime accidents occur at sea causing human casualties. Training facilities serve to reduce the risk of human error by allowing maritime teams to train safety procedures in cooperative real-size immersive simulators. However, they are expensive and only few maritime professionals have access to such simulators. Virtual Reality (VR) can provide a digital all-immersive learning environment at a reduced cost allowing for increased access. However, a key ingredient of what makes all-immersive physical simulators effective is that they allow for multiple participants to engage in cooperative social interaction. Social interaction which allows trainees to develop skills and competencies in navigating situational awareness essential for safety training. Social interaction requires social fidelity. Moving from physical simulators into digital simulators based upon VR technology thus challenges us as HCI researchers to figure out how to design social fidelity into immersive training simulators. We explore social fidelity theoretically and technically by combining core conceptual work from CSCW research to the design experimentation of social fidelity for maritime safety training. We argue that designing for social fidelity in VR simulators requires designers to contextualize the VR experience in location, artifacts, and actors structured through dependencies in work allowing trainees to perform situational awareness, coordination, and communication which are all features of social fidelity. Further, we identify the risk of breaking the social fidelity immersion related to the intent and social state of the participants entering the simulation. Finally, we suggest that future designs of social fidelity should consider not only trainees in the design, but also the social relations created by the instructors’ guidance as part of the social fidelity immersion.
Accurate and reliable optical remote sensing image-based small-ship detection is crucial for maritime surveillance systems, but existing methods often struggle with balancing detection performance and computational complexity. In this paper, we propose a novel lightweight framework called HSI-ShipDetectionNet that is based on high-order spatial interactions and is suitable for deployment on resource-limited platforms, such as satellites and unmanned aerial vehicles. HSI-ShipDetectionNet includes a prediction branch specifically for tiny ships and a lightweight hybrid attention block for reduced complexity. Additionally, the use of a high-order spatial interactions module improves advanced feature understanding and modeling ability. Our model is evaluated using the public Kaggle and FAIR1M marine ship detection datasets and compared with multiple state-of-the-art models including small object detection models, lightweight detection models, and ship detection models. The results show that HSI-ShipDetectionNet outperforms the other models in terms of detection performance while being lightweight and suitable for deployment on resource-limited platforms.
Recent advances, especially in deep learning, allow to effectively detect ship targets in surveillance videos. However, the translation of these detections to the real-world locations of ships has not been sufficiently explored. The common approach in the literature is using a transformation matrix to convert a pixel to a real-world coordinate. However, this approach has three shortcomings: first, a set of reference point pairs has to be manually prepared to establish the matrix; second, the matrix always maps a pixel to the same real-world coordinate, ignoring that there is no one-to-one correspondence between discrete pixel coordinates and continuous real-world coordinates; third, this approach can only work with one camera. In light of this, we propose a technique PixelToRegion that explicitly takes into account the uncertainty in coordinate conversion by mapping each pixel to a spatial polygon. Next, we propose a new algorithm MCbSLE that can estimate ship locations using pixel sets from multiple cameras. The precision of location estimation by MCbSLE is enhanced through spatial intersection between polygons from different cameras. Experiments are conducted under 16 carefully designed multi-camera settings to evaluate MCbSLE wrt four factors: different ports, the number of cameras, the distance between cameras, and camera headings. Results on one-day ship trajectory data show that (1) an 79.8% accuracy in the number of coordinates can be achieved by MCbSLE when there are no more than 10 ships in camera views; (2) using multiple cameras can improve the precision of location estimation by one order of magnitude compared with using one camera.
Ship engines are subject to a very demanding work environment, where maximum availability is a must. In this project we look at different operational variables of a marine engine from large cargo ships, with the aim of detecting and trending damage onset on different engine sub-components. This information can be used by owners to expedite O&M interventions and maximize ship availability.