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.
We present a machine learning model for calculation of wave added resistance. The model training is performed using a large set of pre-calculated added resistance curves covering a broad range of ship hulls and operational conditions, i.e. forward speed, draft and relative wave heading. The underlying hydrodynamic model is the classical strip-theory where the wave added resistance is computed according to a modified version of Salvesen’s formulation. It is concluded that the developed data-driven model is able to produce a non-linear mapping between a set of operational conditions as well as the ship’s main particulars to the wave added resistance coefficient.
Efficient control schemes of Autonomous underwater vehicle (AUV) are challenging due to uncertainties and highly nonlinearities. In this paper, improved fractional order PID controller is proposed for the control of AUV motion with six degrees of freedom (DOF). Genetic algorithm and Particle Swarm Optimization (PSO) are employed to find suboptimal coefficients of FOPID controller to improve performance of the AUV motion. These optimal adjusted coefficients of FOPID controllers minimize the step response characteristics such as maximum deviation and settling time. Simulation results are presented to verify the advantages of the FOPID with respect to the previous works specially proportional-integral-derivative controller (PID).
In recent years, shipboard microgrids (MGs) have become more flexible, efficient, and reliable. The next generations of future shipboards are required to be equipped with more focuses on energy storage systems to provide all-electric shipboards. Therefore, the shipboards must be very reliable to ensure the operation of all parts of the system. A reliable shipboard MG should be pro-tected from system faults through protection selectivity to minimize the impact of faults and facili-tate detection and location of faulty zones with the highest accuracy and speed. It is necessary to have an across-the-board overview of the protection systems in DC shipboards. This paper provides a comprehensive review of the issues and challenges faced in the protection of shipboard MGs. Furthermore, given the different types of components utilized in shipboard MGs, the fault behavior analysis of these components is provided to highlight the requirements for their protection. The protection system of DC shipboards is divided into three sub-systems, namely, fault detection, lo-cation, and isolation. Therefore, a comprehensive comparison of different existing fault detection, location, and isolation schemes, from traditional to modern techniques, on shipboard MGs is presented to highlight the advantages and disadvantages of each scheme.
The cold ironing system is gaining interest as a promising approach to reduce emissions from ship transportation at ports, enabling further reductions with clean energy sources coordination. While cold ironing has predominantly been applied to long-staying vessels like cruise ships and containers, feasibility studies for short-berthing ships such as ferries are limited. However, the growing demand for short-distance logistics and passenger transfers highlights the need to tackle emissions issues from ferry transportation. Incorporating electrification technology together with integrated energy management systems can significantly reduce emissions from ferry operations. Accordingly, this paper proposes a cooperative cold ironing system integrated with clean energy sources for ferry terminals. A two-stage energy management strategy combining sizing and scheduling optimization is employed to reduce the port's emissions while minimizing system and operational costs. The proposed system configuration, determined through the sizing method, yields the lowest net present cost of $9.04 M. The applied energy management strategy managed to reduce operational costs by up to 63.402 %, while significantly decreasing emissions from both shipside and shoreside operations. From the shipside, emissions reductions of 38.44 % for CO2, 97.7 % for NOX, 96.69 % for SO2, and 92.1 % for PM were achieved. From the shoreside, the approach led to a 28 % reduction across all emission types. Thus, implementing cold ironing powered by clean energy sources is a viable solution for reducing emissions generated by ferry operations. The proposed energy management approach enables emissions reduction and delivering cost-effectiveness at ferry terminals.
Determining the key characteristics of a ship during the concept and preliminary design phases is a critical and intricate process. In this study, we propose an alternative to traditional empirical methods by introducing a model to estimate the main particulars of diesel-powered Z-Drive harbor tugboats. This prediction is performed to determine the main particulars of tugboats: length, beam, draft, and power concerning the required service speed and bollard pull values, employing Bayesian network and non-linear regression methods. We utilized a dataset comprising 476 samples from 68 distinct diesel-powered Z-Drive harbor tugboat series to construct this model. The case study results demonstrate that the established model accurately predicts the main parameters of a tugboat with the obtained average of mean absolute percentage error values; 6.574% for the Bayesian network and 5.795%, 9.955% for non-linear regression methods. This model, therefore, proves to be a practical and valuable tool for ship designers in determining the main particulars of ships during the concept design stage by reducing revision return possibilities in further stages of ship design.
Satellite imagery has become a fundamental part for maritime monitoring and safety. Correctly estimating a ship's identity is a vital tool. We present a method based on facial recognition for identifying ships in satellite images. A large ship dataset is constructed from Sentinel-2 multispectral images and annotated by matching to the Automatic Identification System. Our dataset contains 7.000 unique ships, for which a total of 16.000 images are acquired. The method uses a convolutional neural network to extract a feature vector from the ship images and embed it on a hypersphere. Distances between ships can then be calculated via the embedding vectors. The network is trained using a triplet loss function, such that minimum distances are achieved for identical ships and maximum distances to different ships. Comparing a ship image to a reference set of ship images yields a set of distances. Ranking the distances provides a list of the most similar ships. The method correctly identifies a ship on average 60 % of the time as the first in the list. Larger ships are easier to identify than small ships, where the image resolution is a limitation.
The offshore de-oiling process is a vital part of current oil recovery, as it separates the profitable oil from water and ensures that the discharged water contains as little of the polluting oil as possible. With the passage of time, there is an increase in the water fraction in reservoirs that adds to the strain put on these facilities, and thus larger quantities of oil are being discharged into the oceans, which has in many studies been linked to negative effects on marine life. In many cases, such installations are controlled using non-cooperative single objective controllers which are inefficient in handling fluctuating inflows or complicated operating conditions. This work introduces a model-based robust H ∞ control solution that handles the entire de-oiling system and improves the system’s robustness towards fluctuating flow thereby improving the oil recovery and reducing the environmental impacts of the discharge. The robust H ∞ control solution was compared to a benchmark Proportional-Integral-Derivative (PID) control solution and evaluated through simulation and experiments performed on a pilot plant. This study found that the robust H ∞ control solution greatly improved the performance of the de-oiling process.
There has been a continued increase in the load on the current offshore oil and gas de-oiling systems that generally consist of three-phase gravity separators and de-oiling hydrocyclones. Current feedback control of the de-oiling systems is not done based on de-oiling efficiency, mainly due to lack of real-time monitoring of oil-in-water concentration, and instead relies on an indirect method using pressure drop ratio control. This study utilizes a direct method where a real-time fluorescence-based instrument was used to measure the transient efficiency of a hydrocyclone combined with an upstream gravity separator. Two control strategies, a conventional PID control structure and an H ∞ robust control structure, both using conventional feedback signals were implemented, and their efficiency was tested during severely fluctuating flow rates. The results show that the direct method can measure the system's efficiency in real time. It was found that the efficiency of the system can be misleading, as fluctuations in the feed flow affect the inlet concentration more than the outlet oil concentration, which can lead to a discharge of large oil quantities into the ocean.
Injection of water into wells is a common practice in offshore oil and gas installations, and here as in many other industries the water has to be deaerated before it is sent through miles of pipelines to reduce the risk of corrosion in those pipelines and other downstream equipment. It requires extremely low concentrations of dissolved oxygen for the corrosion of metals to begin, and removing the dissolved oxygen is currently done in large vacuum deaeration towers, a highly energy demanding process, along with additional injection of chemical oxygen scavengers. In many instances these processes are controlled in a feed-forward manner, where the operators rely on infrequent sampling and corresponding measurements to control the process. The possibilities for optimization in this field are thus numerous. The main challenges are online measurements of dissolved oxygen and their use in feedback control. This article gives a brief review of the state-of-the-art and investigates the potential of using dissolved oxygen as a reliable feedback parameter, taking inspiration from onshore waste water industries which have been dealing with dissolved oxygen feedback control since the 1970's.