Wind Propulsion Systems (WPS) have gained significant attention as a means of decarbonizing shipping. Limitations in available deck space, emissions reduction targets, and regulatory compliance have led to a wide array of potential WPS configurations, each exhibiting distinct aerodynamic performance and requiring unique optimum sail trims for each unit due to complex interactions. This variability challenges existing aerodynamic models and optimization efforts for maximizing fuel savings. To address this, we present a novel methodology that, for the first time in WPS aerodynamic performance prediction, combines Computational Fluid Dynamics (CFD), independent sail trim optimization, and Machine Learning (ML) to develop surrogate models — Gaussian Process Regression and Feedforward Neural Networks — that rapidly predict aerodynamic performance with CFD-equivalent accuracy. These surrogates capture aerodynamic interactions across various WPS configurations, including unit number, deck arrangement, independent sail trim, hull characteristics, and wind conditions. While employing established ML techniques, our approach is novel in its resource-efficient generation of a comprehensive aerodynamic database, derived from the first in-depth independent trim optimization of a DynaRig case study. Our approach enables the modeling of complex, non-linear interactions that traditional interpolation methods fail to capture. Results show that the developed surrogate models achieve CFD-level accuracy, with an average error below 1 while significantly reducing computational time. This ML-enhanced framework facilitates extensive, rapid WPS design optimizations, supporting efficient integration into performance prediction programs (PPPs) and maximizing fuel savings and emissions reductions tailored to specific routes and wind conditions.Machine Learning; CFD-Simulations; Aerodynamic Performance; Wind Propulsion Systems; Green Shipping; Independent Sail Trim Optimization.
Objective: To promote the physical and mental health of employees in a maritime setting and provide knowledge and tools to assist seafarers in managing daily challenges.
Materials and methods: The intervention drew on a goal-based approach, including workshops, coaching,health checks, interviews, and questionnaires.
Results: A process evaluation was used to explore intervention challenges and barriers. Results show that an intervention at sea is complex and needs flexibility. Findings varied, and the main challenges were low participation in one group and lack of continuity due to Covid-19. Data showed a significant positive shift in how the crew rated perceived stress and a statistically significant increase in intake of salad, fish, and vegetarian food.
Conclusions: Workplace interventions in poor health status settings are complex, necessary, and possible, and management’s participation is crucial. Increased awareness was achieved. Learning outcomes: The results showed some positive changes, such as lower stress levels and more intake of salad, fish, and vegetarian food. Flexibility is important for workplace interventions. Work place interventions contribute to health and wellbeing with appropriate management support.
We consider the Tramp Ship Routing and Scheduling Problem (TSRSP) in which we plan routes for a fleet of tramp shipping vessels operating on a combined contract and spot market. Earlier research has been fragmented due to variations in the side constraints studied. Hence we present the first unified model that can handle speed optimization, chartering costs, bunker planning, and hull cleaning. The model is solved by column generation, where the columns represent the possible routes of a vessel, while the master problem keeps track of the binding constraints. The pricing problem is solved efficiently using a time–space graph and several dominance rules. Real-life instances with up to 40 vessels, 35 geographic regions, and four months planning horizon can be solved to optimality in less than half an hour. The optimized routes increase earnings by 7% compared to historical schedules. Furthermore, policy-makers can use the model as a simulation of a rational agent behavior.
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 importance of reliable battery energy storage systems (BESS) is key to the sustainability of many applications such as renewable power, smart grids, and electric vehicles (EVs). Due to decreasing cost and maturing technology, the Li-ion batteries are now widely used for grid-level storage, grid support for improved power quality, integration with photovoltaic systems, and EV applications. A Li-ion battery pack typically comprises Li-ion cells connected in a suitable combination of series and parallel structure. A battery management system (BMS) is required for charging and discharging, monitoring the current and voltage of each cell or string, battery protection, and temperature control. The system's reliability depends on the BESS reliability and is affected by many factors, including temperature, C-rate, DOD. This research aims to improve BESS reliability by using accurate lifetime modelling for various BMS and converter topologies to identify real-time BESS health and ensure reliability through a suitable control strategy. In particular, the reliability of the BESS for centralized, modularised, distributed, and decentralized topology will be explored along with its cost-reliability trade-off. I will focus on control strategies for optimizing BESS reliability for different applications.
An issue that ROVs experience during operations is disturbances from the tether, making navigation and control more difficult as real-time measurements are not currently available. This paper proposes the development of an innovative sensor that can measure tether forces in multiple degrees of freedom. These tether forces apply an external disturbance during operation, which is difficult to model and predict. The sensor provides real-time input on the effect the tether has on the ROV, which can be utilized in feed-forward in the control system in combination with a feedback loop. There are 2 proposed designs: a 4 DOF sensor design using a plastic bottle and a 6 DOF version utilizing an aluminum cross with hollowed sections. Both designs use strain gauges to measure and determine the direction and magnitude of the force from the tether.
The sensors are implemented to a modified BlueROV2 using ROS. Station-keeping tests in a harbor and test basin are done for the 4 DOF version to evaluate performance. The sensor shows potential, improving response in heave but worsening it in yaw. It removes and adds oscillations both in frequency and amplitude depending on the orientation of the waves relative to the sensor. Indicating alternative control strategies might be more suitable. The 6 DOF version is not tested on the BlueROV2. In future work, additional development is required to ensure the viability of the tether force sensor as a commercial product.
The EU Green Deal calls for a rapid and efficient green transition. On-going climate change and an increasing need for secure and sustainable energy means ambitious projects and goals are accelerated. To expand and exchange offshore wind energy across North Sea neighbouring countries, the Danish government presented in 2020 the Danish North Sea Energy Island (NSEI) project. This pilot project illustrates the shift from ‘nationally individualistic’ modes of connecting offshore wind energy projects, to supplying a multi-lateral renewable offshore energy grid. The Energy Island project builds on the Hub-and-Spoke (H&S) approach, which introduces a new level of complexity to governing the next generation of offshore wind energy projects. This paper analyses the political motivations for the Danish project and the planning and implementation of the Energy Islands, integrating a combination of collaborative and transboundary governance perspectives. The qualitative analysis is based on a document analysis and a literature review. Findings show how planning for the Danish Energy Island has faced delays and challenges, causing uncertainties about the Island’s capability to support Green Deal goals, as well as a mismatch between political ambitions and practical implementation. The artificial offshore island is currently under reconsideration due to costs and is, as of March 2024, still in its planning phase. This case study on the Danish NSEI serves as an introduction to the general functionalities and development of the Island and defines a Danish Energy Island. Results indicate that the combination of transboundary and collaborative governance structures are necessary as part of a successful implementation of Energy Islands.
Driven by increased integration of renewable energy sources, the widespread decarbonization of power systems has led to energy price fluctuations that require greater adaptability and flexibility from grid users in order to maximize profits. Industrial loads equipped with flexible resources can optimize energy consumption rather than merely reacting to immediate events, thereby capitalizing on volatile energy prices. However, the absence of sufficient measured data in industrial processes limits the ability to fully harness this flexibility. To address this challenge, we present a black-box optimization model for optimizing the energy consumption of cooling systems in the aquaculture industry using Extreme Gradient Boosting (XGBoost) and Bayesian Optimization (BO). XGBoost is employed to establish a nonlinear relationship between cooling system power consumption and available measured data. Based on this model, Bayesian Optimization with the Lower Confidence Bound (LCB) acquisition function is used to determine the optimal discharge temperature of water into breeding pools, minimizing day-ahead electricity costs. The proposed approach is validated using real-world data from a case study at the Port of Hirtshals, Denmark based on measurements from 2023. Our findings illustrate that leveraging the inherent flexibility of industrial processes can yield financial benefits while providing valuable signals for grid operators to adjust consumption behaviors through appropriate price mechanisms. Furthermore, machine learning techniques prove effective in optimizing energy consumption for industries with limited measured data, delivering accurate and practical estimates.
The liner shipping industry is undergoing an extensive decarbonization process to reduce its 275 million tons of CO2 emissions as of 2018. In this process, the long-term solution is the introduction of new alternative maritime fuels. The introduction of alternative fuels presents a great set of unknowns. Among these are the strategic concerns regarding sourcing of alternative fuels and, operationally, how the new fuels might affect the network of shipping routes. We propose a problem formulation that integrates fuel supply/demand into the liner shipping network design problem. Here, we present a model to determine the production sites and distribution of new alternative fuels-we consider methanol and ammonia. For the network design problem, we apply an adaptive large neighborhood search combined with a delayed column generation process. In addition, we wish to test the effect of designing a robust network under uncertain demand conditions because of the problem's strategic nature and importance. Therefore, our proposed solution method will have a deterministic and stochastic setup when we apply it to the second-largest multihub instance, WorldSmall, known from LINER-LIB. In the deterministic setting, our proposed solution method finds a new best solution to three instances from LINER-LIB. For the main considered WorldSmall instance, we even noticed a new best solution in all our tested fuel settings. In addition, we note a profit drop of 7.2% between a bunker-powered and pure alternative fuel-powered network. The selected alternative fuel production sites favor a proximity to European ports and have a heavy reliance on wind turbines. The stochastic results clearly showed that the found networks were much more resilient to the demand changes. Neglecting the perspective of uncertain demand leads to highly fluctuating profits.
The offshore oilfields in the North Sea area are increasingly employed for projects beyond oil production, like carbon capture and storage (CCS). Still, the fossil fuel production from mature fields is significant. It has raised environmental concerns associated with discharging produced waters (PW) and drilling mud into the sea. These discharges, which may be highly saline and contain production chemicals, vary significantly in metals and particulate content. Due to density and release depth, the plume is assumed to sink towards the seafloor. Also, a single oilfield can input up to 7.5 tons of Ba, 675 kg of Fe, and 619 kg of P into the water column through PW. Therefore, this study investigates the impact of these discharges on seafloor sediments around two Danish oilfields, assesses the mobility of metals within these sediments, and evaluates the environmental status. PW samples were collected at the discharge outlets from the platforms. Sediment cores were taken near the two oil platforms and from control sites. Using Inductively Coupled Plasma Mass Spectrometry (ICP-MS) and an optimized BCR sequential extraction, we analyzed the composition and distribution of 24 elements in sediment samples. The results revealed significant differences in total extracted concentrations between sediments near the platforms and those from distant locations and stratigraphically older samples, with relevant levels of Br, Ba, and Sn near the platforms (averaged 14, 27, and 0.1 ppb, respectively). Sediment quality indices showed considerable enrichment and geo-accumulation of toxic metals, particularly at one of the platform sites. However, cumulative indices did not display significant pollution anomalies. Therefore, our findings suggest that oil extraction activities may increase the availability of toxic metals in nearby sediments, potentially impacting marine ecosystems.