Wind-assisted ship propulsion (WASP) technology seems to be a promising solution toward accelerating the shipping industry’s decarbonization efforts as it uses wind to replace part of the propulsive power generated from fossil fuels. This article discusses the status quo of the WASP technological growth within the maritime transport sector by means of a secondary data review analysis, presents the potential fuel-saving implications, and identifies key factors that shape the operational efficiency of the technology. The analysis reveals three key considerations. Firstly, despite the existing limited number of WASP installations, there is a promising trend of diffusion of the technology within the industry. Secondly, companies can achieve fuel savings, which vary depending on the technology installed. Thirdly, these bunker savings are influenced by environmental, on-board, and commercial factors, which presents both opportunities and challenges to decision makers.
The utilization of green energy resources for supplying energy to ships in the marine industry has received increasing attention during the last years, where different green resource combinations and control strategies have been used. This article considers a ferry ship supplied by fuel cells (FCs) and batteries as the main sources of ship's power. Based on the designers' and owners' preferences, different scenarios can be considered for managing the operation of the FCs and batteries in all-electric marine power systems. In this article, while considering different constraints of the system, six operating scenarios for the set of FCs and batteries are proposed. Impacts of each proposed scenario on the optimal daily scheduling of FCs and batteries and operation costs of the ship are calculated using a mixed-integer nonlinear programming model. Model predictive control (MPC) is also applied to consider the deviations from hourly forecast demand. Moreover, since the efficiency of FCs varies for different output powers, the impacts of applying a linear model for FCs' efficiency are compared with the proposed nonlinear model and its related deviations from the optimal operation of the ship are investigated. The proposed model is solved by GAMS software using actual system data and the simulation results are discussed. Finally, detailed real-time hardware-in-the-loop (HiL) simulation outcomes and comparative analysis are presented to confirm the adaptation capability of the proposed strategy.
The purpose of this paper is to provide an overview and discussion of potential Market Based Measures (MBMs) under the Initial IMO Strategy for the reduction of greenhouse gas (GHG) emissions from ships. In this context, some related developments are also seen as directly relevant, mainly in the context of the possible inclusion of shipping into the EU Emissions Trading System (ETS). A comparative evaluation of maritime MBMs is made using the following criteria: GHG reduction effectiveness, compatibility with existing legal framework, potential implementation timeline, potential impacts on States, administrative burden, practical feasibility, avoidance of split incentives between ship-owner and charterer, and commercial impacts. The paper breaks down potential MBMs into the following classes: Bunker levy/carbon levy MBMs, ETS (global and/or EU ETS) MBMs and other MBM proposals.
The widespread use of software-intensive cyber systems in critical infrastructures such as ships (CyberShips) has brought huge benefits, yet it has also opened new avenues for cyber attacks to potentially disrupt operations. Cyber risk assessment plays a vital role in identifying cyber threats and vulnerabilities that can be exploited to compromise cyber systems. Understanding the nature of cyber threats and their potential risks and impact is essential to improve the security and resilience of cyber systems, and to build systems that are secure by design and better prepared to detect and mitigate cyber attacks. A number of methodologies have been proposed to carry out these analyses. This paper evaluates and compares the application of three risk assessment methodologies: system theoretic process analysis (STPA-Sec), STRIDE and CORAS for identifying threats and vulnerabilities in a CyberShip system. We specifically selected these three methodologies because they identify threats not only at the component level, but also threats or hazards caused due to the interaction between components, resulting in sets of threats identified with each methodology and relevant differences. Moreover, STPA-Sec, which is a variant of the STPA, is widely used for safety and security analysis of cyber physical systems (CPS); CORAS offers a framework to perform cyber risk assessment in a top-down approach that aligns with STPA-Sec; and STRIDE (Spoofing, Tampering, Repudiation,Information disclosure, Denial of Service, Elevation of Privilege) considers threat at the component level as well as during the interaction that is similar to STPA-Sec. As a result of this analysis, this paper highlights the pros and cons of these methodologies, illustrates areas of special applicability, and suggests that their complementary use as threats identified through STRIDE can be used as an input to CORAS and STPA-Sec to make these methods more structured.
Maritime transport is the most energy-effective mode to move large amounts of goods around the world. Hauling cargo via waterway produces an enormous quantity of greenhouse gas emissions. Vessel fuel efficiency directly influences ship emissions by affecting the amount of burnt fuel. Optimizing ships operating in waves rather than in calm water conditions could decrease the fuel consumption of vessels. In particular, ship propellers are traditionally designed neglecting dynamic conditions such as time-varying wake distribution and propulsion factors, propeller speed fluctuations, ship motions, and speed loss. The effect of waves on the propeller performance can be evaluated using both a quasi-steady and a fully-unsteady approach. The former is a fast computational approximation method based on the assumption that the ratio of propeller angular frequency to wave encounter frequency is sufficiently large. The latter provides a complete representation of the propeller dynamics, but it is computationally expensive. The purpose of this paper is to compare the propeller performance in the presence of waves using the quasi-steady and the fully unsteady approach. This analysis is performed by observing the differences in unsteady propeller forces, cavitation volume, and hull pressure pulses between the two approaches. The full-scale KVLCC2 propeller is utilized for the investigation. Results show a good agreement between the quasi-steady and the fully-unsteady approach in the prediction of the temporal mean and the fluctuation amplitude of KT and KQ, the cavity volume variation, and the hull pressure pulses. Therefore, for the considered operating conditions, the quasi-steady approach can be used to compute the propeller performance in waves.
This article gives a review of techniques applied to make sea state estimation on the basis of measured responses on a ship. The general concept of the procedures is similar to that of a classical wave buoy, which exploits a linear assumption between waves and the associated motions. In the frequency domain, this assumption yields the mathematical relation between the measured motion spectra and the directional wave spectrum. The analogy between a buoy and a ship is clear, and the author has worked on this wave buoy analogy for about fifteen years. In the article, available techniques for shipboard sea state estimation are addressed, but with a focus on only the wave buoy analogy. Most of the existing work is based on methods established in the frequency domain but, to counteract disadvantages of the frequency-domain procedures, newer studies are working also on procedures formulated directly in the time domain. Sample results from several studies are included, and the main findings from these are mentioned.
The climate emergency has prompted rapid and intensive research into sustainable, reliable, and affordable energy alternatives. Offshore wind has developed and exceeded all expectations over the last 2 decades and is now a central pillar of the UK and other international strategies to decarbonise energy systems. As the dependence on variable renewable energy resources increases, so does the importance of the necessity to develop energy storage and nonelectric energy vectors to ensure a resilient whole-energy system, also enabling difficult-to-decarbonise applications, e.g. heavy industry, heat, and certain areas of transport. Offshore wind and marine renewables have enormous potential that can never be completely utilised by the electricity system, and so green hydrogen has become a topic of increasing interest. Although numerous offshore and marine technologies are possible, the most appropriate combinations of power generation, materials and supporting structures, electrolysers, and support infrastructure and equipment depend on a wide range of factors, including the potential to maximise the use of local resources. This paper presents a critical review of contemporary offshore engineering tools and methodologies developed over many years for upstream oil and gas (O&G), maritime, and more recently offshore wind and renewable energy applications and examines how these along with recent developments in modelling and digitalisation might provide a platform to optimise green hydrogen offshore infrastructure. The key drivers and characteristics of future offshore green hydrogen systems are considered, and a SWOT (strength, weakness, opportunity, and threat) analysis is provided to aid the discussion of the challenges and opportunities for the offshore green hydrogen production sector.
The approach documented in this paper employs system identification (SI), or data-based modelling, techniques as an alternative to model determination from first principles for modelling a vented oscillating water column wave energy converter, using real wave tank data gathered at Danmarks Tekniske Universitet. In SI, the parameters of the model are obtained from the experimental input/output data by minimizing a cost function, related to model fidelity. The main advantage of SI is its simplicity, as well as its potential validity range, where the dynamic model is valid over the full range for which the identification data was recorded. Furthermore, SI models are somewhat flexible, since they can be solely based on data (black-box models), or else can incorporate some physics-based information (grey-box models). However, a suitable excitation signal is of primary importance for the parametric model to be representative over a wide range of operating conditions.
In transportation of goods in large container ships, shipping industries need to minimize the time spent at ports to load/unload containers. An optimal stowage of containers on board minimizes unnecessary unloading/reloading movements, while satisfying many operational constraints. We address the basic container stowage planning problem (CSPP). Different heuristics and formulations have been proposed for the CSPP, but finding an optimal stowage plan remains an open problem even for small-sized instances. We introduce a novel formulation that decomposes CSPPs into two sets of decision variables: the first defining how single container stacks evolve over time and the second modeling port-dependent constraints. Its linear relaxation is solved through stabilized column generation and with different heuristic and exact pricing algorithms. The lower bound achieved is then used to find an optimal stowage plan by solving a mixed-integer programming model. The proposed solution method outperforms the methods from the literature and can solve to optimality instances with up to 10 ports and 5,000 containers in a few minutes of computing time.
In transportation of goods in large container ships, shipping industries need to minimize the time spent at ports to load/unload containers. An optimal stowage of containers on board minimizes unnecessary unloading/reloading movements, while satisfying many operational constraints. We address the basic container stowage planning problem (CSPP). Different heuristics and formulations have been proposed for the CSPP, but finding an optimal stowage plan remains an open problem even for small-sized instances. We introduce a novel formulation that decomposes CSPPs into two sets of decision variables: the first defining how single container stacks evolve over time and the second modeling port-dependent constraints. Its linear relaxation is solved through stabilized column generation and with different heuristic and exact pricing algorithms. The lower bound achieved is then used to find an optimal stowage plan by solving a mixed-integer programming model. The proposed solution method outperforms the methods from the literature and can solve to optimality instances with up to 10 ports and 5,000 containers in a few minutes of computing time.