Estimating incident waves onboard is crucial for ensuring safe and efficient operation of a marine vessel. This paper is concerned with an in-situ response-based wave estimation method, in which incoming waves are inversely estimated via measured wave-induced responses and corresponding transfer functions (TRFs); a method widely referred to as the 'Wave Buoy Analogy (WBA)'. Specifically, a new phase-resolved WBA technique is studied. In a transfer function-dependent technique, the accuracy of estimated waves via the WBA is in principle contingent upon the accuracy of TRFs. However, it can be challenging to provide precise TRFs onboard, as the vessel's weight distribution and the operational conditions may vary from voyage to voyage. To address this issue, this paper proposes a novel approach to simultaneously attain estimations of response TRFs and incoming wave profiles based on measured wave-induced responses. Specifically, parametrized TRFs (P-TRFs) are introduced using a well-trained Artificial Neural Network (ANN), and the parameters characterizing the PTRFs are identified through optimization, based on pseudo responses under reconstructed phase-resolved incident waves. A linear strip theory, the so-called New Strip Method (NSM), is utilized to train the ANN. Bayesian Optimization (BO) is employed to identify the parameters in the P-TRF. A numerical investigation using synthetic measurements generated via known TRFs is first made. Following this, the proposed approach is validated against experimental campaigns using scaled models of a bulk carrier and container ship, respectively, in longcrested irregular waves. It is unveiled that the estimation results of TRFs and incoming waves demonstrate a high degree of correlation with the experimental measurements. The computational cost of the presented approach is low, thus making the approach practically feasible for real-time applications.
Decarbonization of shipping is one of the biggest challenges facing the maritime industry. This study assesses the impact of regulating greenhouse gas (GHG) intensity in marine fuels called GHG Fuel Standard (GFS) and its flexible compliance mechanisms - pooling, which allows ships to share emission reductions. A computational model was developed to analyze future fleet transformations, fuel consumption, and costs under GFS scenarios with and without pooling. A scenario study using the model indicates that pooling reduces short-term costs by enabling flexible fuel usage. However, pooling delays the phase-out of heavy fuel oil (HFO)-fueled ships, leading to increased costs in 2050 due to reliance on expensive biofuels. The impact of pooling may vary due to uncertainties in transportation demand and fuel prices. By using Monte Carlo simulations considering the uncertainties, the findings were validated, and it was also found that pooling can reduce cost variability for short-term compliance.
This paper aims to identify the gaps on the path to achieving sustainable development of floating offshore wind in Japan. Japan has a strong desire for floating offshore wind development, motivated by energy security, climate change, and industry promotion. The key challenges are described with an emphasis on the unique environmental conditions of Japan, such as earthquakes and tropical cyclones. In addition, the absence of oil and gas development in Japan has led to social challenges, such as a lack of supply chain, infrastructure, and human resources in offshore wind. A review of state-of-the-art technologies is provided in each technology domain for four research domains: site selection and characterization, technology and engineering, project execution and operation, and industry and economic enabling. The gaps identified in the paper suggest the need for specific research topics, such as the assessment of unique environmental conditions; the design of robust and cost-effective structures considering fabrication, transportation, installation, and operation; and the creation of a data sharing strategy for efficient and rapid learning. In addition, many challenges show technology gaps across domains, indicating the need for interdisciplinary research collaboration and system integration of complex systems with digital engineering approaches. Finally, the need for the development of an industry roadmap to address these challenges is discussed.
The Rio Grande Rise (RGR) is the largest oceanic plateau in the South Atlantic and represents a key natural laboratory for understanding oceanic plateau formation, deep-sea circulation, ecosystem functioning, and ferromanganese crust development. This study presents a critical synthesis of current scientific knowledge on the RGR, integrating geological, geophysical, oceanographic, biological, and geochemical evidence published over the last two decades. Geophysical data reveal a complex tectono-magmatic evolution involving Late Cretaceous plume-related volcanism, crustal thickening, rifting, and subsequent subsidence. The structural framework of the plateau is dominated by the Cruzeiro do Sul Rift, which plays a central role in controlling sedimentation, magmatism, and seawater circulation. Oceanographic studies demonstrate that the interaction between the southern branch of the South Equatorial Current and the complex topography of the RGR generates intense internal tides and bottom currents, strongly influencing sediment transport and benthic habitats. Biological investigations indicate that the RGR hosts diverse deep-sea communities, including sponge grounds, cold-water corals, and associated fauna, whose distribution is tightly linked to geomorphology and hydrodynamics. Ferromanganese crusts occurring on the plateau preserve valuable geochemical records of oceanographic and redox conditions, although their spatial distribution, thickness, and metal budgets remain incompletely constrained. Despite major advances, significant knowledge gaps persist regarding crustal structure, sedimentary evolution, ecosystem functioning, and mineral formation processes. This review highlights these uncertainties and outlines research priorities necessary to improve understanding of oceanic plateaus and deep-sea systems in the South Atlantic.
Open-access datasets for marine-engine predictive maintenance remain scarce, particularly those from controlled fault experiments with documented operating conditions, subsystem-level interventions and system-level measurements. This work presents the Marine Engine Fault Dataset, an openly available dataset from a turbocharged, intercooled three-cylinder marine diesel engine operated on a testbed under both reference and fault-scenario conditions. The experimental campaign combined a reference-performance program across the 30-90
