Search Results (7,895)

To effectively leverage the role of green shipping corridors (GSCs) in promoting greenhouse gas emissions reduction in international shipping, this paper firstly examined the current status and challenges faced by GSCs with the aim of providing valuable solutions for future development. Then, a conceptual framework of multi-stakeholder partnerships (MSPs) for the international maritime industry that enables the construction and implementation of GSCs was proposed. Additionally, the inherent correlation mechanism between the “feasibility wall” of GSCs and the core elements as well as key principles in the MSP framework was also explored. The findings indicate that the GSC initiatives at the global, regional and local levels are advancing rapidly, yet very few have been truly implemented and effectively operationalized, with the fundamental cause lying in the lack of effective theoretical guidance and research support; based on the theory, mechanism and framework of MSPs, the existing GSCs are found to still have considerable deficiencies in partnership building, roles and responsibilities, governance structure, funding and resource support, as well as monitoring and accountability. Concept validation through case studies demonstrates that the conceptual framework proposed in this paper can serve as a practical diagnostic tool for GSC initiatives, which can help to identify the specific stage they are failing at and apply targeted principles to fix it. This paper is expected to contribute to a more effective advancement of the development of GSCs, thereby actively facilitating the achievement of net-zero emission targets for international shipping. Full article

Decarbonizing ocean-going shipping requires decision-grade environmental evidence for propulsion transitions, yet conventional LCA relies on static inventories that inadequately represent dynamic operations and route-dependent renewable generation. This study evaluates well-to-wake (WtW) Global Warming Potential (GWP) for two large container ships operated by a Korean company under four scenarios: conventional diesel main engine, diesel–electric with onboard generator, full battery-electric supplied by shore electricity from the Republic of Korea grid, and battery-electric with a route-resolved solar PV system. A Live-LCA (LLCA) framework couples LCI data with MATLAB/Simulink power and propulsion modeling driven by actual operating profiles and route environmental conditions to generate operational inventories for impact calculation. Diesel–electric operation increases annual WtW GWP by over 26% for both ships versus the baseline of a conventional diesel main engine, whereas shore-electric battery operation is able to reduce WtW GWP by around 40% versus diesel–electric. With limited PV installation, additional reductions are marginal. Depending on electricity profile, it can increase battery-electric GHG emissions by approximately 27%, highlighting sensitivity to electricity evolution. Overall, electric propulsion delivers climate benefits only when paired with low-carbon electricity, and LLCA enables operationally and route-grounded LCA for large container ships. Full article

This study investigates uncertainty quantification for field-level ship hull surface pressure predictions using a U-Net-based data-driven model. A speed-conditioned U-Net is trained on a large CFD dataset covering multiple ship types and velocity conditions to predict pressure distributions on hull surfaces. The model outputs the mean pressure and log-variance at each grid location using a negative log-likelihood loss, allowing aleatoric uncertainty to be estimated, while epistemic uncertainty is quantified by a deep ensemble of independently trained models. The reliability and calibration of the predicted confidence intervals are evaluated at the field level. The results show that calibration stabilizes as ensemble size increases, and coverage slightly exceeds nominal confidence levels. Uncertainty decomposition indicates that aleatoric uncertainty dominates and is insensitive to ensemble size, while epistemic uncertainty primarily affects calibration. Elevated uncertainty is consistently observed near free-surface regions around the bow and stern, reflecting increased prediction difficulty. These findings demonstrate the effectiveness of deep-ensemble-based uncertainty quantification for CFD-driven pressure field prediction models. Full article

The loss of the MV Estonia has been investigated by various organizations since the accident in September 1994. The root cause of the accident has been assumed to be known, and the consequent sinking process is well established. However, in September 2020, a new video recording by an underwater ROV was published, showing a new, previously unknown, penetrating damage on the starboard side of the MV Estonia wreck lying on the seabed. Based on this new evidence, the Estonian Safety Investigation Bureau (ESIB) initiated a preliminary assessment of the new information on the MV Estonia accident. Whether the New Side Damage (NSD) on the starboard side was already present while the MV Estonia was afloat on the surface, or whether it resulted from the collision of the sinking vessel with the seabed somewhat later, is an important issue needing clarification: In the first case, the validity of the conclusions on the root cause of the accident presented in the previous studies could prove premature. One of the goals of the present investigation by the Hamburg Ship Model Basin (HSVA) is to shed light on this question: The results of the numerical simulations of the sinking process carried out for various damage configurations in seaway using not only single simulations, but also a statistical approach are presented. Full article

The operational reliability of gears in ship propulsion systems is an important factor affecting safety, efficiency, and cost-effectiveness in ship operation. Gear failures may result in loss of propulsion, increased maintenance costs, and risks to crew safety. This paper presents an integrated methodological framework for assessing gear reliability in ship propulsion systems by integrating qualitative causal analysis, quantitative reliability growth modelling, and system dynamics simulation. The analysis is based on empirical data collected from the AMOS computerised maintenance management system for ship propulsion gear over the course of 20,000 operating hours. The Ishikawa diagram is applied as a qualitative tool to structure potential failure causes related to human, technical, material, procedural, measurement, and environmental factors. Using a system dynamics approach, a qualitative conceptual model of cause-and-effect relationships and a quantitative simulation model were developed, where the mathematical model of Goel–Okumoto reliability growth was applied to quantitatively describe the process of detecting and eliminating failures, with an exponential decrease in failure intensity over time and a high level of agreement with empirical data (R² = 0.9962), corresponding to the part of the bathtub curve related to the running-in of ship systems. The system dynamics simulation implemented in the POWERSIM environment integrates the analytically estimated model parameters and provides a dynamic representation of the relationships between failure intensity, cumulative failures, reliability, and the mean time between failures. The scientific contribution of this work lies in the structured integration of established methods into a single analytical framework, enabling coherent interpretation of empirical reliability data under real operating conditions. The results provide a methodological basis for developing predictive maintenance tools, optimising maintenance strategies, and improving the safety of ship propulsion systems. Full article

Marine target classification is a key technology for unmanned surface vehicles (USVs) to perform ocean surveillance. Traditional maritime target classification methods require improvements in both accuracy and processing speed when handling classification tasks. In this paper, a distributed maritime target classification (DMTC) method based on broad learning and MobilityFirst is proposed. Firstly, a multi-model collaborative classification and fusion framework is proposed to achieve feature consistency fusion. Secondly, to enhance the security and privacy of communication in autonomous surface vehicles, the MobilityFirst approach is employed to improve information complementarity among multiple models within the distributed framework. Finally, the broad learning system, as the model’s classification layer, reduces the training complexity. Extensive experimental results demonstrate that this proposed approach surpasses single-model and distributed methods in accuracy, F1 score, and the area under the precision–recall curve (AUPR). This approach offers a clear advantage in multi-ship classification tasks while simultaneously enhancing the model’s generalization capability. Full article

Burnout represents a critical occupational health issue within the maritime sector, where demanding work schedules, prolonged periods at sea, and safety-critical responsibilities expose seafarers to significant psychological strain. This study investigates how burnout influences safety behaviors among maritime workers, adopting a multilevel framework that incorporates work engagement, quality of life, and work–family conflict as key factors shaping this relationship. Data was collected through a structured questionnaire administered to 216 seafarers distributed across 36 commercial vessels, representing a diverse range of onboard roles and operational contexts. The multilevel design allows for simultaneous examination of individual-level experiences and ship-level dynamics, offering a more nuanced understanding of how psychosocial risks translate into safety-relevant outcomes in maritime environments. Data were analyzed using multilevel structural equation modeling (MSEM), including multilevel confirmatory factor analysis (ML-CFA) and multilevel path analysis, implemented in Mplus version 8.10. The findings reveal that burnout undermines seafarers’ safe behaviors through diminished work engagement and a worsened quality of life. Furthermore, high levels of interference between work and family life amplify the negative effect of burnout on safe behaviors. This study contributes to the limited empirical literature on maritime behavioral health and provides implications for strengthening safety culture and crew well-being in the global shipping industry. Full article

Coastal ecosystems are increasingly threatened by anthropogenic activities associated with tourism development and maritime traffic. This study evaluates the environmental quality of a coastal sector using an integrated approach combining sediment characteristics, heavy metal concentrations, and benthic foraminiferal assemblages. Nineteen surface sediments were collected and analyzed for trace metals using ICP-MS, while benthic foraminiferal assemblages were quantified, and ecological indices were calculated. Results reveal elevated concentrations of trace metals at coastal stations, closely associated with high TOM and fine-grained sediments, indicating significant anthropogenic inputs. These stations are characterized by low species richness, reduced Shannon diversity, high dominance, low living foraminiferal percentages, high malformed individuals, and markedly low FoRAM values, reflecting stressed environmental conditions. Opportunistic taxa such as Ammonia tepida dominate impacted sites, whereas sensitive carbonate-producing taxa (Quinqueloculina lamarckiana, Coscinospira hemprichii, Elphidium striatopunctatum, Elphidium crispum) prevail at less disturbed stations. Multivariate analyses clearly separate polluted coastal stations from relatively unimpacted offshore sites. The combined geochemical and biological evidence demonstrates that tourism-related activities and ship effluents exert a strong negative influence on benthic ecosystems. Benthic foraminifera, together with heavy metals, provide an effective and sensitive tool for assessing anthropogenic impacts and coral reef health for sustainable coastal management of Safaga Bay. Full article

This paper investigates ship–pier collision risk caused by yaw deviation in inland bridge waterways. The proposed framework is conceived for fixed auxiliary thruster installation in bridge areas, rather than retrofitting shipboard propulsion systems. A proactive intervention scheme is developed based on state estimation and short-horizon prediction. A Kalman filter is used for state fusion and short-horizon motion prediction. Yaw events are detected via a threshold rule with consecutive-decision logic. An extended state observer (ESO) is adopted to estimate lumped disturbances and model uncertainties. A fuzzy self-tuning PID law is then applied to generate thruster commands for closed-loop corrective control. Numerical simulations suggest that, relative to rudder-only recovery, thruster-assisted intervention yields improved restoration behavior, reduced lateral deviation accumulation, and increased minimum clearance to bridge piers under the tested conditions. Additional tests with cross-current disturbances indicate that the risk-triggered scheme with ESO-based compensation can maintain stable recovery and a higher safety margin. The proposed approach provides an engineering-oriented pathway to extend bridge-area risk management from warning-level assessment to executable control intervention. Full article

Unmanned surface vehicles (USVs) increasingly rely on vision-based perception for safe navigation and maritime surveillance, while onboard computing is constrained by strict size, weight, and power (SWaP) budgets. Although deep convolutional neural networks (CNNs) offer strong recognition performance, their computational and memory requirements pose significant challenges for deployment on low-cost embedded platforms. This paper presents a hardware–software co-design architecture and deployment study for CNN acceleration on a heterogeneous ARM–FPGA system, targeting energy-efficient near-sensor processing for embedded maritime applications. The proposed approach exploits a fully streaming hardware architecture in the FPGA fabric, based on line-buffered convolutions and AXI-Stream dataflow, while the ARM processing system is responsible for lightweight configuration, scheduling, and data movement. The architecture was evaluated using representative CNN models trained on a maritime ship dataset. Our experimental results on a Zynq-7020 system-on-chip demonstrate that the proposed co-design strategy achieves a balanced trade-off between throughput, resource utilisation, and power consumption under tight embedded constraints, highlighting its suitability as a practical building block for onboard perception in USVs. Full article

Accurately predicting the remaining useful life (RUL) of ship shafting is crucial for ensuring navigation safety and optimizing operation and maintenance. Traditional Bayesian Network (BN) methods are usually based on the assumption of symmetric distributions. They struggle to effectively characterize common statistical properties such as asymmetry and heavy tails during the shafting degradation process, leading to biases in prediction results. To address this issue, this study proposes an Asymmetric Distribution Bayesian Network (ADBN) method. The method consists of three key components. Firstly, each node selects the optimal asymmetric distribution form based on the Bayesian Information Criterion (BIC) to better fit data characteristics. Secondly, a Generalized Linear Model (GLM) is used to associate distribution parameters (e.g., location, scale, shape) with parent node states, enabling the conditional distribution to adaptively evolve with the system degradation process. Finally, to tackle the complex inference problem under asymmetric distributions, an approximate algorithm based on stochastic gradient variational inference is designed to ensure prediction timeliness. Experimental results show that the ADBN method outperforms traditional Gaussian networks in terms of Mean Absolute Error in the early, middle, and late stages of RUL prediction, and can provide more accurate prediction intervals. This research offers a probabilistic approach that better aligns with actual statistical properties for modeling ship shafting degradation. Full article

For human beings, fine-grained object recognition is a progressive process that proceeds from global outlines to local details. They can determine how to further focus on the distinctive regions based on the overall context, followed by recognition. To enhance the algorithm’s capability to capture critical features, a multi-stage recognition framework, integrated with human-attended key regions for fine-grained ship recognition, is proposed in this manuscript. First, a set of distinctive templates is constructed following human identification logic. On this basis, a supervised attention method, Key Regions Guided Yolo11 (KRGY), with part-to-whole regulation is proposed to help the model focus on critical components, leading to better recognition and location performance. Furthermore, a multi-head joint recognition classification module is proposed, with key regions of ship cropped with the distinctive templates. With the hypothesis and verification framework Key Regions Guided Yolo11-Multi Head Classifier (KRGY-MHC), the accuracy of ship recognition is significantly improved based on a challenging datasets with high inter-class similarity DCL-11. Full article

The marine centrifugal pump is one of the most energy-intensive pieces of equipment in ship auxiliary machinery, and the efficient design of its hydraulic components can effectively reduce the total energy consumption of the ship system. Aiming at the complex three-dimensional twisted blade profile structure of the marine centrifugal pump, this paper optimized the clonal selection algorithm and constructed an automatic hydraulic optimization design method for the high-efficiency centrifugal pump impeller. Considering the multi-condition operation characteristics of the marine centrifugal pump, a performance test platform for the marine centrifugal pump was built, and the actual operating conditions of the model pump were tested to obtain its performance characteristics under operating conditions. The numerical simulation method was employed to capture and analyze the internal flow field and flow characteristics of the model pump. Addressing the design challenges of the marine centrifugal pump impeller, which involve multiple parameters with significant interactions, a traditional clonal selection algorithm was enhanced using a Slime Mold Algorithm, and a hybrid Clonal Selection Algorithm integrated with Slime Mold and Tangent Flight mechanisms was established. Based on the MATLAB and ANSYS platforms, an automated hydraulic optimization design framework for the centrifugal pump impeller was established. Using the optimized clonal selection algorithm, with the operational efficiency of the model pump as the optimization objective and controlling ten key geometric parameters of the blade profile through Bézier curves, the blade profile optimization design was achieved. The pump hydraulic efficiency under the rated flow condition increased by 7%. The unsteady internal flow efficiency of the optimized marine centrifugal pump was significantly improved. The blade optimization alleviated flow separation phenomena on the tangential surface of the impeller and in partial regions of the volute, reduced the flow loss area, and significantly decreased overall flow losses. Full article

Typhoon avoidance is critical for ship maneuvering safety under extreme meteo-ocean conditions. This study proposes a data-driven framework that converts AIS trajectories into interpretable course deviation and speed change responses for navigational decision support. After AIS cleaning, temporal resampling, and matching with gridded wind, wave, and current fields, rule-based sliding-window and regression procedures, informed by experienced captains and company staff, automatically generate proxy labels for deviation and speed reduction. Samples are stratified by vessel size to reflect differences in inertia and maneuverability, and XGBoost classifiers are trained with simple resampling to mitigate class imbalance. The framework is demonstrated on a single-event case study of Typhoon Yagi in the South China Sea, covering 8609 vessels and reconstructed sailing fragments. On the test set, the deviation model achieves 89.8% accuracy and high recall for deviation cases, while the speed change model reaches 82% balanced accuracy under the proxy-label setting. Results suggest a scale-dependent response: smaller vessels exhibit more frequent course deviation, whereas larger vessels more often reduce speed under severe wind-wave loading. The framework offers a proof-of-concept approach to derive behavior-based indicators from AIS and environmental data and may support situational assessment under adverse weather. Full article

Drones have long been explored by commercial and military users for supply. While several systems offering small payloads in drone delivery have seen operational use, large-scale supply drones have yet to be adopted. A range of setbacks cause this, including technological and operational challenges that hinder their adoption. Here, we evaluate these challenges from a conceptual modelling perspective and forecast their applicability once these barriers are overcome. This study uses technology trend modelling and bibliometric activity mapping methodologies to predict the applicability of specific technologies that are currently identified as operational challenges. Specifically for supply drones, we model trends in technological improvements of battery technology and aircraft control, and project its focus on landing zone autonomy and powertrain. The prediction also focuses on the current state of hybrid power and higher levels of automation required for landing zone operations. These models are validated through several published case studies of small delivery drones and then applied to assess the feasibility and constraints of larger supply drones. A case study involving the conceptual design of a supply drone large enough to move a shipping container is presented to illustrate the critical technologies required to transition large supply drones from concept to operational reality. Key technologies required for large-scale supply drones have yet to build up a critical mass of research activity, particularly on landing zone autonomy and powertrain. Moreover, additional constraints beyond technological and operational challenges could include limitations in autonomy, certification hurdles, regulatory complexity, and the need for greater social trust and acceptance. Full article