Search Results (423)

A benchmark study of 10 different numerical methods for ship motion and load assessment is presented. Pitch motions and midship vertical bending moments are compared to model test results for a containership at zero speed in head regular waves. The wave steepness is varied from 2.1% to 10.5%. The model tests show that pitch and the vertical bending moment (VBM) display nonlinear behavior even for low-steepness waves. It is demonstrated that computational fluid dynamics (CFD) methods can reproduce the ship responses with good accuracy, even in very steep waves, involving green water and parts of the ship going in and out of water. Weakly nonlinear potential-theory methods tend to overestimate the pitch motions and the sagging moments as the wave steepness increases. For the vertical bending moment in steep waves, the 3D panel methods did not give significantly better results than those obtained with the nonlinear strip theories. Full article

Agentic shopping assistants increasingly move beyond recommending products to executing actions in users’ workflows (e.g., adding items to cart, applying coupons, selecting shipping). This shift from advice to delegation raises questions about appropriate reliance, perceived control, and how interface cues support oversight when systems can act. We report a laboratory eye-tracking experiment using a chat-only e-commerce prototype in a mixed 2 × 2 design: action autonomy varied within participants (recommend-only vs. act-on-behalf, with undo/edit), and transparency cues varied between participants (minimal statements vs. preview + rationale describing what will happen and why). Three standardized shopping tasks were completed by 72 participants. Results included behavioral logs (task time, overrides), areas-of-interest (AOI)-based eye-tracking (chat attention and verification indicators), and post-task self-reports (trust, control, uneasiness, perceived transparency). Act-on-behalf autonomy reduced completion time, but it also increased unease, decreased trust and perceived control, and increased the likelihood of an override, suggesting a trade-off between efficiency and oversight. The autonomy-related penalties for trust and perceived control under act-on-behalf execution were lessened by preview + rationale transparency, which additionally enhanced perceived transparency, trust, and unease. This mechanism coincided with eye-tracking: transparency decreased verification latency during agent actions and redirected attention toward information supplied by assistants. Transparency did not reliably reduce overrides, suggesting that minimal effective transparency can streamline supervision and improve evaluations without eliminating corrective behavior. Full article

With the expansion of water transportation networks and increasing traffic intensity, maritime accidents have become frequent, posing significant threats to safety and property. This study presents a knowledge graph-driven framework for maritime accident analysis, addressing the limitations of traditional risk analysis methods in extracting and organizing unstructured accident data. First, a standardized ontology for ship collision accidents is developed, defining core concepts such as event, spatiotemporal behavior, causation, consequence, responsibility, and decision-making. Advanced natural language processing models, including a lexicon-enhanced LEBERT-BiLSTM-CRF and a K-BERT-BiLSTM-CRF incorporating ship collision knowledge triplets, are proposed for named entity recognition and relation extraction, with F1-score improvements of 6.7% and 1.2%, respectively. The constructed accident knowledge graph integrates heterogeneous data, enabling semantic organization and efficient retrieval. Leveraging graph topological features, an accident severity classification model is established, where a graph-feature-driven LSTM-RNN demonstrates robust performance, especially with imbalanced data. Comparative experiments show the superiority of this approach over conventional models such as XGBoost and random forest. Overall, this research demonstrates that knowledge graph-driven methods can significantly enhance maritime accident knowledge extraction and severity classification, providing strong information support and methodological advances for intelligent accident management and prevention. Full article

Maritime vessel detection in nearshore waters is a fundamental capability for artificial intelligence (AI)-enabled maritime transportation systems, including coastal monitoring, traffic management, and digital maritime services. Although attention mechanisms are widely incorporated into YOLO-based detectors, their relative effectiveness in marine environments under strictly controlled experimental conditions remains insufficiently clarified. This study presents a systematic comparison of Coordinate Attention (CA), Convolutional Block Attention Module (CBAM), and CLIP-based semantic fusion within a unified YOLOv8n framework for binary discrimination between ships and fishing boats in cluttered coastal imagery. All model variants were trained under identical data partitions and optimization settings to isolate architectural effects. The experimental results show that CA achieves the highest localization robustness (mAP@0.5:0.95 = 0.6127) and substantially improves precision (+7.13% over baseline), while CBAM provides the most balanced performance with the highest F1-score. In contrast, CLIP-based semantic fusion consistently degrades detection reliability, indicating limitations of global vision–language representations in small-scale maritime datasets. Precision–Recall and F1 analyses further reveal architecture-specific confidence calibration behaviors relevant to deployment-sensitive maritime applications. The findings provide practical guidance for selecting attention mechanisms in AI-driven maritime perception systems and support reliable AI integration in marine science and engineering applications. Full article

The increasing regulatory pressure on the maritime sector to decarbonize, driven in part by market-based mechanisms at the European level, is accelerating the development of onboard carbon management and energy-efficiency solutions. In this context, this study evaluates an integrated architecture that combines a CO₂ liquefaction system with organic Rankine cycles. The system captures 66% of the total CO₂ emitted by ship engines and is capable of recovering up to 2600.8 kW of energy from onboard hot and cold sources. To identify the most suitable working fluids, an extensive screening of 208 low-GWP zeotropic mixtures is conducted, assessing their thermophysical behavior and energy recovery performance. A detailed thermo-economic assessment is undertaken, including the calculation of CO₂-equivalent savings, GHG abatement cost, and payback periods. To account for fuel price variability, probabilistic modelling based on Monte Carlo sampling is applied to estimate the distribution of discounted payback outcomes. The results demonstrate that Novec 649-based zeotropic mixtures combined with the proposed architecture reduce fuel consumption and enhance onboard CO₂ management while remaining safe and economically viable across a wide range of operating scenarios. Full article

Amid growing international pressure for carbon neutrality, the maritime industry is facing mounting institutional demands for environmental innovation. Drawing on protection motivation theory, this study surveyed 499 employees from 1519 shipping service firms to examine how coercive, mimetic, and normative pressures shape green innovative work behavior. By extending protection motivation theory to a highly regulated maritime context, this study demonstrates that institutional pressures do not directly drive green innovation; instead, they enhance employees’ self-protective motivation, which subsequently fosters eco-innovation. Moreover, these relationships are stronger when firms perceive greater climate risks or receive government subsidies, indicating that contextual conditions amplify the translation of motivation into behavior. Overall, the findings reveal how macro-level institutional forces shape the sustainable transition of maritime services through employee psychology, offering governance-relevant insights for policymakers and firms seeking to promote green innovation. Full article

The promotion of shore power is a key pathway for reducing port-related emissions and achieving sustainable maritime development. This study analyzes the strategic interactions among governments, ports, and shipping companies by constructing a tripartite evolutionary game model. Specifically, it addresses three core questions: (1) how stakeholders’ initial intentions and strategic choices influence the system’s evolutionary path and eventual equilibrium; (2) how critical parameters—including subsidies for shore power infrastructure, wind turbine installation, and ship retrofitting, as well as electricity price support, carbon pricing, and policy implementation costs—shape the dynamics of the system and the equilibrium strategies of the three parties; and (3) how heterogeneity in national energy mixes, particularly the roles of wind turbine, affects decision-making behaviors across different countries. Simulation experiments are conducted to explore the effects of varying policy interventions and energy conditions on the stability of cooperative strategies. The results provide insights into the design of differentiated policy instruments that promote shore power adoption while accounting for the structural characteristics of national energy systems. This research enriches the theoretical application of evolutionary game theory to maritime sustainability and offers practical guidance for governments and stakeholders in advancing decarbonization in the port and shipping sectors. Full article

Autonomous ships require reliable and economical navigation; however, their performance is hindered when satellite-based positioning signals become unavailable. In such global navigation satellite system (GNSS)-denied conditions, a backup navigation system integrating a strapdown inertial navigation system (SINS), Doppler velocity logger (DVL), and a compass (SINS/DVL/COMPASS) can provide essential state information, but the accuracy and fault tolerance of such systems are constrained by weak observability of position/heading errors and strong dependence on DVL measurements. This study proposes a fault-tolerant optimization method based on perceptual information compensation. First, radar imagery and electronic chart data are fused at the feature level using a weighted wavelet strategy to enhance the environmental feature saliency for shoreline extraction. Second, characteristic coastline inflection points are detected and tracked using a dual-curvature and distance-constrained procedure, generating external position observations via radar–chart matching. These observations are incorporated into the SINS/DVL/COMPASS framework to improve its state observability and robustness. Simulation results show that under nominal conditions, perceptual compensation mitigates error divergence and promotes the convergence of position errors, improving the positioning stability. In terms of robustness, the proposed method delivered more stable state-error behavior than the baseline under DVL speed faults of +2 m/s, −2 m/s, and +2 m/s injected at 301–330, 701–730, and 1101–1130 s, respectively. Quantitatively, the 3σ bounds of velocity and position-related errors are reduced under fault conditions, indicating improved fault tolerance and suitability for short-term nearshore autonomous navigation during GNSS outages. Full article

Ship-to-ship (STS) transfer has become a major concern for maritime security and regulatory authorities, as it is frequently exploited for smuggling and other illicit activities. Accurate and timely identification of STS events is therefore essential for effective maritime supervision. Existing monitoring approaches, however, suffer from two inherent limitations: AIS-based surveillance is vulnerable to intentional signal shutdown or manipulation, and remote-sensing-based ship detection alone lacks digital identity information and cannot assess the legitimacy of transfer activities. To address these challenges, we propose a Multi-source Observation framework with SAR and AIS (MOSA), which integrates SAR imagery with AIS data. The framework consists of two key components: STS-YOLO, a high-precision fine-grained ship detection model, in which a dynamic adaptive feature extraction (DAFE) module and a multi-attention mechanism (MAM) are introduced to enhance feature representation and robustness in complex maritime SAR scenes, and the SAR-AIS Consistency Analysis Workflow (SACA-Workflow), designed to identify suspected abnormal STS behaviors by analyzing inconsistencies between physical and digital ship identities. Experimental results on the SDFSD-v1.5 dataset demonstrate the quantitative performance gains and improved fine-grained detection performance of STS-YOLO in terms of standard detection metrics. In addition, generalization experiments conducted on large-scene SAR imagery from the waters near Panama and Singapore, in addition to multi-satellite SAR data (Capella Space and Umbra) from the Gibraltar region, validate the cross-regional and cross-sensor robustness of the proposed framework. The effectiveness of the SACA-Workflow is evaluated qualitatively through representative case studies. In all evaluated scenarios, the SACA-Workflow effectively assists in identifying suspected abnormal STS events and revealing potential AIS inconsistency indicators. Overall, MOSA provides a robust and practical solution for multi-scenario maritime monitoring and supports reliable detection of suspected abnormal STS activities. Full article

With the rapid development of the shipping industry, the collision risk among ships in open waters has been steadily increasing, making effective multi-ship collision avoidance decision-making a critical issue for ensuring navigational safety. This paper proposes a multi-ship collision avoidance decision-making method based on the COLREGs. First, a fuzzy comprehensive evaluation method is used to construct a collision risk index model. Then, considering navigational safety, COLREG compliance, turning amplitude, and path economy, an objective function for ship collision avoidance is formulated. Next, the AVOA is improved by incorporating SA to simulate the foraging and navigation behavior of vultures. The Metropolis acceptance criterion is applied to help the algorithm escape local optima and enhance global search capabilities. Experiments conducted in the VSC simulation environment show that the proposed method significantly improves decision-making performance in multi-ship encounter scenarios compared to the standard AVOA. Full article

The transfer of aircraft on deck relies on the traction system, which is easily affected by the offshore environment. Violent ship motion in the complex marine environment poses a great threat to the aircraft traction process, such as the tire sideslip, off-ground phenomena, the aircraft overturning, traction rod fatigue fracture, and so on. Therefore, it has merits in both academia and engineering practice to study the dynamic behaviors of the ship-borne aircraft towing system under high sea states. Considering the intricate coupling motions of the hull roll, pitch, and heave, the dynamic analysis of the towing system with rod are carried out based on the multibody dynamics theory. The influence of the sea state level and the traction speed on the dynamic characteristics of the towing system is investigated. The results indicate that noticeable tire sideslip occurs under sea state 3, with the peak lateral tire force increasing by approximately 250% compared with sea state 2. Under sea state 4, intermittent off-ground phenomena are observed, accompanied by a further increase of about 22% in lateral tire force. These findings provide quantitative insights into the dynamic characteristics and operational limits of rod-traction systems for ship-borne aircraft in rough marine environments. Full article

Microplastics (MPs) and Antifouling Paint Particles (APPs) are pervasive anthropogenic pollutants that threaten global ecosystems, with distinct yet overlapping environmental behaviors and toxic impacts. MPs disperse widely in aquatic systems via runoff and wastewater; their toxicity stems from physical, chemical, and synergistic effects. APPs are concentrated in coastal zones, estuaries, and shipyard areas, and are acutely toxic due to their high metal and biocide content. This study systematically characterized the composition, concentration, and size distribution of common MPs and APPs in ship-hull derusting wastewater produced by ultra-high-pressure water jetting, using pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS) coupled with particle size analysis. The wastewater exhibited a total suspended solids (TSS) concentration of 20.04 g·L⁻¹, within which six types of MPs were identified at 3.29 mg·L⁻¹ in total and APPs were quantified at 330.25 mg·L⁻¹, representing 1.65% of TSS. The residual fraction primarily consisted of algae, biological debris, and inorganic particles. Particle size distribution ranged from 3.55 to 111.47 μm, with a median size (D50) of 31 μm, while APPs were mainly 5–100 μm, with 81.4% < 50 μm. Extrapolation to the annual treated ship-hull surface area in 2024 indicated the generation of ~57,440 m³ wastewater containing ~0.2 tons of MPs and ~19 tons of APPs. These findings highlight the magnitude of pollutant release from ship maintenance activities and underscore the urgent need for targeted treatment technologies and regulatory policies to mitigate microplastic pollution in marine environments. Full article

The Estimated Time of Arrival (ETA) of vessels is a vital operational indicator for voyage planning, fleet deployment, and resource allocation. However, most existing studies focus on short-distance liner services with fixed routes, while ETA prediction for long-distance tramp bulk carriers remains insufficiently accurate, often resulting in operational inefficiencies and charter party disputes. To fill this gap, this study proposes a data-driven stacking ensemble learning framework that integrates Light Gradient-Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), and Random Forest (RF) as base learners, combined with a Linear Regression meta-learner. This framework is specifically tailored to the unique complexities of tramp shipping, advancing beyond traditional single-model approaches by incorporating systematic feature engineering and model fusion. The study also introduces the construction of a comprehensive multi-dimensional AIS feature system, incorporating baseline, temporal, speed-related, course-related, static, and historical behavioral features, thereby enabling more nuanced and accurate ETA prediction. Using AIS trajectory data from bulk carrier voyages between Weipa (Australia) and Qingdao (China) in 2023, the framework leverages multi-feature fusion to enhance predictive performance. The results demonstrate that the stacking model achieves the highest accuracy, reducing the Mean Absolute Error (MAE) to 3.30 h—a 74.7% improvement over the historical averaging benchmark and an 11.3% reduction compared with the best individual model, XGBoost. Extensive performance evaluation and interpretability analysis confirm that the stacking ensemble provides stability and robustness. Feature importance analysis reveals that vessel speed, course stability, and remaining distance are the primary drivers of ETA prediction. Additionally, meta-learner weighting analysis shows that LightGBM offers a stable baseline, while systematic deviations in XGBoost predictions act as effective error-correction signals, highlighting the complementary strengths captured by the ensemble. The findings provide operational insights for maritime logistics and port management, offering significant benefits for port scheduling and maritime logistics management. Full article

This study investigates the sailing performance and maneuverability of a fully wind-powered ship equipped with two rigid wing sails and a rudder, using Computational Fluid Dynamics (CFD). Unlike some conventional approaches that separately analyze above-water and underwater forces, this research employs a comprehensive CFD model to predict ship motion and performance under various wind directions and sail angles, from a stationary state to steady sailing. The accuracy of the CFD method is confirmed through comparison with experimental drift test data. Although the simulated drift data showed some discrepancies from the observed data due to the difficulty of accurately modeling the wind field in the simulation, the results indicate that the CFD method can effectively reproduce the ship motions observed in the experiments. Simulations reveal that the previously proposed L-shaped and T-shaped sail arrangements, which were designed to maximize thrust without considering maneuvering effects, remain effective even when ship motion is included. However, the results also show that conventional sail arrangements can achieve higher steady-state speeds due to reduced leeway-related resistance, while the L-shaped and T-shaped arrangements yield distinct steady-state leeway (drift) characteristics under heading control. These findings suggest that dynamically adjusting sail arrangements according to operational requirements may help manage the ship’s trajectory (lateral offset) and mitigate maneuvering difficulties, contributing to the practical application of fully wind-powered ships. The study provides quantitative insights into the relationship between sail arrangement, acceleration, and leeway/drift behavior, supporting the design of next-generation wind-powered ships. Full article

A quantitative understanding of methane leakage has become essential for safety design as eco-friendly fuel systems expand in modern ship applications. To address this need, controlled methane-release experiments were conducted in a large indoor chamber (30 × 16 × 20 m) to evaluate how sensor-network resolution (1 m vs. 0.5 m spacing) influences dispersion measurement and 5% Lower Explosive Limit (LEL)-based risk assessment. Initial tests with a 1 m grid showed that most sensors detected only low concentrations except for near the release nozzle, demonstrating that coarse spatial resolution cannot capture the primary dispersion pathway or transient peaks. This limitation motivated the use of a 0.5 m high-density sensor network, which enabled clear identification of the dispersion centerline, concentration-gradient development, early detection behavior, and the evolution of diluted regions, particularly under buoyancy-driven plume rise. Experimental results were compared with CFD simulations using the RNG k–ε and k–ω GEKO turbulence models. Strong agreement was obtained in peak concentration, concentration-rise rates during the accumulation phase, and LEL-based dispersion distances. These findings confirm the suitability of the selected turbulence models for predicting methane behavior in large enclosed spaces and highlight the sensitivity of model–experiment agreement to measurement resolution. The results provide an experimentally grounded reference for sensor layout design and verification of gas-detection strategies in ship compartments, fuel-gas preparation rooms, and modular supply units. Overall, the study establishes a methodological framework that integrates high-resolution experiments with CFD modeling to support safer design and operation of methane-fueled vessels. Full article