Shipboard automation in electrical power management has improved efficiency and reliability, but it has also fostered dependency that may erode essential manual competencies. Blackouts arising from failures in automatic standby generator start-up, synchronisation or load-sharing can rapidly result in propulsion and steering loss, heightening consequent collision and grounding risk, particularly in confined or congested waters. Ensuring that ship engineers retain manual synchronisation proficiency is therefore fundamental to navigation safety and to meeting IMO STCW competency requirements. This study explores and examines how maritime simulation-based training supports Situational Awareness (SA) and manual generator synchronisation under automation-failure conditions. Using a full-mission ship engine-room simulator, 31 maritime cadets and students performed emergency manual synchronisation following deliberate deactivation of the automatic system. A dual-layer SA assessment is combined by adopting the Situation Awareness Global Assessment Technique (SAGAT) with the Situation Awareness Rating Technique (SART), complemented by objective simulator logs and instructor performance ratings. The findings demonstrate the value of dual-SA measurement in Maritime Education and Training. Incorporating instruments such as SAGAT and SART into STCW-aligned simulator curricula can reveal latent SA deficits that traditional checklist-based assessment may overlook and strengthen cognitive readiness for automation-failure events that directly affect navigation-safety for bridge and engine teams.
The water exit of an axisymmetric body represents a typical unsteady multiphase flow process, in which the hydrodynamic forces become highly complex due to dynamic fluid-structure interactions. In this study, the applicability of the Volume of Fluid (VOF) multiphase model, turbulence model, cavitation model, and overset mesh technique was validated through a series of numerical simulations. Based on this framework, the water-exit behaviour of an axisymmetric body was investigated in various hydrodynamic environments. The effects of initial inclination angle, flow direction, and current velocity in a current environment, as well as wave steepness and wave celerity in a wave environment, were systematically examined in relation to the trajectory evolution and hydrodynamic force characteristics during the exit process. The results indicate that a smaller initial inclination angle (i.e. closer to vertical) and lower current velocity reduce the influence on the exit trajectory and force coefficients. In contrast, greater wave steepness and lower wave celerity significantly intensify the impact on both the water-exit trajectory and the associated hydrodynamic loads.
This study presents a comparative computational fluid dynamics (CFD) investigation of the Joubert BB2 submarine under straight-ahead (0 Deg.), rising (+10 Pitch), and diving (-10 Pitch) conditions, focusing on how turbulence modelling influences resistance components and stern-wake characteristics. Simulations were performed using a finite-volume solver in OpenFOAM with the k - omega SST framework, employing both a conventional Reynolds-averaged Navier-Stokes (RANS) closure and a partially averaged Navier-Stokes (PANS) formulation with locally varying resolution control. The numerical methodology was first assessed against benchmark information provided by MARIN using the total resistance level and the propeller-plane wake characteristics, providing a consistent baseline for model-to-model comparisons. Across all attitudes, the predicted total resistance coefficients from RANS and PANS were generally comparable; however, resistance decomposition revealed systematic differences, with PANS tending to yield slightly higher pressure resistance and lower viscous resistance than RANS, particularly under inclined conditions. Model-dependent discrepancies were also observed in the stern wake and propeller-plane inflow: compared with RANS, PANS produced a more pronounced and spatially extended axial-velocity deficit and maintained broader regions of elevated vorticity, indicating different preservation of rotational wake features and coherent vortical structures. Overall, the results show that, even when integrated resistance is similar, the turbulence modelling approach can lead to meaningful differences in component-level resistance and wake topology relevant to downstream propulsor inflow assessment.
Machine learning models are increasingly used to predict ship fuel consumption from high-frequency operational data. However, the strong temporal autocorrelation of shipboard sensor measurements raises concerns regarding the validity of commonly adopted evaluation practices. Many existing studies rely on random data partitioning and cross-validation schemes that can introduce information leakage, resulting in overly optimistic performance estimates. This study examined the impact of the validation strategy on fuel consumption prediction using one-minute operational data collected from a bulk carrier over approximately 16 months. Gradient-boosted decision tree models (XGBoost and CatBoost) and deep learning architectures were evaluated using three validation schemes: random train-test splitting, blocked chronological hold-out, and rolling-window temporal evaluation. The results demonstrate that random splitting substantially inflates the predictive performance, with coefficients of determination exceeding 0.99, whereas temporally consistent validation reveals significantly reduced accuracy and performance degradation across unseen operating periods. Under realistic temporal testing, gradient-boosted models exhibit greater robustness than deep learning models, which exhibit higher sensitivity to distributional shifts. These findings highlight the critical importance of temporally aware validation for obtaining credible generalisation estimates in ship fuel consumption modelling.
Decarbonisation in the maritime sector requires not only technological innovation but also economically viable compliance strategies under increasingly stringent regulatory frameworks. This study evaluates the technical and economic implications of e-methanol-based fuel conversion combined with pooling mechanisms under FuelEU Maritime and the EU Emissions Trading System (EU ETS). A five-vessel containership fleet is analysed within a scenario-based framework, where one vessel is converted to e-methanol while the others continue operating on conventional fuels. The results indicate that the converted vessel may generate an average annual compliance surplus of approximately 1,800 tonnes of CO2eq, enabling fleet-level compliance and producing total incentive revenues of USD 9.34 million. The conversion investment of approximately & euro;5.77 million is estimated to be recovered within 14 years. Regulatory costs are projected to increase from approximately 25% to 65% of total fleet costs, reflecting a shift toward compliance-driven cost structures. The findings suggest that selective fuel conversion combined with regulatory flexibility may offer a cost-effective transitional pathway. However, the results are scenario-dependent and sensitive to key uncertainties, including fuel prices, carbon pricing, and policy developments.
The future of maritime traffic will enter a transitional period characterised by a blend of manned ships and intelligent ships with varying degrees of autonomy. Existing research on maritime navigation situation assessment primarily focuses on traditional ships or a single-ship perspective, lacking a regulatory-oriented comprehensive assessment framework for high-density ship areas in mixed maritime traffic scenarios. These methods are difficult to meet the actual needs of shore-based supervision. Based on the RFRM (Risk Filtering, Ranking & Management) concept, this paper constructs a key risk indicator system affecting the overall navigation situation in high-density ship areas and designs mapping functions and calculation methods for multiple indicator values. The limitation of single weighting is overcome by integrating subjective and objective weights to form comprehensive weights. The Finite Interval Cloud Model (FICM) is adopted for quantitative rating of navigation situations in high-density areas, addressing the assumption deviation of traditional cloud models that rely on normal distribution. Case validation confirms the effectiveness of the proposed model. The research findings can provide decision support for regulators in supervising ships in mixed scenarios and address the research gaps in regulatory-oriented situation assessment under such scenarios.
Addressing navigation safety challenges posed by restricted harbour channels and dense traffic, this paper proposes an adaptive ship navigation decision-making method under multiple constraints, using the southern waters of Hong Kong's Victoria Harbour as a case study. First, a digital traffic environment is constructed to enable real-time situational awareness in confined waters. Second, drawing inspiration from human drivers' cognitive mechanisms, a motion prediction and control framework based on sequential rolling is established. This framework employs a separated architecture for prediction and simulation models, resolving the conflict between rapid decision simulation and simulation execution through a closed-loop feedback mechanism. Building upon this, a multi-objective optimisation evaluation function is developed that comprehensively considers dynamic collision risks and environmental geometric constraints. Under strict adherence to the 1972 International Regulations for Preventing Collisions at Sea (COLREGs) and sound seamanship principles, it simultaneously solves for optimal collision avoidance strategies and recovery plans. Case studies using real historical Automatic Identification System (AIS) datasets demonstrate that the proposed method dynamically adapts to residual system errors and changes in target ship course/speed, achieving safe avoidance under multiple constraints while promptly tracking the route (resuming navigation) after completing the clear-way maneuver.
To optimise the service life and operating cost of the hydrogen fuel cell ship energy system and improve the ship's endurance, a two-layer energy management strategy (EMS) is proposed in this paper. The first layer of the EMS is based on nonlinear model predictive control (NMPC), which combines the analysis of the load demand of the multi-fuel cell stack (MFCS) with the battery to minimise the operating cost and maximise the endurance. The dynamic programming (DP) is used to provide an initial reference and an optimisation range for NMPC to further enhance its convergence speed and optimisation quality. The second layer addresses the power allocation to each fuel cell (FC) in the MFCS and based on the MFCS optimal efficiency range (MER) and considering power generation characteristics and performance degradation of FC, the MFCS optimal power allocation curve (MOC) is proposed to improve the fuel efficiency of the MFCS while decreasing the performance degradation rate of the FC and promoting the consistent performance degradation of the MFCS. The experiment demonstrates that the EMS proposed in this paper can effectively reduce the operating cost, extend the system service life, promote consistent FC performance degradation, and enhance the endurance of the ship.
Underwater explosion loads induce severe nonlinear damage in marine structures such as ship hulls and offshore platforms. Traditional experimental and numerical simulation methods suffer from high costs, long calculation cycles and inability to meet real-time prediction requirements. To overcome these drawbacks, a real-time prediction method for damage characteristics of flat plate structures under underwater explosion loading is proposed based on the U-Net deep learning network. Predictive performance is validated against experimental data and numerical results from LS-DYNA and ABAQUS in terms of damage mode, breach size, plastic deformation and computational efficiency. Results indicate that: (1) The U-Net model accurately predicts breach shape and size, with a maximum error of only 1.26% compared with LS-DYNA simulations; (2) The average error of plastic deformation prediction is within 10%, satisfying accuracy requirements for underwater explosion structural damage research; (3) The model prediction time is approximately 2.5 s, which is 40,320 times and 1008 times more efficient than the LS-DYNA fluid-structure coupling method (approximate to 28 h) and the ABAQUS acoustic-solid coupling method (approximate to 0.7 h), respectively. This study provides an efficient and high-precision real-time prediction approach for numerical simulation and damage evaluation of structures subjected to underwater explosion loading.
This study investigates whether and how mental health mediates the relationship between psychosocial workload and work performance among ferry seafarers operating in European Union and United Kingdom waters. Survey data were collected from 144 active ferry seafarers and analysed using a chained mediation model based on structural equation modelling. Validated instruments were employed to measure workload (ERI-S, Overcommitment), mental health (GAD-7, PHQ-9, WHO-5), and work performance (IWPQ). Results indicate that overcommitment shows a strong positive association with anxiety, confirming that the high-frequency, time-critical operational profile of ferry services creates sustained psychological pressure. Contrary to expectations, general well-being rather than depression emerged as the most consistent predictor of task, contextual, and counterproductive performance outcomes. The analysis identifies a significant indirect pathway from overcommitment to performance impairment via anxiety and reduced well-being, accounting for a substantial proportion of the total effect. These findings suggest that performance deterioration in high-intensity ferry operations primarily arises from the erosion of positive psychological resources rather than from clinical symptomatology. The results highlight the importance of proactive monitoring of well-being and targeted interventions addressing overcommitment to enhance crew resilience and maritime safety.
To solve the maximum power point tracking control problem of wave energy conversion systems, a prescribed performance fixed-time H infinity tracking controller is proposed. The mathematical model of direct-drive wave energy conversion system with external disturbances is established. The main novelties are as follows: (1) Prescribed performance control is integrated with fixed-time control, and the initial value of the prescribed performance function is introduced into error conversion to solve the problem that the initial error of the system exceeds the prescribed boundary, and the tracking error of the system converges to zero within a fixed time. (2) Radial basis function neural networks are designed to approximate the uncertainties caused by parameter changes and compensated in the controller within a fixed time. (3) H infinity performance criterion is introduced to attenuate external disturbances, so the robustness of system is enhanced. The simulation results show that the proposed controller is better than finite time control.
This study presents a scenario-based environmental and economic assessment of hybrid propulsion systems and alternative marine fuels within Panama Canal-centred maritime operations. Dual-fuel (DF) engines configured in diesel-electric and mechanical propulsion systems, as well as fuel cell/battery electrification, were evaluated across two bulk carrier case studies. Cold ironing (CI) was assessed independently to quantify its contribution to port-side emission reductions. Ammonia DF systems achieved the highest tank-to-wake (TtW) greenhouse gas (GHG) reductions, up to 78.49% with CI, while green ammonia reduced well-to-wake (WtW) emissions by 66.03%. Liquefied Natural Gas (LNG) systems achieved GHG reductions aligned with 2030 targets. Methanol offered moderate benefits, while first- and second-generation biodiesel failed to meet GHG targets and showed limited environmental viability. Economically, grey ammonia yielded the lowest levelized cost of energy ($140.61/MWh), while green ammonia reached $469.01/MWh, indicating a trade-off between sustainability and cost. LNG and methanol remained competitive under both low- and average-price scenarios. The Panama Canal's strategic location makes it a key connection hub for low-energy-density alternative fuels, which require larger storage volumes and pose trade-offs between cargo capacity and refuelling frequency. As a mid-route chokepoint, the Canal enables reliable refuelling, supporting the operational viability of zero-carbon fuels in long-haul shipping.
As maritime operations become increasingly dynamic and safety-critical, supporting effective learning and decision-making remains a central challenge in maritime education. Within such training contexts, feedback plays a central role in helping seafarers reflect on their weaknesses. Building on this, this study examines seafarers' perceptions of artificial intelligence (AI)-based feedback in maritime training by comparing three feedback formats following a COLREGs assessment: traditional feedback, AI-generated global performance summaries, and AI-generated personalised feedback at the question level. Forty-nine seafarers evaluated each feedback format across six instructional dimensions: correctness, sufficiency, usefulness, clarity, adaptiveness, and motivational impact.Statistical analysis confirmed that both AI-based feedback approaches significantly outperformed the traditional method by providing more relevant guidance, better addressing individual learning needs, and enhancing the overall instructional value. Participants expressed a clear preference for AI-generated feedback tailored to individual questions, which they described as more motivating, clearer, more informative, and more learner-centred. These findings highlight the potential of AI to deliver scalable, faster, and more cost-efficient feedback to support engagement and learning in maritime training.
