Engineering

Abstract: The rectilinear motion of a slender body in a fluid beneath an ice cover is considered in this study. The ice cover is modeled as a thin viscoelastic plate. The fluid is assumed to be ideal and incompressible, and its motion is irrotational. The problem is solved within the framework of potential flow theory. The flow around the body is modeled using source–sink theory, and the calculated wave resistance values are validated through comparison with known numerical solutions obtained for viscous fluid theory. The wave resistance of an underwater body moving at a distance of two diameters from the ice–water interface is analyzed. The maximum values of wave resistance and the corresponding critical speeds of the body are investigated as functions of the water depth, length of the body, and parameters of the ice cover. It is found that as the water depth decreases, the maximum values of wave resistance increase. For shallow water, the critical speeds of the underwater body are proportional to the square root of the water depth. As the water depth increases, the critical speeds approach a relationship—dependent on the body length and ice cover parameters—that corresponds to the motion of a body beneath an ice cover in an infinitely deep fluid.

Abstract: The accelerating impacts of climate change and rising global energy demand underscore the importance of transitioning toward sustainable energy solutions. Acknowledging the crucial role of the maritime sector in global trade, its transition to sustainable energy can substantially support climate policy objectives.In this context, Iceland not only relies heavily on maritime activities but also has a unique position in renewable energy. Therefore, this study presents an exploratory policy assessment of the energy transition in Icelandic ports, based on desk research, secondary data analysis, systems thinking, and SWOT analysis.Global decarbonization policies and energy transition initiatives are reviewed. The key opportunities and vulnerabilities facing Icelandic ports with respect to the energy transition are identified.The results indicate that Icelandic ports are well-positioned to offer sustainable energy solutions, contributing to the maritime sector's green future. This is more evident in bigger Icelandic ports, which benefit from established infrastructure and sufficient marine traffic. At these ports, supply of both shore power and e‑fuels can be effectively accommodated. In contrast, smaller ports with limited marine traffic may prioritize shore power deployment as an initial step in their energy transition.The results provide a structured overview of energy-transition readiness in Icelandic ports and may support future planning and policy development.

Abstract: To reduce diagnostic uncertainty in hydraulic systems during vessel operation, this study investigated the correlation between fault features and monitoring parameters. Using monitoring data collected under normal operating conditions as the baseline, the association strength between multi-source monitoring features and ten fault modes was quantified via standardized deviation (Z-scores). On this basis, a hierarchical weight matrix, incorporating correlations between faults and features, devices and features, and subsystems and features, as well as lateral coupling coefficients, was constructed to capture hidden associations among signals, devices, and subsystems. Subsequently, a PGC-LightGBM algorithm was proposed, which embedded prior knowledge from the hierarchical weight matrix into model training through differentiated sample weighting: fault samples exhibiting larger deviations on physically correlated features were assigned higher training weights, thereby enabling multi-level fault diagnosis that accounts for multi-fault coupling and operational interactions across the entire hydraulic system. On a marine hydraulic pump station test bench dataset, the proposed method improved the macro-averaged F1 score from 75.3% to 77.8% under 5-fold stratified cross-validation, and from 63.2% to 68.2% on the independent test set. The fault-class average F1 rose from 59.9% to 65.4%, while the normal-class F1 of the weighted model reached 96.3%, remaining unaffected compared with 95.7% for the standard model. Ablation studies indicated that fault-level physically informed weights were the main contributing factor to the performance improvement, while device-level, subsystem-level, and lateral coupling components provided richer physical interpretability. This study provides an effective reference for reliability analysis and risk prediction in marine hydraulic systems.

Abstract: In complex underwater acoustic environments, biases in the effective sound speed and propagation-delay estimation errors may introduce skewness and heavy tails into measurement noise, thereby degrading maneuvering target tracking accuracy. Under these conditions, noise parameter estimates obtained through per-time-step variational updates are susceptible to measurement outliers, while coupling among parameters may hinder effective parameter updates. To exploit recent observations and improve parameter estimation stability, this paper proposes an adaptive interacting multiple model sliding-window variational Bayesian unscented filtering algorithm. Within the interacting multiple-model framework, variational inference and the unscented transform are combined to estimate the target state and noise-related variables at each time step. Sliding-window parameter fusion is applied to the location parameter and mixing variable, while sliding-window maximum a posteriori estimation is employed to update the shape parameter. Furthermore, an adaptive process noise covariance adjustment strategy based on changes in model probabilities is developed to balance rapid response to target maneuvers with robust tracking during steady motion. Simulation and lake-trial results demonstrate that the proposed algorithm outperforms the benchmark methods under skewed heavy-tailed measurement noise and target maneuvers. In the lake trial, the mean position and velocity tracking errors are reduced by 54.5% and 10.7%, respectively.

Abstract: The paper presents an integrated methodology for designing water-filled acoustic impedance tubes and interpreting measurements of the acoustic impedance of single- and multilayer polymer and elastomer in underwater conditions. The first part consolidates a family concept of three circular, water-filled tubes dedicated to different frequencies, respectively, based on a plane-wave criterion, wall-stiffness constraints and a four-hydrophone configuration with two end-cap sensors and two mid-span sensors positioned symmetrically around the sample. The proposed geometry sizing algorithm derives tube length, inner diameter, wall thickness, tube material selection and deaeration requirements for the laboratory stand. The second part links the hardware proposal to the physics requirements in water by recalling the relations between sound pressure, par-ticle velocity and specific impedance. Moreover, there is a proposal for formulating reflection, transmission and absorption coefficients for normal incidence at water–solid plates. Using a Transfer-Matrix Method (TMM), single-layer and multilayer configurations such as water–elastomer–water and water–rubber–polymer–water are modelled with complex wavenumbers to account for frequency-dependent phase velocity and attenuation. This approach enables prediction of resonance bands and broadband absorption behaviour in the 10–100 kHz range.

Abstract: Deep reinforcement learning (DRL) offers a promising approach to autonomous navigation and collision avoidance for unmanned surface vehicles (USVs) by learning decision-making policies through interaction with maritime environments. However, existing methods often couple goal-directed navigation and obstacle avoidance within a single policy network, without explicitly distinguishing their contributions or regulating the strength of avoidance intervention. This implicit coupling may induce premature deviations from the intended course or inadequate avoidance maneuvers, reducing navigation efficiency and increasing collision risk. To address this limitation, we propose proximal policy optimization with a policy-splitting network (PSN-PPO), which restructures the PPO actor into jointly trained navigation and avoidance branches. The navigation branch generates a nominal navigation output, while the avoidance branch provides a bounded correction. A learnable risk gate modulates this correction based on encounter risk assessed over a finite prediction horizon. In the absence of obstacles or when the predicted risk falls below the activation threshold, the policy mean is determined solely by the navigation branch. A closed-loop simulation framework incorporating a three-degree-of-freedom vessel motion model is used to compare PSN-PPO with standard PPO across five scenarios: obstacle-free navigation, static-obstacle avoidance, head-on encounters, overtaking, and crossing. Both methods are trained independently in each scenario. Simulation results show that PSN-PPO improves navigation efficiency and collision-avoidance performance relative to standard PPO in the evaluated scenarios.

Abstract: Floating photovoltaic (FPV) systems are an increasingly attractive route to large-scale solar deployment on water bodies, but their structural integrity and energy yield are threatened by the wave environments to which they are exposed at nearshore and offshore sites. Floating breakwaters (FBWs), and keeled geometries in particular, are a candidate protection system. Here we develop a smoothed particle hydrodynamics (SPH) model of a moored, keeled floating breakwater using the open-source solver DualSPHysics, with waves generated by a piston wavemaker with active wave absorption and, for two of the irregular sea states, by the relaxation-zone method (Altomare et al., 2017, 2018). The model is assessed against wave-basin experiments in two stages: a verification stage under regular waves spanning six height–period combinations, and an assessment stage under irregular sea states (two JONSWAP spectra and a broadband white-noise excitation). We report the wave transmission and reflection behaviour, the breakwater motions and their response amplitude operators (RAOs), and find that, once the floating body, the mooring and the incident waves are resolved consistently, the SPH model reproduces the measured heave, roll and sway to within about 20 per cent over most of the regular-wave range and follows the measured RAOs of the irregular sea states, with an accuracy comparable to a boundary-fitted finite-volume model at a fraction of its computational cost.

Abstract: Saline intrusion poses a significant threat to the quality of water in Gatun Lake, which is the country’s main source of freshwater. Therefore, a thorough understanding of the mixing patterns between saltwater and freshwater is imperative in order to mitigate negative effects. This study analyzed salinity transport in the Panama Canal locks using the Smoothed Particle Hydrodynamics (SPH) method with DualSPHysics® software. The main objective was to simulate various lock operation scenarios under different hydrodynamic conditions in order to evaluate salinity propagation under typical configurations. The results show that the greatest influx of saltwater occurs when vessels descend into the ocean. Furthermore, the effectiveness of implementing retention structures, such as saltwater retention pits, in reducing the influx of saltwa-ter into the lake is demonstrated.

Abstract: Battery-electric and hybrid ships combine battery energy storage systems with networked control, machine-learning state estimation, digital twins, high-capacity charging, and, in some applications, second-life cells. These configurations introduce risks for which maritime approval instruments may lack verifiable acceptance criteria. This paper defines a Regulatory Readiness Level (RRL) from 0 (unrecognised) to 4 (verifiable acceptance) and applies it to nine frontier risks in three families: cyber-physical, AI-driven, and second life/lifecycle. The assessment covers IMO, EMSA, IACS, IEC, UL, NFPA, and battery requirements of five classification societies, using instruments in force on 31 July 2026. No risk reaches RRL 4 under the study’s target. Median readiness is RRL 2: one risk is at RRL 0, two at RRL 1, four at RRL 2, and two at RRL 3. The two RRL 3 cases illustrate different limitations: state-of-health estimation is verified periodically without qualification between tests, whereas mixed-chemistry and mixed-state-of-health packs are addressed through prohibition rather than qualification. Baseline priority places AI3, auditability of AI safety functions, first, with AI2 and SL1 in the next band; SL1 is sensitive to aggregation. Ordinal dominance excludes CP2, CP3, and SL1 from the lead without assuming equal spacing. Sensitivity analysis shows that uncertainty in readiness classification has a larger effect on ranking than uncertainty in severity and exposure. Adjacent-sector assurance mechanisms provide templates for maritime incorporation, and the paper proposes an acceptance objective, verification method, and regulatory vehicle for each risk.

Abstract: Sensor failures, communication interruptions, and other monitoring problems can lead to missing acceleration responses in structural health monitoring of offshore platforms. To address this issue, a hierarchical feature fusion reconstruction (HFFR) method is proposed. The method employs multi-scale local morphology encoding to characterize local response morphology over different temporal neighborhoods, uses bidirectional temporal modeling to capture response evolution before and after the missing data, and adaptively weights temporal information according to feature correlation. A dataset is constructed using numerical simulation data from an offshore platform under different wave–current conditions, and the proposed method is evaluated through ablation and comparative experiments. After introducing multi-scale local morphology encoding and adaptive feature weighting into the bidirectional temporal modeling framework, the RMSE decreases from 0.5036 to 0.1109, while R² increases from 0.7581 to 0.9883, confirming the effectiveness of the hierarchical information representation framework. The results show that HFFR can recover the major peaks and valleys, amplitude envelopes, and local fluctuations of the missing responses, thereby improving both reconstruction accuracy and the preservation of dynamic response characteristics. The current validation is mainly based on high-fidelity numerical simulations; therefore, the applicability of the proposed method in real monitoring environments should be further verified through physical model tests.

Abstract: Conventional catenary mooring systems for floating offshore wind turbines (FOWTs) face significant challenges related to high installed cost, large seabed footprint, and supply chain constraints. To address these challenges, this paper proposes a novel length-varying tension-fixed (LVTF) mooring concept characterized by a compact seabed footprint. Theoretical formulations are developed, and parametric study results are presented on the fundamental station-keeping characteristics of a single-anchor LVTF mooring concept for a 15 MW semi-submersible FOWT. Preliminary design of a multi-anchor passive LVTF mooring system in 100 m water depth with a mooring anchor radius of 70 m is performed for a 16 MW multi-column Spar-type FOWT for potential deployment in the South China Sea. A wave basin model test campaign is conducted to validate the design of the passive LVTF mooring system for the Spar-type FOWT under extreme typhoon conditions. The model test results, including platform global motions and mooring line tensions, are compared with those of the conventional catenary mooring design. It is found that the small-footprint passive LVTF mooring system can provide adequate station-keeping forces for the 16 MW Spar-type FOWT. A preliminary cost assessment indicates a potential installed-cost reduction of approximately 67% in comparison with the conventional catenary mooring design.

Abstract: Ship safety assessment must accommodate aleatory variability, epistemic incompleteness, and heterogeneous representations of uncertainty. This study develops a generalized probability theory (GPT) as an extended probabilistic framework that interfaces with classical probability, fuzzy theory, grey system theory, and Dempster-Shafer (D-S) evidence theory while preserving their distinct semantics. Generalized random numbers (GRNs) represent information at different levels of precision; the degree of overlap (DOO) characterizes shared information structures; and the residual event W retains probability mass outside the currently identified factor set. Generalized total probability and generalized Bayesian inference enable forward propagation and backward diagnostic updating, respectively. Using multi-factor coupling data from complex waters, the framework distinguishes empirical co-occurrence from information overlap, corrects double counting among coupled factors, and supports higher-order propagation and posterior updating. The case study is limited to real-valued coupling data. Within this validation boundary, the generalized probability theory provides a consistent and interpretable interface for uncertainty representation and probabilistic inference in ship safety assessment.

Abstract: This paper investigates a nonlinear strong prescribed-time cooperative control theory, aiming to achieve spatio-temporal consistency in multi-agent control under disturbance. Taking multiple unmanned surface vessels(USVs) as the application object, a strong prescribed-time cooperative target-guided coordinate control(SPT-TACC) method is proposed to ensure that USVs can complete the cooperative target tracking task at the prescribed time. First, the error models of the cooperative kinematic subsystem and the kinetic subsystem are converted into periodic delayed forms, and a strong prescribed-time cooperative control system based on periodic delayed feedback is designed, enabling each USV to arrive at the desired tracking point simultaneously at the prescribed time. Second, a complete stability proof is provided for the nonlinear strong prescribed-time cooperative control theory with physical constraints. Finally, an empirical formula for the lower bound of the prescribed convergence time of the system with physical constraints is summarized, and the rules for setting the prescribed convergence time of each subsystem are summarized to avoid the divergence problem of the control system caused by unreasonable settings of multiple prescribed time. Simulation comparison results effectively verify the effectiveness of the proposed control method.

Abstract: Marine cable winch systems used in offshore operations are exposed to substantial tension fluctuations produced by vessel heave motion and irregular ocean-wave disturbances. These fluctuations can compromise operational stability and may contribute to undesirable slack–re-tension cycles, mechanical loading, fatigue, and reduced reliability. Conventional proportional-integral (PI) controllers remain attractive because of their simplicity and dependable steady-state behavior, but fixed-gain feedback control may be less effective when operating conditions and disturbance patterns vary. This study investigates a reinforcement-learning approach based on Proximal Policy Optimization (PPO) combined with Long Short-Term Memory (LSTM) recurrent memory for adaptive marine winch tension control. The controller is trained entirely in a Unity ML-Agents simulation environment containing a nonlinear drum-winch plant and a stochastic JONSWAP irregular-wave disturbance model. The agent uses a nine-dimensional observation vector comprising normalized tension, setpoint, tracking error, drum angular velocity, rope length, motor torque, dominant wave-phase sine and cosine components, and tension rate of change. A continuous normalized motor-torque command is generated at 60 ms decision intervals. Training employs a graduated curriculum over an 8-20 kN setpoint range, with an initial fixed 8 kN phase followed by randomized setpoints. The final reported model is evaluated against a manually tuned PI controller using randomized JONSWAP realizations. Across the combined operating range, the PPO-LSTM controller achieves a mean tension error of 1.724 kN compared with 1.780 kN for the PI controller, corresponding to a 3.1% improvement that is not statistically significant at the 0.05 level. More importantly, the reinforcement-learning controller reduces error standard deviation by 17.3% overall, with reductions of 26.3% and 39.2% in the 12-15 kN and 15-20 kN ranges, respectively. At the 95th percentile of errors, the overall reduction is 6.4%. The results indicate that the main benefit of the recurrent RL architecture is improved consistency and robustness rather than a large reduction in average tracking error. The study also identifies curriculum bias at low setpoints, increased slack incidence in the 8-12 kN range, catastrophic forgetting during targeted high-setpoint fine-tuning, and the simulation-to-reality gap as important limitations. The expanded discussion therefore emphasizes both the potential and the engineering constraints of PPO-LSTM control for marine winch applications.

Abstract: This study presents a comprehensive parametric optimization of the blade geometry for the so-called Vertical Axis Autorotation Current Turbine (VAACT) using Computational Fluid Dynamics (CFD) simulations at high-blockage conditions. The optimization process aimed to minimize the resisting torque produced by the returning blade, the major contributor to performance loss in drag-type vertical axis turbines. The investigation was first conducted to assess the effects of maximum camber and its position on turbine efficiency. A response surface relating the power coefficient (C_P) to maximum camber (M), camber position (P), and tip speed ratio (TSR) was then generated and used as input for the optimization routine. The optimal configuration was M = 40%, P = 65%, and TSR = 1.06, achieving an efficiency of 40.68% at 21.4% blockage ratio. The enhanced blade demonstrated a 21% improvement in performance compared to the conventional S-shaped profile, primarily due to the reduction of resisting torque during the returning phase. The intra-cycle analysis showed that the optimized turbine allowed a better distribution of hydrodynamic effects along the cycle, improving efficiency. Flow field analysis confirmed that the optimized geometry delays vortex shedding and reduces pressure gradients along the returning blade, leading to improved hydrodynamic behavior. The results demonstrate this procedure offers an effective way for the design of vertical axis hydrokinetic turbines.

Abstract: In deep-water environments, the coupled effects of hydrostatic pressure, explosion-induced shock waves, and bubble pulsation can produce complex nonlinear dynamic responses and instability in stiffened cylindrical shells. Clarifying these response mechanisms is critical for the safety assessment and blast-resistant design of deep-sea equipment. In this study, an acoustic-structure coupled numerical method was developed for stiffened cylindrical shells subjected to underwater explosion loading and validated using deep-water explosion tests conducted in a pressure vessel. The results show that the proposed method predicts the dynamic response of stiffened cylindrical shells under deep-water explosion loading with satisfactory accuracy. Based on this validated model, a systematic investigation was conducted to evaluate the effects of hydrostatic pressure, stand-off distance, shell-plate thickness, and stiffener number on the deep-water explosion response of stiffened cylindrical shells. The findings provide practical guidance for blast-resistant design and parameter optimization of deep-water stiffened cylindrical shells.

Abstract: Radar sensors, which enable the identification of the navigational situation and a preliminary assessment of collision risk, form the basis for safe ship control. This paper presents the author’s quantitative methods for assessing collision risk in the context of multi-ship navigation, and then synthesizes safe control methods. This study proposes four neural domain variants and three mathematical models of collision risk. Proprietary methods are presented: neural dynamic control (NDC) and game-based control (GC). An experimental comparison of these control methods is conducted using data from real-world navigation scenarios. Therefore, under favorable traffic conditions, the NDC method proves to be the most effective, while the GC method facilitates effective cooperative and non-cooperative management in situations of restricted ship traffic. The safe control methods proposed in this study will contribute to increased navigation safety, particularly in situations of high ship traffic density and challenging environmental conditions.

Abstract: Near-seabed contact operations with underwater hexapod robots can require one leg to execute a contact task, which reduces the stability of the remaining asymmetric five-legged support. To address this problem, this study proposes a five-legged asymmetric coordinated stability control method that integrates gait rhythm planning with attitude feedback. The method decouples the left middle leg from support, propulsion and CPG phase evolution to form a “5+1” asymmetric support base. This configuration reduces the periodic influence of task-induced disturbances on body balance. Meanwhile, a five-legged Hopf-CPG rhythm maintains continuous gait under asymmetric support. Low-bandwidth attitude feedback modulation is introduced between the CPG-generated foot trajectory and the inverse-kinematics input to balance attitude correction with foot-contact continuity. Full-degree-of-freedom underwater simulations in Webots show that, at a flow speed of 0.8 m/s, the method reduces combined attitude RMS by 41.52% relative to the no-feedback strategy. Relative to high-gain PD control, it reduces the RMS rate of change of the control output by 71.32%. These results indicate that the method improves input smoothness and compatibility with five-legged gait rhythms, with a moderate trade-off in transient attitude suppression. It therefore offers a coordinated approach to balancing attitude stability and gait continuity under asymmetric operation.

Abstract: Numerous studies indicate that a flapping-foil thruster installed at a ship’s bow can convert wave energy into propulsive power and reduce added wave resistance while improving dynamic stability. Building on prior research into the drag reduction mechanism of an elastic bow-flapping-foil system, this paper further compares its resistance reduction and motion response across three typical ship forms (DTMB 5415, KCS, and Wigley) under similar wave conditions. Using the ISIS-CFD solver in NUMECA software, the study simulates the ship coupled with a semi-active elastic flapping foil in head waves, analyzing the influences of spring stiffness, foil size, installation position, and encounter phase on system performance. Numerical results show effective wave energy harvesting, boosting propulsion and stability. Under optimized parameters, ship pitch and heave amplitudes decrease by up to 20.14%, resistance reduction reaches 9.60%, and DTMB 5415 achieves a comprehensive drag-reduction and thrust-increase efficiency of 31.39%. Further analysis reveals that thrust performance does not rise proportionally with spring stiffness, whereas drag and heaving reduction improve with increasing stiffness. The system performs best at a wavelength-to-ship-length ratio of 1.2 and a foil encounter phase of −90°. This work supports parameter optimization for such systems based on practical application scenarios and requirements.

Abstract: Submarine telecommunication cables carry over 99% of international data traffic yet remain persistently vulnerable to geophysical, anthropogenic, and oceanographic hazards. Existing risk assessment methods , GIS-based least-cost path analysis and cable-body condition monitoring , operate in isolation, cannot fuse heterogeneous multi-modal data, and yield neither uncertainty estimates nor physics-consistent predictions. We present MarineGuard-GNN, a physics-informed multimodal heterogeneous graph neural network that unifies four hazard modalities , bathymetric, oceanographic, vessel traffic, and seismic , in a single relational graph over four semantic node types and four typed relation edges. Three novel technical contributions distinguish the framework: (i) the Cross-Modal Spatiotemporal Tokenizer (CMST), which jointly encodes GEBCO bathymetric raster fields via a SatMAE-pretrained ViT-S and NOAA AIS vessel trajectories via a Transformer-XL into a unified 256-dimensional token space, addressing the modality-heterogeneity gap; (ii) a Physics-Informed Heterogeneous Graph Transformer with a differentiable Mohr–Coulomb geomechanical shear-stress regularization loss Lphys that prevents physically inconsistent risk assignments at steep bathymetric gradients; and (iii) a Bayesian Uncertainty-Aware Risk Head using Monte Carlo Dropout (K=50, p=0.3) to produce calibrated epistemic uncertainty maps for inspection prioritization. Under strict 4-fold geographic cross-validation across the Pacific, Atlantic, Indian, and Mediterranean basins using five publicly available datasets, MarineGuard-GNN achieves the highest mean AUC-ROC (0.7228±0.0665) and Average Precision (0.6418±0.1040) among five baselines. A Dijkstra-based spatial route optimizer reduces mean predicted fault risk by 0.0035 absolute on the Atlantic New York–UK corridor, avoiding one additional high-risk node at a distance overhead of only +7.4 km (+0.56%). All code, preprocessing pipelines, and datasets are publicly available.