Engineering

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 and absorbed through the relaxation-zone method [1,2]. 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 there is reasonable agreement in the SPH simulations at longer wave length conditions.

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.

Abstract: Ship path planning is a central challenge in autonomous navigation for unmanned surface vehicles and maritime autonomous surface ships. It is not simply a shortest-path problem, but a constrained sequential decision process that must reconcile collision risk, route efficiency, COLREGs compliance, vessel dynamics, and environmental uncertainty. Here we review the field through a unified framework based on planning scope, decision basis, and deployment requirements. We examine search- and sampling-based, geometric and rule-based, optimization-based, learning-driven, and hybrid methods, with particular emphasis on deep reinforcement learning for discrete decisions, continuous manoeuvring, multi-vessel interaction, and safety-oriented control. Representative studies are compared across objective and reward design, state representation, exploration and policy optimization, rule integration, disturbance modelling, simulation platforms, and operational validation. The synthesis identifies persistent barriers, including ambiguous rule formalization, partial observability, strategic coupling among vessels, inconsistent benchmarks, limited cross-scenario generalization, and insufficient full-scale validation. We further discuss priority directions in explicit safety constraints, digital twins, transfer and meta-learning, world models, scalable multi-agent coordination, and large-model-assisted mission reasoning. We argue that progress will depend less on further algorithmic proliferation than on integrated, verifiable architectures that combine data-driven adaptation with model-based structure, standardized evaluation, and staged real-world assurance.

Abstract: Since the heavy clutter seriously restricts the ability of radar to detect target, it is significant to build the target detector under heavy clutter. For practical situations without the secondary data or prior knowledge of target and clutter, this paper proposes a polarization-space-time detector. First, a general radar model is constructed for multiple pulses, multiple arrays, and multiple polarizations. Based on the theory of ternary hypothesis, the secondary data free (SDF) GLRT detector is proposed, which can maintain the constant false alarm probability (CFAR) in inhomogeneous clutter. Then, this paper proposes a matrix transform operator and an adaptive detection method using sliding window respectively, which do not need to know the steering vector of radar and the noncentral parameter of clutter in advance, so that the SDF-GLRT detector can adapt to different application scenarios. In addition, this paper optimizes the polarization waveform of the radar system by constructing a projection matrix, which can give the closed-form solutions of the optimal polarization and worst polarization, instead of relying on numerical solution. Finally, the performances of SDF-GLRT detector and other three detectors are compared by simulated and real data, which verifies that SDF-GLRT detector in this paper can still maintain superior performance in various clutter environments without secondary data.

Abstract: Monitoring the current technical condition of a marine internal combustion engine and diagnosing the causes of deviations of its operating parameters from reference values are important tasks for ensuring the efficient and safe operation of marine power plants and vessels in general. This paper investigates the theoretical founda-tions, methods, and practical implementation of a digital twin of a marine internal combustion engine as a tool for solving these tasks. The main requirements and stages of digital twin development are presented, including: calibration of the mathematical model; determination of the set of sensors and other input data obtained from the op-erating engine; identification of the current engine operating mode; definition of objec-tive functions characterizing deviations of engine operation from reference conditions; and automation of fault detection algorithms. In particular, modifications introduced into the mathematical model of engine operating processes are presented, allowing more flexible representation of fuel injection characteristics as well as automatic ad-justment of model parameters according to the identified engine operating mode. A fault detection method based on simulation modeling and the classical theory of design of experiments is proposed. The four-factor fractional factorial experiments were per-formed, followed by analysis of the obtained results using second-order polynomial regression equations. The results demonstrate that the proposed method provides cor-rect identification of engine faults, although the numerical values of the investigated factors are determined with a certain error. Therefore, further development of the proposed approach requires improvement of the mathematical model, formulation of explicit rules for selecting the investigated factors, and automation of fault identifica-tion methods under conditions of cycle-to-cycle variability of the engine working pro-cess and measurement uncertainties.

Abstract: This study validates the URANS k-ω SST turbulence model in OpenFOAM for simulating 2D flow around a fixed circular cylinder, a key benchmark for offshore structure design. Balancing accuracy and computational cost, the model was tested for laminar (Re=40) and subcritical turbulent (Re=10,000) regimes. Results showed excellent agreement with benchmarks: for laminar flow, a drag coefficient (Cd) of 1.54 and separation angle of 52.3°; for turbulent flow, a mean Cd of 1.15, a Strouhal number of 0.22 at x/D=2, and a separation angle of 86.5°. This confirms the model's reliability for predicting integral forces and vortex shedding in these regimes. However, the study also highlights the inherent limitations of the 2D domain due to its inability to resolve 3D anisotropic wake turbulence.

Abstract: The ship recycling industry experiences frequent accidents with severe consequences attributed to human and organizational factors. Existing human factors frameworks, including the Human Factors Analysis and Classification System (HFACS), provide comprehensive taxonomies for accident analysis, but their complexity and granularity pose challenges in low-resource, high-risk contexts such as ship dismantling yards. This paper presents the development of HFACS-SR (HFACS for Ship Recycling), a simplified human factors taxonomy tailored to the ship recycling sector, retaining the systemic insight of HFACS while enhancing usability for practitioners in shipbreaking yards. A participatory card-sorting study with 14 industry experts (safety officers, academics, regulators, supervisors, and an NGO representative) used 117 human factor codes derived from the SHIELD taxonomy, developed under the EU SAFEMODE project for aviation and maritime safety occurrences. Participants sorted the codes via KardSort, rated their relevance to ship recycling, and suggested modifications. Quantitative tiering identified 50 high-consensus codes. Qualitative refinement merged overlapping codes, promoted practitioner-identified gaps, and retained threshold-adjacent codes on evidential grounds, reducing the taxonomy from 117 to 53 codes across five levels. A new External Influences level captures regulatory, economic, and political pressures unique to ship recycling and absent from conventional HFACS. Inter-rater agreement was substantial (Fleiss' κ = 0.69). Plain language renaming further enhanced frontline usability. HFACS-SR preserves the multi-layered systemic perspective of HFACS while improving applicability in ship recycling, offering practitioners a clearer, context-specific framework for accident investigation and safety management.

Abstract: Autonomous inspection of ballast water tanks requires three-dimensional (3D) LiDAR-based simultaneous localization and mapping (SLAM) in Global Positioning System (GPS)-denied, geometrically repetitive interiors, where sensing, mapping, and control modules share a limited onboard memory budget. Graph SLAM backends that rely on sparse factorization can incur fill-in, increasing peak memory and limiting deployment on edge computers. The proposed architecture couples a robust hierarchical bundle adjustment frontend with a factorization-free Kaczmarz backend. The frontend combines residual-adaptive weighting, damped and bounded pose updates, soft fallback, local-map compression, and memory-aware keyframe control. The backend stores the whitened Jacobian in compressed sparse row (CSR) format and performs row-wise projections without explicitly forming the normal equations, a Cholesky factor, or a transpose cache. Evaluation used Norwegian University of Science and Technology (NTNU) Ballast Water Tank missions 1--3, containing 851, 1202, and 1084 LiDAR frames. Following robust local bundle adjustment and verified similarity alignment, translational root-mean-square errors were 0.080, 0.110, and 0.127m, corresponding to 0.137%, 0.144%, and 0.122% of the reference path lengths; archived baseline ratios ranged from 0.281% to 0.372%. The results support a numerical architecture that combines frontend stabilization, row-wise optimization, and memory-aware policies for resource-constrained marine inspection robots.

Abstract: Ship recycling is expanding in Nigeria with no dedicated statutory framework to govern it, creating a mismatch between a high-hazard industry and an underdeveloped regulatory environment. Although Nigeria ranks among the top ten emerging ship-recycling countries, the sector remains informally governed, with overlapping mandates, thin enforcement capacity, and fragmented risk-assessment practices. This study evaluates Nigeria's approach through a robustness lens, drawing on Rasmussen's socio-technical levels, Renn's integrative risk-governance model, and Hale's robustness criteria, with Hollnagel's Safety-II as a complementary, proactive perspective. The analysis combines documentary review, field observations across four recycling hubs (Lagos, Warri, Onne/Port Harcourt, and Calabar), and semi-structured interviews with fifteen stakeholders, including yard managers, regulators, workers and safety officers, and maritime experts, followed by a multi-stakeholder validation workshop. Of nineteen sub-criteria, only six are formally present, four are partially addressed, and nine are absent altogether, and even the elements that exist on paper are generic and weakly enforced. Field visits revealed ad hoc practices, hazardous working conditions, weak supervision, and little institutional learning. On this basis we propose a Nigeria-fit Robustness-Focused Risk-Governance Framework built on three pillars: multi-level safety oversight, an integrative risk-governance process, and enhanced mitigation strategies. It emphasises legal recognition, clearer accountabilities, interdisciplinary assessment, transparent risk-acceptance criteria, continuous monitoring, and feedback loops, with a phased implementation pathway. The study shows why robustness, not resilience, should be the organising principle for governing emergent high-hazard sectors in developing economies, and offers practical steps for strengthening institutional capacity in Nigeria's maritime domain.