Engineering

Abstract: To ensure the operational safety of the OCTABUOY platform used for offshore wind turbine installation in shallow waters, an eight-point symmetric mooring system was designed based on its octagonal structural configuration. The system provides high horizontal stiffness and balanced load distribution, enhancing stability under complex environmental conditions.Physical model tests were conducted under combined wind, wave, and current loading, considering multiple wave directions, environmental cases, and five draft conditions. The mooring tensions and six-degree-of-freedom motions were systematically analyzed to evaluate system performance and safety.Results show that the proposed mooring system effectively limits platform motions and maintains stable load-sharing characteristics. The minimum safety factor under the most unfavorable condition exceeds the design requirement. In addition, the system demonstrates good redundancy: after single-line failure, remaining mooring lines redistribute loads without progressive collapse. Draft and wave incident angle significantly influence peak tensions and motion responses, with smaller drafts and oblique wave directions producing relatively higher loads.The experimental results confirm the reliability and safety margin of the eight-point mooring system and provide practical guidance for the engineering application and operational assessment of the OCTABUOY platform in shallow-water wind installation projects..

Abstract: A systematic experimental investigation was conducted on the motion response (RAO) and mooring performance of a novel disk-shaped buoy (geometric scale 1:10) subjected to combined wind, wave, and current actions. A hybrid experimental strategy was employed, integrating a large-scale wave flume (for long-period waves and currents) with a harbor basin (for short-period waves and wind), aiming to mitigate the scale effects inherent in Froude-scaled models, particularly with regard to drag force measurements. The test matrix included free decay in calm water, RAOs under regular waves, motion and mooring line tension under irregular waves, and measurements of wind and current drag coefficients. Key results indicate a natural roll period of approximately 3.0 s with a notably high dimensionless damping ratio (ζ ≈ 0.14–0.15), which is conducive to rapid motion attenuation. A pronounced resonance peak in the roll RAO (26.6°/m) was observed near the 3 s period. Under an extreme sea state (Hₛ = 13.8 m, Tₚ = 16.1 s), the maximum roll angle and dynamic mooring line tension reached 21.30° and 61.56 kN, respectively, the latter being about 3.0 times the static pretension. The mean wind drag coefficient and current drag coefficient were determined as 0.76 and 0.44. This research provides a validated dataset and critical insights for the design, mooring system optimization, and operational safety assessment of such disk-shaped buoys. The effectiveness of the hybrid testing approach is confirmed, and the favorable damping characteristic of this buoy form is highlighted.

Abstract: Coastal zones are facing rising exposure to climate-related hazards alongside intensifying human pressures, which highlights the need for robust tools to assess vulnerability. This study uses a GIS-based Coastal Vulnerability Index (CVI) to quantify and map relative vulnerability along ~13 km of shoreline in Al Hoceima Bay (northern Morocco). The proposed CVI integrates eight geological and physical indicators, including geomorphology, shoreline erosion and accretion rates, coastal slope, elevation, natural habitats, relative sea-level rise, significant wave height, and tidal range. Spatial analyses were performed using remote sensing data, historical records, field measurements, and Geographic Information Systems (GIS). The analysis reveals that 37% of the shoreline is categorized as high vulnerability, 44% is moderate, and 19% is low. Highly vulnerable sectors are primarily associated with low elevations, gentle coastal slopes, sandy beach systems, limited natural habitat protection, and proximity to river mouths. These findings demonstrate that the applied CVI provides a rapid and cost-effective framework for identifying priority areas for coastal management and climate adaptation. The proposed approach offers valuable decision-support insights for sustainable coastal planning in Al Hoceima Bay and other Mediterranean coastal environments characterized by limited data availability.

Abstract: In extremely shallow water environments, the limited water depth is comparable to the maximum bubble radius. The pulsation of an underwater explosion bubble is strongly constrained by both the free surface and the rigid seabed, exhibiting complex nonlinear coupling effects, which are of great significance for the safety assessment and protection design of nearshore engineering. To address this issue, an axisymmetric two-dimensional numerical model based on the Eulerian finite element method (EFEM) with operator splitting technique and the Volume of fluid (VOF) interface-capturing approach is established. Under the assumptions of inviscid and incompressible flow, a systematic numerical investigation is carried out to examine the effects of the water depth parameter λ, position parameter γ)and buoyancy parameter δ on the bubble dynamics and the evolution of free surface structures. The results show that the maximum bubble radius, pulsation period and jet characteristics are all significantly regulated by the above three parameters. Moreover, under multi-period bubble pulsation, different parameter conditions lead to diverse evolution characteristics of free surface structures including the water spike, wrinkles and water skirt. The findings reveal the governing mechanisms of key dimensionless parameters on the nonlinear bubble-multi-boundary coupling dynamics in extremely shallow water explosions, providing an important numerical basis and theoretical reference for the theoretical analysis and safety design of related shallow water explosion engineering problems.

Abstract:

This study evaluates the techno-economic feasibility of LNG regasification alternatives, including offshore platform conversion, floating storage and regasification unit (FSRU) retrofit, and onshore LNG terminals, under conceptual design conditions at a capacity of 100 MMSCFD. The analysis integrates cost estimation, project schedule, and technical maturity within a multi-criteria decision-making framework based on the Analytic Hierarchy Process (AHP), combining quantitative techno-economic results with expert judgment to support structured comparison of alternatives. Cost estimation is conducted using two approaches, namely cost–capacity scaling and analogous estimation, to examine their influence on feasibility outcomes. The results indicate that the conventional scaling method, using an exponent of 0.6, produces inconsistent results across configurations, overestimating costs for offshore-based systems while underestimating costs for onshore LNG terminals. Back-calculation of effective scaling exponents yields values of approximately 0.43 for offshore platform conversion, 0.37 for FSRU retrofit, and 0.78 for onshore LNG terminals, demonstrating that cost–capacity relationships are configuration-dependent and cannot be represented using a single uniform exponent. The AHP evaluation, conducted under two scenarios based on the applied cost estimation methods, shows that offshore platform conversion consistently achieves the highest feasibility ranking, followed by FSRU retrofit and onshore LNG terminals. While the ranking remains unchanged, the choice of cost estimation method influences the magnitude of score differences, affecting the strength of preference among alternatives. These findings highlight the limitations of conventional scaling approaches and demonstrate that offshore platform conversion can serve as a cost-competitive and time-efficient alternative for LNG infrastructure development, particularly in regions with existing offshore assets.

Abstract: Accurate prediction of nitrogen oxide (NOx) emissions from marine medium-speed four-stroke diesel engines is crucial for meeting increasingly stringent environmental standards. This paper focuses on optimizing the first and most significant reaction of the extended Zeldovich mechanism for the formation of nitric oxide (NO). A numerical engine model was developed and validated against experimental measurements of combustion pressure, power, and emissions at 81.95% of the Maximum Continuous Rating (MCR). The research analyzes the influence of various chemical reaction rate constants (k_1,f) on the accuracy of NO concentration predictions. The results demonstrate that by carefully selecting the kinetic parameters, the deviation of the numerical model can be reduced to only -0.93%. Utilizing the optimized constant for the primary Zeldovich reaction k_1,f = 1.8*10¹⁴ *e^(-38300/T), significantly improves the reliability of combustion and emission formation simulations.

Abstract: Efficient path planning and trajectory tracking are central to the safe and autonomous navigation of autonomous underwater vehicles (AUVs) in complex and unknown environments. To address the inherent challenges of safety, smoothness, and exploration efficiency in such settings, this paper presents an integrated framework that synergistically couples three enhanced core modules with complementary innovations. First, improved I-LazyTheta* and A-IRRT* algorithms are developed to incorporate safety margin collision detection and dynamic obstacle avoidance weight regulation, which enable efficient generation of collision-free paths that proactively maintain safe clearance in cluttered 3D spaces. Second, a trajectory tracking module based on a finite-state machine is designed, integrating B-spline optimization and a curvature-adaptive speed control mechanism to ensure high-precision following with guaranteed path smoothness and trackability. Third, a novel 3D autonomous exploration strategy tailored to underwater sonar constraints is constructed, combining frontier point clustering, multi-dimensional information gain evaluation, and traveling salesman problem (TSP) path optimization to achieve efficient unknown environment traversal while significantly reducing redundant detection and energy consumption. The proposed framework supports modular decoupling for independent reuse as well as integrated collaborative operation, offering flexible adaptability to diverse underwater robotic platforms. Simulations demonstrate that the integrated approach achieves superior performance in path safety and tracking accuracy, along with an exploration coverage of 79.08%, validating its effectiveness for robust AUV autonomy in complex underwater scenarios.

Abstract: Dolphins are widely recognized as intelligent marine mammals with sophisticated communication and echolocation. Accurately classifying their whistles is essential for understanding how they communicate and for tracking population size, structure, and distribution. Here, we assemble a large, high-quality dataset of dolphin whistle signals collected at the Chimelong Ocean Kingdom, including a whistle type not previously available to researchers. We then explore Convolutional Neural Networks (CNNs) for classifying whistles of the Indo-Pacific bottlenose dolphin (Tursiops aduncus), testing 5 CNN architectures to analyse the signals. Model performance is reported using mean Average Precision (mAP), showing that CNN approaches can reliably separate different whistle classes. To probe robustness, we also introduce noise at defined SNR levels to increase testing complexity and assess the stability of the classifier. We use Bellhop for channel simulation to construct the channel impulse response. The simulated data can be used as augmented data to add to the original data training set. The results did indicate that this can enhance the robustness of the classification model. This work provides valuable tools for marine biologists and researchers specialising in animal acoustics, enhancing the understanding of dolphin communication. It also contributes to the conservation and management efforts of dolphin populations, offering significant insights into their behaviour and ecological needs.

Abstract: Illegal, unreported, and unregulated (IUU) fishing threatens marine ecosystems in the Western Pacific. Traditional patrol strategies suffer from low efficiency due to insufficient utilization of multi-source surveillance data. This study proposes a maritime patrol framework integrating AIS fishing effort, Sentinel-1 SAR dark vessel detections, and vessel encounter records. An Adaptive Priority-Boosted Ant Colony Optimization (APB-ACO) algorithm with two-phase deadline-aware construction ensures high-priority coverage within 72 hours while minimizing total distance. Experiments on real satellite datasets demonstrate that APB-ACO achieves 7% shorter routes with 46× lower variance than conventional methods, with 100% high-priority task coverage. The framework provides an effective decision-support tool for maritime law enforcement. This framework can serve as a practical decision-support tool for maritime law enforcement and marine resource management.

Abstract: The rapid and accurate assessment of residual ultimate strength after ship damage is crucial for rescue decision-making and navigation safety, while traditional methods struggle to meet the demands of complex random damage scenarios in terms of efficiency or accuracy. This study proposes a hybrid framework that integrates high-fidelity nonlinear finite element simulation (FEM) and a Bayesian-regularized backpropagation neural network (BPNN). FEM is used to accurately simulate a large number of random damage scenarios, generating a physically credible benchmark dataset. BPNN serves as an efficient surrogate prediction model, with its key parameters—the number of hidden layers and the training algorithm—systematically optimized to enhance generalization capability. The results show that: 1) The FEM simulation results deviate by less than 5% compared to the Smith method, validating the reliability of the dataset. 2) The prediction performance of BPNN is highly dependent on the number of hidden layers and the training algorithm, exhibiting non-monotonic variation, with an optimal parameter combination identified as 8 hidden layers paired with the Bayesian algorithm, achieving a prediction regression value R of 0.91662. 3) Deep networks are prone to overfitting, while shallow networks suffer from insufficient feature capture. 4) The Bayesian algorithm performs best in terms of overfitting resistance and stability. This study not only provides a high-precision and efficient intelligent solution for residual strength assessment of damaged hulls, but its systematic neural network parameter optimization strategy, particularly the approach of identifying optimal depth and selecting anti-overfitting algorithms, also offers important reference for the design of intelligent damage assessment models for similar engineering structures.

Abstract: Coastal socio‑ecological systems are increasingly exposed to the combined pressures of climate change, land‑use intensification, hydrological alterations and expanding infrastructure networks. These pressures interact across the land–catchment–lagoon–sea continuum, generating complex feedbacks that challenge traditional planning instruments, which remain sectoral and fragmented. The Mar Menor (SE Spain), a semi‑enclosed Mediterranean lagoon affected by intensive agriculture, urbanisation, hydrological modifications and recurrent extreme climatic events, exemplifies this systemic vulnerability. Existing planning frameworks—local urban plans, regional territorial plans, river basin management plans, maritime spatial plans and lagoon‑specific strategies—operate independently, each addressing only a fragment of the system and none integrating climate change as a structuring axis. This article introduces Integrated Spatial Planning (ISP) as a novel territorial–climatic framework designed to overcome these limitations. ISP integrates climate forcing, land uses, catchment processes, lagoon dynamics, marine conditions, critical infrastructures, intermodal and energy corridors and multilevel governance into a single analytical structure. A central component of the methodology is a four‑zone multilevel zoning system that connects municipal, regional, basin, marine and EEZ planning domains within a unified territorial–climatic logic. The ISP matrix is applied to the Mar Menor to produce the first holistic diagnosis of the system. Results reveal strong land–sea–catchment interactions, high climatic exposure, vulnerable infrastructures and structural governance fragmentation. The matrix exposes systemic incompatibilities and vulnerabilities that remain invisible in sectoral planning instruments. The discussion demonstrates how ISP clarifies the roles and responsibilities of each governance level, supports multilevel coherence and integrates critical infrastructures and intermodal corridors into climate‑resilient planning. ISP reframes climate change as the organising principle of territorial planning and provides a replicable, scalable methodology for coastal socio‑ecological systems facing accelerating climate pressures. The Mar Menor case illustrates the urgent need for integrated territorial–climatic governance and positions ISP as a scientifically robust and operationally viable pathway for long‑term adaptation and resilience.

Abstract: The basic aim of this research was to compare the experimentally evaluated flow field at the stern region of a hull form with large block coefficient with the respective numerical results. To this end, a Five-Hole Pitot tube was used to capture the wake flow at the stern region of a scaled model of a bulk carrier in the towing tank of the Laboratory for Ship and Marine Hydrodynamic (LSMH) of the National Technical University of Athens (NTUA). The measurements were carried out at three aft sections of the model, where large scale vortices are usually generated: the section at the propeller, a section ahead of it and another one under the transom stern. The model was towed at a speed of 1.214 m/s, corresponding to Fn =0.17. The tube was calibrated on air at an equivalent Re, while a second in-house calibration technique was developed to consider installation misalignments and to increase overall measurement accuracy. The numerical calculation of the flow was performed using CFD tools developed at LSMH of NTUA. The method solves the RANS equations by applying the finite volume approach underneath a prescribed free surface which is derived by a potential flow code. The numerical results are in good agreement with the experimental ones, confirming the robustness of both methods.

Abstract: Field measurements of ship motions at berth are often sparse, heterogeneous, and collected across multiple vessels and locations, limiting the applicability of conventional multivariate response-modelling approaches. This study presents a statistical framework for analysing sea-state-conditioned motion responses of berthed ships using hourly field data from multiple vessels and berth locations with incomplete overlap between Degrees of Freedom (DoF). Each motion DoF is analysed independently and conditioned on the corresponding sea-state parameters, primarily significant wave height (Hs), peak wave period (Tp), and wave direction. A quality-control procedure that combines physical plausibility checks and robust regression is used to identify and remove inconsistent response–sea-state pairs while preserving the dominant response structure. Sea-state-conditioned motion response envelopes are derived by binning observations in sea-state space and computing representative and conservative statistics, including the median and upper-percentile responses. The results show a consistent increase in motion variability with increasing across all DoFs. Quantitative envelope metrics reveal that surge exhibits the strongest translational sensitivity to wave height, while roll displays the largest normalised motion coefficient, indicating strong amplification relative to wave height. Rotational motions, particularly roll and yaw, exhibit the largest envelope spreads and strongest directional dependence, whereas heave shows comparatively compact and monotonic behaviour consistent with direct wave excitation. Quadratic envelope fits further indicate that motion responses are not purely linear in Hs, with roll, yaw, and surge exhibiting clear superlinear growth in typical response levels. In contrast, extreme responses to heave and sway exhibit greater curvature in the upper-percentile envelopes. To support physical interpretation, synthetic sea surface elevations are generated for representative quality-controlled sea states using a spectral random-phase approach. Validation confirms that the generated sea states reproduce the prescribed spectral characteristics and statistical wave parameters, providing realistic time-domain representations of the sea surface. An ablation study further demonstrates the robustness of the proposed framework by quantifying the effect of individual methodological components, showing that the quality-control and sea-state conditioning stages are essential for reducing response dispersion and obtaining stable motion envelopes. Overall, the proposed methodology preserves most available field data, avoids restrictive assumptions about DoF simultaneity, and provides a transparent framework for extracting engineering-relevant response characterisation from heterogeneous berth-monitoring datasets. The approach offers a practical basis for assessing berth operability and evaluating motion risk under real-world sea conditions.

Abstract: To address the accuracy divergence problem of the integrated navigation system caused by drilling slippage and mismatch between the tail cable encoder and the robot's motion when a seafloor drilling robot operates in deep-sea soft sedimentary layers, this paper proposes a robust navigation method based on robust square root ductile Kalman filter (RSRCKF). Considering the large deformation mechanical characteristics of the seabed under drilling conditions, a unified state-space model including the time-varying odometer scaling factor error is first established. To solve the numerical instability of the nonlinear system under non-Gaussian noise interference, the square root ductile Kalman filter (SRCKF) framework is introduced, and the positive definiteness of the error covariance matrix is dynamically maintained using QR decomposition. Based on this, an online fault detection mechanism based on the novel chi-square test is designed, and an adaptive variance expansion factor is constructed by combining a two-segment IGG weight function to realize the real-time identification and weight reduction processing of abnormal observations caused by slippage. Field drilling and turning tests on the mudflats off the coast of Zhoushan show that, under typical soft clay slippage conditions, this method can effectively identify "false displacement" interference. Compared with the traditional EKF and standard SRCKF, the position error is reduced by approximately 82.4%, and the heading angle error is controlled within±0.5∘Within a certain range, the high robustness and engineering practicality of the algorithm under complex seabed topography were verified.

Abstract: In the marine navigation environment, static obstacles such as shallow waters, islands, and restricted zones coexist with dynamic threats like typhoons. Rapidly planning safe, shortest routes is crucial for ensuring vessel and personnel safety while enhancing navigation efficiency. However, existing path planning algorithms face limitations when addressing dynamic threats like typhoons, struggling to achieve an effective balance between efficiency and effectiveness. To address this, this study proposes an improved Time-Dynamic Theta algorithm (TDM-Theta*) based on the Theta algorithm. By incorporating wave height as a key constraint, it comprehensively evaluates the actual impact of dynamic marine environments on routes, thereby efficiently planning safe, shortest paths that proactively avoid typhoon impacts. Through testing and analysis of eight case studies across three typical scenarios, this algorithm demonstrates high efficiency and effectiveness in planning the shortest safe routes across diverse operational environments. The research findings provide theoretical foundations and methodological support for intelligent planning of safe vessel routes.

Abstract: This study investigates the factors contributing to the degradation of spirally wound armored steel wires used to protect core-structured unarmored optical-fiber submarine cables, driven by coupled multi-factor corrosion mechanisms in marine environments. It also assesses the influence of physical properties of deep-sea sediments on the durability of unarmored cables. The objective is to establish a scientific basis for cable longevity by integrating theoretical insights with empirical evidence. Although the steel utilized in armor is cost-effective and durable, it remains vulnerable to corrosion. Since the inaugural practical deployment of submarine communication cables between the United Kingdom and France in the 1850s, only a limited number of studies worldwide have examined the armor's corrosion and durability. Furthermore, there is limited literature on the physical characteristics of deep-sea surface sediments that directly affect the service life of the mechanically fragile polyethylene sheath. An in-depth analysis of cable damage and environmental conditions observed during maintenance operations offers valuable insights into the primary environmental factors influencing armor corrosion behavior and cable longevity. This research aims to provide essential guidelines for future cable system design and to support the development of effective strategies to enhance the sustainability and durability of cable systems operating in marine environments.

Abstract: The increasing complexity of contemporary ship design, driven by multidisciplinary integration, dense spatial constraints, and stringent regulatory frameworks, poses significant limitations to traditional Computer-Aided Design (CAD)-based engineering workflows. While Artificial Intelligence (AI) techniques have been applied to isolated marine optimization problems, their systematic and governance-compliant integration into regulated CAD environments remains underdeveloped. This paper proposes a governance-aware methodological framework for the integration of Cognitive AI into marine CAD systems. The framework defines a layered architecture that combines structured data management, engineering corpus modeling, hybrid reasoning mechanisms (rule-based systems, machine learning models, and multi-objective optimization), and real-time CAD interaction. A human-in-the-loop cognitive cycle is embedded to ensure traceability, regulatory compliance, decision transparency, and professional accountability. To quantitatively assess engineering impact, a normalized performance evaluation model is introduced, incorporating indicators for design cycle time reduction, iteration convergence, compliance enhancement, and rework minimization. The framework is validated through a scenario-based application to pipe routing, demonstrating its analytical consistency and integration feasibility within operational design workflows. The proposed methodology establishes a reproducible and certification-aligned foundation for AI-augmented ship design, contributing to the structured digital transformation of Shipyard 4.0 environments.

Abstract: The paper presents an analysis of the risk of failure of port structures in a modern seaport due to vessel impacts. The analysis addresses potential damage related to port maneuvers of self-maneuvering vessels and possible risk reduction options that can be applied to enhance port resilience. The proposed system model—including ship, port infrastructure and environment—enabled the observation of both implemented and anticipated future risk reduction measures. The analysis was carried out using the ferry terminal in the large Polish Port of Gdynia as a case study. A Bayesian influence diagram—including decisions related to the implementation of risk reduction options—was used to determine the total risk associated with ro-pax ferry port calls. Sustainable risk management led to the implementation of a cloud-based monitoring system and, subsequently, to the design of a new terminal in line with the green port concept. A comparative risk assessment for the two locations demonstrated improved safety and reduced environmental pollution in the new Public Ferry Terminal, primarily due to reduced spatial risk and the implementation of cold-ironing technology in the new terminal. The potential future implementation of an automated mooring system was also discussed.

Abstract: This study presents a large-scale empirical comparison of operational efficiency metrics derived from the IMO Data Collection System (DCS) and the EU Monitoring, Reporting and Verification (MRV) framework. Using a matched dataset of 15,755 dual-reported vessels and over 50,000 ship-year observations from 2019 to 2024, paired non-parametric tests, effect size estimation, and agreement diagnostics were applied to assess consistency across monitoring systems. Results indicate that although statistically significant differences are detected (p < 0.001), practical differences are negligible (Cohen’s d < 0.025), with MRV-based values averaging approximately 1.4% lower Annual Efficiency Ratio (AER) and fuel intensity than DCS values. Distributional analysis confirms substantial overlap between datasets, and temporal trends show progressive convergence following the implementation of the Carbon Intensity Indicator (CII) regulation. However, pronounced vessel-type heterogeneity is observed. Flexible cargo vessels exhibit consistent efficiency improvements in EU-related voyages, whereas container ships show minimal variation and LNG carriers demonstrate indicator-dependent patterns. Overall, the findings indicate that DCS and MRV provide broadly comparable representations of operational efficiency, with observed differences primarily reflecting vessel-type-specific operational characteristics rather than structural inconsistencies in reporting systems. The study contributes a scalable statistical validation framework for cross-regulatory monitoring assessment.

Abstract: The motion prediction of semi-submersible platforms is of significant importance for improving operational efficiency, ensuring platform safety, and providing early warning information for potential risks. Traditional prediction methods, such as those based on hydrodynamic simulations combined with Kalman filters, often face limitations due to their reliance on precise hydrodynamic parameters, which are difficult to obtain in practice. More recently, data-driven approaches, particularly deep learning models like Long Short-Term Memory (LSTM) networks, have shown promise in predicting complex motions. However, these methods often treat the prediction process as a “black box,” leading to issues such as lack of generalization ability, overfitting, and an inability to quantify the uncertainty of prediction results. To address these challenges, this paper proposes a novel motion prediction method for semi-submersible platforms based on a Bayesian neural network (BNN). The BNN incorporates Bayesian inference to effectively integrate prior knowledge and measured data, thereby quantifying uncertainties and improving prediction accuracy. The method is validated using field-measured motion data from a semi-submersible platform in the South China Sea. Compared with LSTM networks, the BNN demonstrates superior anti-noise performance and prediction accuracy, achieving an accuracy rate of up to 91.5%. Moreover, over 92% of the true values are captured within the 95% confidence interval of the prediction results. This study highlights the potential of BNNs for real-time motion prediction of offshore platforms, providing valuable support for early warning systems and operational decision-making.