Abstract: The development of autonomous cargo ships necessitates reliable anchoring operations, a critical challenge due to low-speed maneuverability issues and anchorage disturbances. This paper addresses the challenges of reduced maneuverability at low speeds and susceptibility to anchorage disturbances in autonomous cargo ships, conducting research on anchoring decision-making methods. The process was systematically analyzed. Safety anchorage areas were quantified using ship parameters and environmental data. An available anchor position identification method based on grid theory, combined with an anchorage allocation mechanism to derive optimal anchorage selection. The development of a multi-level guided anchoring trajectory planning algorithm was informed by practical anchoring. This algorithm was designed to facilitate the scientific calculation of turning and stopping guidance points, with the objective of guiding cargo ship to navigate towards the anchorage in a predetermined attitude. An integrated autonomous anchoring system was constructed, encompassing perception, decision-making, planning, and control. Digital simulations verified the system's effectiveness and robustness under complex sea conditions. This study provides a theoretical foundation and feasible technical approach for enhancing the autonomous decision-making and safety control capabilities of intelligent ships during anchoring operations.

Abstract: Nowadays, decarbonization of the shipping industry has become the top priority of the maritime community. In an effort to reduce emissions from shipping, numerous tech-nological and design solutions are being investigated; Waste Heat Recovery (WHR) by marine engines is one of the most important and widespread ones. This paper investi-gates the utilization of a carbon dioxide Supercritical Brayton Cycle (SBC) for WHR of a LNG carrier. SBC is an innovative, promising technology for power generation with unprecedented performance and a small form factor, due to the properties of the working fluid. A thermodynamic model is developed and programmed in MATLAB using the CoolProp free library. By means of this model, the performance of simple and recuperated SBC (RSBC) for WHR of a specific marine engine at its full load operation is assessed and the optimum compressor pressure ratio for power maximization of the RSBC is selected. The combined system Diesel-RSBC exhibits an increase of about 2.9% in thermal efficiency and a similar reduction in specific fuel oil consumption, com-pared to the sole power production by the Diesel engine, at its full load operation. Sig-nificant performance benefits are also demonstrated at part-load operation of the main engine. To assess how the benefits scale with the main engine power, seven similar marine engines of different power are considered, revealing a possible relationship between the optimal pressure ratio and SBC efficiency with the engine’s exhaust gas temperature.

Abstract: This paper presents the development and validation of a 3D CFD model of a heave plate under forced oscillations using a Lattice-Boltzmann, LES software, which has never been used for industrial applications in this context. The main objective of the model is to be versatile enough to maintain accuracy in extreme cases of amplitudes and frequencies. The validation is carried out with experimental results from previous research, with some results also compared with the ones obtained using a finite-volume software. A lattice and time step convergence is achieved along with a symmetry study. Once the optimal model has been selected it is tested under 4 extreme cases, analyzing the results yielded for the force, added mass and damping coefficients and also assessing its limitations. Results show good correlation between the model and the experimentation, especially in cases of higher force values, and also with the results from the finite-volume software. Further-more, a vorticity field study will be carried out to better understand the behavior of the heave plate in these extreme cases. Finally, an assessment of the dominance of pres-sure-induced forces over viscous forces under low KC numbers is carried out using radial and surface integration.

Abstract: Against the backdrop of the global energy transition, the efficient exploitation of marine renewable energy has become a key pathway toward carbon neutrality. Wind–wave hybrid systems (WWHSs) have attracted increasing attention due to their resource complementarity, efficient spatial utilization, and shared infrastructure. However, most existing studies focus on single components or local optimization. A systematic integration of the full technology chain remains limited, hindering the transition from demonstration projects to commercial deployment. This review provides a comprehensive overview of the technological evolution and key characteristics of offshore wind turbine (OWT) foundations and wave energy converters (WECs). Fixed-bottom foundations remain the mainstream solution for near-shore development. Floating offshore wind turbines (FOWTs) represent the core direction for deep-sea deployment. Among WEC technologies, oscillating buoy (OB) WECs are the dominant research pathway. Yet high costs and poor performance under extreme sea states remain major barriers to commercialization. On this basis, the paper summarizes three major integration modes of WWHSs. Among them, hybrid configurations have become the research focus due to their structural sharing, hydrodynamic coupling, and significant cost and energy synergies. Furthermore, the review synthesizes optimization strategies for both technology design and spatial layout, aiming to enhance energy capture, structural stability, and overall economic performance. Finally, the paper critically identifies current research gaps and bottlenecks, and outlines key technological pathways required for future commercial viability. These include the development of high-performance adaptive power take-off (PTO) systems, deeper understanding of multi-physics coupling mechanisms, intelligent operation and maintenance enabled by digital twins, and comprehensive life-cycle techno-economic and environmental assessments. This review aims to provide a systematic reference for the advancement of multi-energy offshore systems and to support future integrated energy development in deep-sea environments.

Abstract: Time-domain fatigue analysis of floating offshore wind turbines (FOWTs) is accurate but often prohibitive for early-stage design. The Unit Load Response (ULR) method, based on linear superposition, offers an efficient alternative, but its application to large, shell-based structures with complex distributed loads remains a challenge. We propose a workflow that integrates ULRs with force-based submodelling to enable whole-structure fatigue screening at design cost. Two key innovations make it practical: (i) A "Virtual Test Rig" is used to create a computationally fast, stiffness-equivalent simplified global model for extracting boundary loads. (ii) A ULR catalogue is generated for a detailed local submodel, which includes a trilinear interpolation scheme (with water height, pitch, roll) to efficiently handle complex, wave pressure fields. The workflow is first verified on a canonical portal frame and then applied to a full-scale semisubmersible FOWT. Across 14 critical locations, the reconstructed stress time histories match the submodel with a median bias ≈ of approximately −3.8% to −4.9%, and the stress and fatigue rankings are preserved, with Kendall’s τ-a ≥ 0.7 at stress concentrations and τ-a ≥ 0.8 overall. Compared to classic step-by-step submodelling, the method achieves ~13-29 times lower wall-clock effort and produces outputs that are otherwise impractical at scale (e.g., full-hull damage maps), enabling earlier, more informed fatigue-driven design decisions.

Abstract: In view of the requirements and characteristics that a deep-sea polymetallic nodule collector must travel according to a planned path and speed during operation, a collector trajectory tracking system scheme based on virtual target vehicle following is proposed. In this system, the virtual target vehicle travels according to the planned path and speed, thereby generating a dynamic target path and speed. A fuzzy controller calculates the collector’s angular-velocity command based on the lateral position deviation and the heading-angle deviation between the collector and the target vehicle, and a proportional controller calculates the collector’s body linear velocity control command based on the longitudinal position deviation between the collector and the target vehicle. By integrating these two commands, the collector follows the target vehicle and thereby realizes trajectory tracking of the planned path and speed. A tracking system is designed and simulation studies are carried out. The results show that the designed system enables the collector to follow the planned path and speed well under operational conditions. The trajectory tracking method based on virtual target vehicle following can also form an organic integration of path planning and trajectory tracking, generate dynamic planned paths and speeds for the entire mining area, and realize tracking travel of the collector along the planned path and speed throughout the whole operation.

Abstract: Deep-sea cages are highly susceptible to biofouling due to long-term seawater immersion, which promotes the attachment and growth of marine organisms on nets, significantly reducing fish survival. To address this issue, this study explores the use of low-pressure abrasive-water jets (LPAWJ) for cage fouling removal through numerical simulation. Based on a Box-Behnken response surface design, nozzle inlet pressure X1, nozzle outlet diameter X2, and target distance X3 were selected as optimization parameters. The peak jet impact force Z1, stable jet impact force Z2, peak abrasive-water jet velocity Z3, and peak abrasive particle velocity Z4 were chosen as evaluation indicators to characterize the jet’s instantaneous impact ability, sustained action ability, and dynamic particle behavior. Using the entropy method, weights for each indicator were determined, and the jet’s overall removal capability was calculated. A regression model was developed by integrating numerical simulation with the response surface methodology (RSM), and the optimal parameter combination was identified as X1 = 4.5 MPa, X2 = 10 mm, and X3 = 205.396 mm. Compared with the poorest experimental condition (Condition 1), the jet’s overall removal capability obtained under the optimal parameter combination increases by 101.35%. Experimental validation further confirms that the optimized parameters yield the best oyster-removal performance of the low-pressure abrasive jet, with the average removal rate improving by 100.55% relative to Condition 1. The methodology and results of this study provide a theoretical foundation and technical reference for the design and optimization of automated net-cleaning systems or net-cleaning robots equipped with low-pressure abrasive jets. By integrating the proposed model and operating parameters, future robotic systems will be able to predict and dynamically adjust jet conditions according to fouling characteristics, thereby improving the efficiency, cost-effectiveness, and sustainability of maintenance operations in marine aquaculture.

Abstract: Marine Spatial Planning (MSP) and Terrestrial Spatial Planning (TSP) have traditionally operated as separate systems, resulting in fragmented governance of coastal territories. This article introduces Integrated Spatial Planning (ISP), a methodology that unifies MSP and TSP through a multi‑level zoning framework spanning municipal, regional, national, and international scales. ISP embeds climate change adaptation into planning instruments, ensuring resilience and sustainability in land–sea interactions. The approach is applied to the Region of Murcia (Spain), where numerous instru-ments—urban development plans, the Regional Land and Urban Planning Law, the Territorial Strategy, the Mar Menor Integrated Management Plan, the Regional Climate Change Strategy, the Segura River Basin Hydrological Plan, and Coastal Adaptation Guidelines —exist but remain fragmented. The case study highlights gaps in runoff management, infrastructure coherence, renewable energy planning, and climate adap-tation. ISP addresses these by creating integrated governance mechanisms, enhancing ecosystem protection, socio‑economic development, and adaptive coastal management.

Abstract: Underwater Vehicle-Manipulator Systems (UVMS) play a critical role in various marine operations, where the choice of manipulator architecture significantly influences system performance. While serial robotic arms have been widely adopted in UVMS applications due to their operational flexibility, their inherent structural characteristics present certain challenges in underwater environments. These challenges primarily stem from the cu-mulative effects of joint mechanisms and dynamic interactions with the fluid medium. In this context, we explore an innovative UVMS solution that incorporates the Delta parallel mechanism, which offers distinct advantages through its symmetrical architecture and unilateral motor configuration, particularly in maintaining operational stability. We de-velop a comprehensive framework that includes mechanical design optimization, imple-mentation of distributed control systems, and formulation of closed-form kinematic mod-els, with comparative analysis against conventional serial robotic arms. Experimental validation demonstrates the system's effectiveness in underwater navigation, target ac-quisition, and object manipulation under operator-guided control. The results reveal sub-stantial enhancements in motion consistency and gravitational stability compared to tra-ditional serial-arm configurations, positioning the Delta-based UVMS as a viable solution for complex underwater manipulation tasks. Furthermore, this study provides a compar-ative analysis of the proposed Delta-based UVMS and conventional serial-arm systems, offering valuable design insights and performance benchmarks to inform future develop-ment and optimization of underwater manipulation technologies.

Abstract: Unmanned underwater vehicles (UUVS) capable of agile, high-speed maneuvering in complex environments require propulsion systems that can dynamically modulate three-dimensional forces. The California sea lion (Zalophus californianus) provides an exceptional biological model, using its foreflippers to achieve rapid turns and powerful propulsion. However, the specific kinematic mechanisms that govern instantaneous force generation from its powerful foreflippers remain poorly quantified. This study experimentally characterizes the time-varying thrust and lift produced by a bio-robotic sea lion foreflipper to determine how flipper twist, sweep, and phase overlap modulate propulsive forces. A three-degree-of-freedom bio-robotic flipper with a simplified, low-aspect-ratio planform and single compliant hinge was tested in a circulating flow tank, executing parameterized power and paddle strokes in both isolated and combined-phase trials. The time-resolved force data reveal that the propulsive stroke functions as a tunable hybrid system. The power phase acts as a force-vectoring mechanism, where the flipper’s twist angle reorients the resultant vector: thrust is maximized in a broad, robust range peaking near 45°, while lift increases monotonically to 90°. The paddle phase operates as a flow-insensitive, geometrically driven thruster, where twist angle (0° optimal) regulates thrust by altering the presented surface area. In the full stroke, temporal phase overlap governs thrust augmentation, while power-phase twist provides robust steering control. Within the tested inertial flow regime (Re ≈ 10⁴–10⁵), this control map is highly consistent with propulsion dominated by geometric momentum redirection and impulse timing, rather than circulation-based lift. These findings establish a practical, experimentally derived control map linking kinematic inputs to propulsive force vectors, providing a foundation for the design and control of agile, bio-inspired underwater vehicles.

Abstract: The intelligent decision-making systems of Autonomous Underwater Vehicle (AUV) are undergoing a significant transformation, shifting from traditional control theories to data-driven paradigms. Deep learning (DL) serves as the primary driving force behind this evolution; however, its application in complex and unstructured underwater en-vironments continues to present unique challenges. To systematically analyze the de-velopment, current obstacles, and future directions of DL-enhanced AUV deci-sion-making systems, this paper proposes an innovative "four-module" decomposition framework, which are information processing module, information understanding module, information judgment module and output module. This framework enables a structured review of the progression of deep learning technologies across each stage of the AUV decision-making information flow. To further bridge the gap between theo-retical advancements and practical implementation, we introduce a task complexity–environment uncertainty four-quadrant analytical matrix, offering strategic guidance for selecting appropriate DL architectures across diverse operational scenarios. Addi-tionally, this work identifies key challenges in the field as well as anticipates future developments to solve these challenges.This paper aims to provide researchers and engineers with a comprehensive and strategic overview to support the design and op-timization of next-generation AUV decision-making architectures.

Abstract: Sargassum seaweed beaching events in the Northern Brazilian coast pose environ-mental and economic challenges. Understanding these occurrences is essential for preparing effective management strategies. This study systematically reviews scien-tific articles — screening 2,821 records and assessing 17 full texts — to identify gaps in research and practice, focusing on the impacts, management, and potential uses of pelagic Sargassum biomass. The review identified significant gaps in existing infra-structure and public policy to manage future seaweed influxes effectively. Although innovative approaches—such as the use of Sargassum biomass in sustainable con-struction materials — have been reported as potential strategies, their implementa-tion remains incipient. However, further development of local processing facilities and regulatory frameworks is crucial to reduce logistical challenges, support local economies, and minimize environmental impacts. This study underscores the urgent need for integrated strategies combining infrastructure investments, technological innovation, and policy reforms to address the socio-environmental challenges posed by Sargassum and harness its potential as a valuable resource.

Abstract: Offshore wind farms (OWFs) represent an increasingly important and strategically growing renewable energy source. However, their environmental impacts, particularly noise emissions, require further systematic study. Estimating the operational source level (SL) of a single turbine is challenging, and implementing open-source propagation models to predict sound pressure levels (SPL) at vulnerable locations can be tedious. In this study, we integrate a state-of-the-art turbine operational SL prediction algorithm with open-source propagation models in a Jupyter Notebook to streamline cumulative SPL estimation for OWFs. We also incorporate species-specific audiograms and weighting functions to assess the potential biological impacts of received noise levels. The developed tool is applied to four planned OWFs, two in the Canary region and two in the Belgian and German North Seas, under conservative assumptions. Results indicate that at 10 m/s wind speed single turbine’s operational SL reaches 143 dB re 1 µPa in the one-third octave band centered at 160 Hz. Propagation varies notably with bathymetric and seabed character-istics, with maximum SPLs of 112 dB re 1 µPa at 160 Hz within OWFs (exceeding heavy marine traffic noise levels from generic ambient-noise curves), decreasing in some cases to 50 dB re 1 µPa at ~100 km. Weighted SPL against audiograms analyses show that within OWFs, Phocid Carnivores in Water (PCW) and Low-Frequency (LF) cetacean hearing groups are likely to be affected, while outside the farms, only LF groups are impacted.

Abstract: Offshore wind energy is a key enabler of the global net-zero transition. As nearshore fixed-bottom projects reach maturity, floating offshore wind turbines (FOWTs) are becoming the next major focus for large scale deployment. To accelerate this development and reduce construction costs, it is essential to optimize mooring systems through a systematic and performance driven framework. This study focuses on the mooring optimization of the Taiwan-developed DeltaFloat semi-submersible platform supporting a 15 MW turbine at a 70 m water depth offshore Hsinchu, Taiwan. A full chain catenary mooring system was designed based on site specific metocean conditions. The proposed framework integrates ANSYS AQWA and Orcina OrcaFlex simulations with sensitivity analyses and performance-based Fitness metrics including offset, inclination, and line tension to identify key parameters governing mooring behavior. Additionally, an analysis of variance (ANOVA) was conducted to quantitatively evaluate the statistical significance of each design parameter. Results indicate that mooring line length is the most influential factor affecting system performance, followed by line angle and diameter. Optimizing these parameters significantly improves platform stability and reduces tension loads without excessive material use. Building on the optimized symmetric configuration, an asymmetric mooring concept with unequal line lengths is proposed. The asymmetric layout achieves performance comparable to traditional 3×1 and 3×2 systems under extreme environmental conditions while demonstrating potential reductions in material use and overall cost. Nevertheless, the unbalanced load distribution highlights the need for multi scenario validation and fatigue assessment to ensure long-term reliability. Overall, the study establishes a comprehensive and sensitivity-based optimization framework for floating wind mooring systems. The findings provide a balanced and practical reference for the cost-efficient design of floating offshore wind farms in the Taiwan Strait and other shallow-water regions.

Abstract: This study presents an optimal bearing arrangement for the propulsion shafting system of ships equipped with multiple strut bearings, ensuring both structural stability and cost-effectiveness under shallow-draft conditions where the propeller must remain fully submerged. To this end, the shafting flexibility, alignment characteristics, and whirling vibration responses were analyzed for various bearing arrangements. The analysis results show that removing the stern tube bearing and supporting the shaft using only the Y-type and I-type strut bearings, with the bearing span adjusted so that the L/d ratio remains within 15 to 18, minimizes the reaction influence number, shaft bending moments, and variations in bearing loads. At this configuration, the first natural frequency corresponding to the propeller blade order is also more than 30 percent higher than the service speed, thereby avoiding resonance caused by transverse vibration. Accordingly, this study confirms that adjusting the layout of strut bearings can simultaneously enhance both the structural reliability and dynamic stability of the propulsion shafting system.

Abstract: The coastal environment is a dynamic system shaped by both natural processes and human activities. In recent decades, increasing anthropogenic pressure and climate change—manifested through sea-level rise and more frequent extreme events—have accelerated coastal retreat, highlighting the need for improved management strategies and standardized tools for coastal risk assessment. Existing approaches remain highly heterogeneous, differing in structure, input data, and the range of factors considered. To address this gap, this study proposes an index-based methodology of general validity designed to quantify coastal erosion risk through the combined analysis of hazard, vulnerability, and exposure factors. The approach was developed for multi-scale and multi-risk applications and implemented across 54 representative sites along the Calabrian coast in southern Italy, demonstrating strong adaptability and robustness for regional-scale assessments. Results reveal marked spatial variability in coastal risk, with the Tyrrhenian sector exhibiting the highest values due to the combined effects of energetic wave conditions and intense anthropogenic pressure. The proposed framework can be easily integrated into open-access GIS platforms to support evidence-based planning and decision-making, offering practical value for public administrations and stakeholders, and providing a flexible, accessible tool for integrated coastal risk management.

Abstract: This paper aims to provide a detailed insight into the wave loads induced by a dam break flow over a dry horizontal bed under well controlled laboratory conditions. The setup used is based on the one in [1], with the addition of rigid obstacles, that lead to three dimensional dynamics. The focus is on wave impact pressure, for which measurements at the downstream wall at three different locations placed at the same height but at different transverse positions have been recorded. Pressure peak maxima are identified and the pressure impulses are computed. A statistical analysis of the experimental data generated in the campaign is performed. Repetitions of each case have been carried out until convergence of the statistics is observed. Comparisons between the configurations studied and against previous experimental campaigns, involving two-dimensional dam breaks, are provided, highlighting the deviations introduced by the obstacles. The data can be used to validate and calibrate numerical solvers aimed at computed wave induced structural loads.

Abstract: Swift and accurate semantic segmentation of underwater images is key for precise object recognition in complex underwater environment. Nonetheless, the inherent complexity of these environments and the limited availability of labeled data pose significant challenges to underwater image segmentation. Traditional deep learning methods struggle to cope with limited and noisy annotations. In this paper, we delineate the formulation of a novel semi-supervised paradigm with dynamic mutual adversarial training for the semantic segmentation of underwater images. This paradigm identifies the sources of inaccuracies in pseudo-labeling by analyzing different confidence maps, generated by models with unique prior knowledge. A dynamic reweighing loss function is then employed to orchestrate the mutual instruction of two divergent models. Furthermore, the delineation of confidence map is facilitated via adversarial networks, which involves simultaneous adversarial refinement of the discrimination network and the segmentation model, using the predictions with high-confidence maps as pseudo-labels. Experimental results on public underwater datasets verify that the proposed method can effectively improve semantic segmentation performance under the condition of a small amount of labeled data.

Abstract: Suction anchors are widely used in marine engineering because of its easy installation, cost-effectiveness, and excellent load-bearing capacity. However, existing research on its bearing capacity has primarily focused on homogeneous soils, which fails to adequately reflect the actual bearing capacity of layered seabed soils. Therefore, this study conducted a series of numerical simulations to investigate the pullout bearing capacity of suction anchors subjected to inclined loads in upper- stiff-lower-soft layered clay. By considering the clay strength (Sum/kD) and soil layer thickness ratio (Th/L, Tc/L), this study systematically explores influence on the optimal centerline loading depth (Zcl,opt), uniaxial ultimate bearing capacity (Hult and Vult), and the VH failure envelope of suction anchors. The results indicate that the layer thickness ratio Th/L of lightly overconsolidated clay (LOC) is the key factor influencing the Zcl,opt and ultimate bearing capacity Hult and Vult. An increase in Th/L significantly enhances the pullout resistance of suction anchors, which primarily results from the combined enhancement effect of lateral friction resistance and end resistance at the anchor-soil interface. The layered clay has a distinct influence on the horizontal and vertical bearing capacities of suction anchors. Based on the results of parameter analysis, a conservative analytical expression for the lower bound of the VH failure envelope curve is further proposed. The research conclusions provide theoretical basis and engineering practice guidance for the optimized design and safety assessment of suction anchors in layered soil.

Abstract: Autonomous navigation is critical for unlocking the full potential of Unmanned Surface Vehicles (USVs) in complex maritime environments. Deep Reinforcement Learning (DRL) has emerged as a powerful paradigm for developing self-learning control policies, yet the design of reward functions to balance conflicting objectives, particularly fast arrival at the target position and collision avoidance, remains a major challenge. The precise, quantitative impact of reward parameterization on a USV's maneuvering behavior and the inherent performance trade-offs have not been thoroughly investigated. Here we demonstrate that by systematically varying reward function weights within a framework relying on the Proximal Policy Optimization (PPO), it is possible to quantitatively map the trade-off between collision avoidance safety and mission time. Our results, derived from simulations, show that agents trained with balanced reward weights achieve target-reaching success rates exceeding 98\% in dynamic multi-obstacle scenarios. Conversely, configurations that disproportionately penalize obstacle proximity lead to overly cautious behavior and mission failure, with success rates dropping to 22\% due to workspace boundary violations. This work provides a data-driven methodological framework for reward function design and parameter selection in safety-critical robotic applications, moving beyond ad-hoc tuning towards a more structured parameter influence analysis.