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.

Abstract: A comprehensive review of Dual Gradient Drilling (DGD) and Riserless Mud Recovery (RMR) technology was conducted. Deepwater and ultra-deepwater drilling operations were confronted with significant challenges, primarily characterized by narrow formation pressure windows and the physical limitations of conventional riser systems. DGD was proposed as a theoretical framework to address these issues by fundamentally reshaping the wellbore pressure profile. The RMR system, recognized as a mature and commercially successful implementation of the DGD concept, enabled closed-loop recovery of drilling fluid and precise pressure control without employing a conventional riser. This paper systematically reviewed the principles, historical development, global applications, and future challenges associated with this technology. First, the core mechanism of DGD was elucidated, which involved establishing a "virtual wellhead" on the seabed to achieve segmented pressure control, along with the technical architecture and operational workflow of the RMR system. Second, the technological evolution was traced, from its initial conceptualization and subsequent joint industry research and development to its commercialization and expansion into deepwater operations. The review summarized the application outcomes in major global deepwater basins, highlighting its effectiveness in mitigating shallow geohazards, optimizing wellbore architecture, and meeting stringent environmental regulations. Furthermore, the challenges faced during the technology's advancement towards ultra-deepwater environments and intelligent development were analyzed, including issues pertaining to critical equipment reliability, intelligent control systems, adaptability to extreme environments, and cost-effectiveness. Finally, its potential application in emerging fields such as scientific ocean drilling and natural gas hydrate exploitation was explored. This review aimed to provide a systematic reference to support the advancement of deepwater drilling technologies.

Abstract: The structural design of unstiffened cylindrical shells under external hydrostatic pressure is critical forthe safety of marine structures, such as submarine hulls and pressure vessels. Accurately assessing nonlinear buckling and collapse failure modes traditionally requires computationally intensive Finite Element Analysis (FEA), which creates a bottleneck in iterative design optimization. To address this, our research leverages a robust Deep Neural Network (DNN) model developed in a preceding study. This predictive model was trained on a large‐scale dataset of 46,060 points generated through FEA simulations and rigorously validated against 28 physical experimental data points. Building upon this foundation, the present study implements a novel optimization framework that integrates the pre‐trained DNN as a high‐speed surrogate model with a Differential Evolution (DE) algorithm for global optimization. The primary objective is to minimize structural weight while strictly satisfying collapse strength requirements. Additionally, a grid search component is incorporated to provide designers with multiple feasible design candidates almost instantaneously. Validation against independent FEA results confirms high fidelity, with error rates of less than 2%. This methodology transforms the design cycle from days to mere minutes, establishing a reusable digital asset that significantly enhances efficiency and structural safety in marine engineering.

Abstract: The escalating development of Maritime Autonomous Surface Ships (MASS) has imposed rigorous demands on the precision, continuity, and resilience of onboard integrated navigation systems. However, in complicated marine settings, data from the Global Navigation Satellite System (GNSS) and Doppler Velocity Log (DVL) are frequently corrupted by multipath effects and non-line-of-sight (NLOS) interference. These disturbances introduce anomalous observations that violate Gaussian noise assumptions, thereby severely deteriorating the robustness and estimation quality of traditional sliding-window factor graph optimization (SW-FGO). To mitigate this problem, this study introduces a novel integrated navigation strategy termed Gradient-Adaptive Factor Graph Optimization (GA-FGO). By designing a gradient-adaptive robust objective function within the factor graph structure, the proposed method dynamically re-weights constraints from the Inertial Navigation System (INS), GNSS, and DVL. This mechanism effectively attenuates the influence of measurement outliers at the optimization level. Furthermore, a unified solution framework utilizing Iterative Reweighted Least Squares (IRLS) and the Gauss–Newton method is established to simultaneously perform adaptive weight updates and state estimation. Validation based on offline field data—benchmarked against the Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and standard SW-FGO. Simulation results demonstrated that the GA-FGO algorithm achieves superior positioning accuracy and estimation stability under realistic measurement conditions.

Abstract: As global aquaculture continues to expand there is an increasing interest for sustainable and non-invasive tools to monitor fish growth. Nile tilapia (Oreochromis niloticus) is one of the most farmed species worldwide. Its biomass estimation often relies on manual sampling or stereo-cameras systems limited by water turbidity. This study establishes a robust relationship between lateral Target Strength (TS) and the total length (TL) and weight (W) of Nile tilapia using a cost-effective 201 kHz single-beam echosounder. Measurements were conducted with free-swimming fish in a controlled pond environment (TL range 13–44 cm). The results show a strong linear correlation between acoustic and biometric data. Specifically, the relationship for mean TS was defined as TS_mean = 20.1log(TL) − 66.7 (R2=0.91) and TS_mean = 6.3log(W) − 55.4 (R2=0.96), proving the system’s accuracy for biomass estimation. Furthermore, the Method of Fundamental Solutions (MFS) was employed for numerical validation based on X-ray morphometry of the swim bladder. A very good agreement was observed between experimental data and numerical simulations, reinforcing the validity of the acoustic models despite the inherent complexity of biological targets. These findings demonstrate that calibrated single-beam acoustic systems provide a viable, non-intrusive tool for real-time monitoring in aquaculture ponds.

Abstract: For many decades the performance of ships was optimized at their design speed and con-firmed by ship trials upon delivery to the shipowner, when the hull and all systems are in perfect condition. However, during operation both the underwater hull and the propeller are appended by biofouling and become progressively rough. Roughness constitutes a major resistance component superimposed on the frictional ship resistance and deteriorates its performance leading to increased power requirements and CO2 emissions to retain a specified speed or to a speed reduction at the same power delivered by the main engine ME. Sailing at reduced speed either due to adverse sea conditions, when the excessive dynamic responses of the ship compel the operator to reduce speed, or for fuel economy is a quite common situation, occurring for a major part or even the whole period of charter contracts to reduce the cost of transportation. In this paper the effect of roughness is investigated and the overall performance of a ship at reduced speeds is assessed directly affecting its techno-economic management. Currently the speed dependence of fouling resistance is evaluated only once at the beginning of the charter contract being valid throughout the whole charter period. The study uses full-scale records and CFD calculations available in the literature to demonstrate the effect of fouling in the overall performance of ships.

Abstract: The objective of this study was to synthesize silver nanoparticles (AgNPs) utilizing an eco-friendly approach with Ocimum lamiifolium leaf extract as a biological reducing agent. The research focused on investigating how various experimental conditions influenced the stability and particle size of the AgNPs. The characterization of the synthesized nanoparticles involved multiple techniques, including X-ray diffraction (XRD), scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), UV-visible spectroscopy, particle size analysis, Polydispersity Index (PDI), and zeta potential measurements. During the reduction process, a noticeable color change from colorless to grey indicated successful conversion of Ag+ to Ag°. The UV-vis spectra revealed a maximum absorption at 467 nm, confirming nanoparticle formation. The average particle size was found to be 65.37 nm with a PDI of 0.241, suggesting a relatively uniform size distribution. The zeta potential was measured at -29.85 mV, indicating good colloidal stability over time. FTIR analysis identified various functional groups associated with phytochemicals, supporting the role of plant compounds in reduction and stabilization. XRD patterns confirmed the face-centered cubic (FCC) crystalline structure of the AgNPs. Furthermore, antibacterial testing showed increased inhibitory zones with higher AgNP concentrations, with the minimum inhibitory zone of 4 mm and maximum of 15.45 mm against E. coli. The green synthesis mechanism involved reduction, stabilization, membrane disruption, reactive oxygen species (ROS) production, and bacterial cell death. Overall, AgNPs produced via Ocimum lamiifolium extract demonstrated enhanced stability and effective antibacterial activity, highlighting the potential of plant-based green synthesis methods for biomedical applications.

Abstract: Maritime shipping significantly contributes to air pollution in port cities, yet comprehensive emission inventories remain scarce for major ports in developing economies. This study presents the first bottom-up emission inventory for Ambarlı Port, Turkey’s largest container port, utilizing AIS data from Global Fishing Watch for calendar year 2025. Emissions of CO₂, NOx, SO₂, PM₁₀, PM_2.5, CO, and NMVOC were quantified using EMEP/EEAactivity-based methodology with IMO Tier II emission factors and vessel type-specific load factors (75% for passenger, 45% for cargo) from ENTEC guidelines. Non-commercial vessels (tugs, service craft, fishing vessels) and lay-up vessels exceeding six months continuous berthing were excluded to focus on active commercial shipping operations, resulting in a validated dataset of 10,267 port visits from commercial cargo, passenger, and bunker vessels. Annual emissions from active commercial vessels totaled 404,766 tonnes CO₂, 8,487 tonnes NO_x, 6,724 tonnes SO₂, 914 tonnes PM₁₀, and 849 tonnes PM_2.5. Passenger vessels dominated the inventory (93.3% of CO2) due to high auxiliary power demands for hotel services and elevated load factors, while cargo vessels contributed 6.5% despite representing 61.4% of port visits. Turkish-flagged vessels accounted for the majority of domestic ferry traffic. These findings provide baseline data for air quality management in the Istanbul metropolitan area and support policy development regarding shore power implementation, with particular emphasis on reducing emissions from passenger vessels with extended berth times.

Abstract: Autonomous underwater vehicles (AUVs) face significant challenges in complex dynamic marine environments, where ocean currents, uncertain obstacles, and in-ter-vehicle interactions increase collision and mission failure risks. This study proposes a risk-aware cooperative path planning framework for multiple AUVs that integrates conditional Bayesian networks (CBN) for probabilistic environmental risk assessment directly into a receding horizon optimization scheme. The approach models AUV kinematics under time-varying ocean currents, incorporates collision avoidance, en-ergy consumption, path smoothness, and dynamic risk constraints derived from CBN-inferred probabilities. Risk levels are mapped nonlinearly to enable gradi-ent-based optimization while maintaining continuous sensitivity. The framework is evaluated through Monte Carlo simulations in a realistic South China Sea canyon en-vironment using HYCOM reanalysis current data, with comparisons against baseline methods. Results demonstrate substantial improvements: mission success rate increas-es by up to 35%, energy consumption decreases by 12–18%, path smoothness improves, and risk exposure is significantly reduced across various current intensities and obsta-cle densities. This method enhances operational safety and efficiency for cooperative AUV missions in uncertain dynamic oceans, offering a promising engineering solution for real-world underwater applications. This work presents an engineering-oriented framework that embeds a CBN-derived probabilistic risk index into cooperative re-ceding-horizon trajectory optimization for multi-AUV systems operating under realis-tic, time-varying ocean current fields. The main contributions of this work are summarized as follows: (1) A risk-aware cooperative path planning framework is developed for mul-ti-AUV systems, in which a probabilistic environmental risk model based on a Conditional Bayesian Network (CBN) is directly embedded into a reced-ing-horizon optimization process, rather than used as a post hoc evaluation or external safety filter. (2) Unlike existing deterministic or purely reactive approaches, the proposed CBN-based risk inference mechanism enables the planner to explicitly reason about coupled terrain–current–uncertainty effects, providing a continuous risk gradient that cannot be obtained from binary obstacle representations. (3) The proposed receding-horizon cooperative optimization embeds probabilis-tic risk directly into the planning objective, allowing multi-AUV systems to proactively trade off efficiency and safety in a mathematically tractable manner, rather than relying on post hoc risk filtering. (4) The effectiveness and practical applicability of the proposed method are demonstrated through extensive Monte Carlo simulations in a realistic sub-marine canyon environment using reanalysis-based ocean current data, showing statistically consistent improvements in mission success rate, energy efficiency, trajectory smoothness, and reduction of high-risk exposure com-pared with a baseline cooperative planning strategy. The proposed framework provides a practical and scalable solution for real-world multi-AUV missions, with potential applications in marine environmental monitoring, seabed surveying, underwater inspection, and ocean engineering operations.

Abstract: Stiffened panels are essential structural elements that play a critical role in maintaining the integrity of engineering structures, particularly when subjected to torsional loading. Ensuring their adequate strength is therefore a fundamental requirement in design and assessment. Conventional approaches to strength evaluation using the finite element method (FEM) often face challenges due to the complexity of modeling stiffened geometries and the time-consuming setup required, which can reduce efficiency and limit accessibility for practical applications. To overcome these limitations, this study introduces the development of a graphical user interface (GUI) specifically designed to facilitate FEM-based strength analysis of stiffened panels under torsional loads. The GUI, implemented in Python, automates essential modeling steps, streamlines the input process, and enhances user interaction through an intuitive interface, thereby making torsional strength analysis more efficient and user-friendly. Numerical simulations were carried out on nine panel configurations, systematically combining three variations of plate thickness with three variations of longitudinal stiffener geometry. The results demonstrate that plate thickness has a direct influence on torsional resistance, with thicker plates exhibiting significantly higher strength, while stiffener design was also found to strongly affect performance: the 80 x 80 x 8 stiffener provided the greatest resistance against general torsional loading, whereas the 100 x 65 x 9 stiffener displayed superior behavior under pure torque conditions. These findings are consistent with theoretical predictions, confirming the reliability of the developed approach, and overall, the proposed GUI proves to be an effective tool in supporting FEM-based strength assessment of stiffened panels, offering both accuracy and efficiency while highlighting the potential of integrating computational modeling with user-oriented interfaces to broaden the applicability of FEM in structural engineering practice, particularly in analyzing complex torsional behaviors of stiffened panel systems.

Abstract: Suspended sediment concentration affects the erosion and deposition of estuaries and coastal zones, and affects channel construction and safety. Sediment settling velocity controls sediment transport and sedimentation processes, and is crucial for assessing sediment distribution, diffusion, and material transport. As an important means for the inversion study of sediment concentration in estuaries and coasts, remote sensing alone cannot establish a model of the nearshore suspended sediment concentra-tion field by inverting surface sediment. Based on the remote sensing inversion of surface sediment, this study, in combination with the vertical distribution calculation method of sediment concentration in estuaries, inversely deduced the sediment concentration patterns in the middle and bottom layers, and proposed a sediment settling velocity calculation formula considering turbulent shear and concentra-tion influence. The results show that the highest concentration of suspended sediment in the study area appears in the east of Guan River Estuary, which is characterized by a high concentration in the east and a low concentration in the west. At a low suspended sediment concentration, the settling velocity is positively correlated with the suspended sediment concentration. At a high suspended sediment con-centration, the two are negatively correlated. The method introduced in this study is simple and feasi-ble, and the results are stable and reliable. It can be effectively used to evaluate the suspended sediment concentration and sediment settling velocity in different research areas.

Abstract: Driven by global initiatives to mitigate climate change, the offshore wind power industry is experiencing rapid growth. Personnel transfer between service operation vessels (SOVs) and offshore wind turbines under complex sea conditions remains a critical factor governing the safety and efficiency of operation and maintenance (O&M) activities. This study establishes a fully coupled dynamic response and control simulation framework for an SOV equipped with an active motion-compensated gangway. A numerical model of the SOV is first developed using potential flow theory and frequency-domain multi-body hydrodynamics to predict realistic vessel motions, which serve as excitation inputs to a co-simulation environment (MATLAB/Simulink coupled with MSC Adams) representing the Stewart platform-based gangway. To address system nonlinearity and coupling, a composite control strategy integrating velocity and dynamic feedforward with three-loop PID feedback is proposed. Simulation results demonstrate that the composite strategy achieves an average disturbance isolation degree of 21.81 dB, significantly outperforming traditional PID control. Validation is conducted using a ship motion simulation platform and a combined wind-wave basin with a 1:10 scaled prototype. Experimental results confirm high compensation accuracy, with heave variation maintained within 1.6 cm and a relative error between simulation and experiment of approximately 18.2%.

Abstract: Microplastic quantification in marine vegetated ecosystems remains analytically demanding, yet little is known about the environmental footprint of the laboratory procedures required to isolate and measure these particles. This study applies Life Cycle Assessment (LCA) to laboratory analytical workflows for microplastics quantification, focusing exclusively on sample preparation and analytical procedures rather than natural environmental absorption or fate processes, in two ecologically relevant matrices: (i) pelagic algae (Sargassum) and (ii) seagrass biomass. Using openLCA 2.5 and the ReCiPe Midpoint (H) v1.13 method, the analysis integrates foreground inventories of digestion, filtration, drying, and spectroscopic identification, combined with background datasets from OzLCI2019, ELCD 3.2 and USDA. Results show substantially higher impacts for the algae scenario, particularly in climate change, human toxicity, ionising radiation and particulate matter formation, largely driven by longer digestion times, increased reagent use and higher energy demand during sample pre-treatment. Conversely, the seagrass scenario exhibited lower burdens per functional unit due to reduced organic complexity and shorter laboratory processing requirements. These findings highlight the importance of matrix-specific methodological choices and the influence of background datasets on impact profiles. The study provides a first benchmark for the environmental performance of microplastic analytical workflows and underscores the need for harmonised, low-impact laboratory protocols to support sustainable monitoring of microplastic pollution in marine ecosystems.

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.