Search Results (10,924)

In this study, a numerical simulation method was used to explore the impact of changes in riverine runoff input flow variations on the marine environmental dynamics in the northern Yellow Sea of China. Based on the depth-averaged two-dimensional shallow water equations, a hydrodynamic and water quality coupling model was established to simulate the changes of the water environmental indicators under five conditions. Validation against filed-measured data confirmed the model’s great accuracy and stability. The findings show that interception activities had a relatively small impact on hydrodynamic conditions, and the changes in velocity did not exceed 10 cm/s; however, the salinity changed significantly. As the interception rate increased, the moving distance of the isohaline with a value of 5 towards the estuary gradually increased, with a maximum distance of 3420 m. Meanwhile, the amount of reduction of the area of the envelope curve with a salinity of 26.8 gradually increased, with a peak areal reduction rate of 10.7%. The amount of changes in nutrient concentration was related to the interception rate and the distance of a station from the estuary. The maximum percentages of changes in inorganic nitrogen and inorganic phosphorus contents were 5.39% and 6.34%, respectively. This study provides a technical methodology for evaluating the impacts of analogous riverine runoff variations on estuarine and adjacent ecosystems. Full article

This study investigates the dual-task control challenge of path following and obstacle avoidance for deep-sea mining robots operating in complex, unstructured environments. To address the limitations of traditional training strategies, we propose an optimized training framework that integrates environmental design enhancements and algorithmic advancements. Specifically, we develop a Dual-Task Training Environment by combining the Random Obstacle Environment with a newly proposed Obstructed Path Environment, ensuring a balanced learning approach. While agents trained solely in the Random Obstacle Environment exhibit unilateral obstacle avoidance strategies and achieve a 0% success rate in randomized obstacle scenarios, those trained in the Dual-Task Environment demonstrate 85.4% success under identical test conditions and acquire more complex bilateral avoidance strategies. Additionally, we introduce a Dynamic Multi-Step Update mechanism, which integrates immediate rewards with long-term returns to enhance deep reinforcement learning (Twin Delayed Deep Deterministic Policy Gradient, TD3) performance without increasing computational complexity. Under the optimal multi-step setting (n = 5), the Dynamic Multi-Step Update mechanism significantly improves path following accuracy, reducing trajectory deviations to 0.128 m on straight paths and 0.195 m on S-shaped paths, while achieving nearly 100% success in multi-directional obstacle avoidance tests. These improvements collectively enhance the adaptability, robustness, and operational performance of deep-sea mining robots, advancing intelligent control strategies for autonomous deep-sea exploration and resource extraction. Full article

For a successful mangrove plantation, previous studies have proposed a small rubble mound breakwater, termed a “portable reef”, and explored the effectiveness of such reefs in terms of wave transmission. This study conducted a real-scale wave flume experiment incorporating a portable reef to assess the oscillatory behavior of young mangroves. To capture the dynamics of these young mangrove analogs—represented as elastic bodies—we employed a high-speed camera for precise tracking. A comparative analysis of the oscillatory characteristics was performed, evaluating the responses in both the presence and absence of the reef. The findings revealed several important points. First, portable reefs can effectively reduce wave heights, but they reduce plant oscillations to an even greater degree. Second, by calibrating the elastic modulus of the plant models, their oscillation behaviors can be analytically predicted. The results of our analytical model indicate that the acceleration experienced by the plants is amplified under conditions of shorter wave periods and softer stems, highlighting an increased susceptibility to damage from short-period waves, particularly in very young mangroves. Third, we identified that the conventional wave transmission formulas tend to overestimate the reduction in wave energy attributable to portable reefs, which consequently leads to an underestimation of the young mangroves’ oscillations. Based on these findings, we propose an integrated chart that combines wave transmission and plant oscillation coefficients, aimed at enhancing the design and effectiveness of portable reefs in protecting young mangroves. The insights obtained from this study will aid in the informed design of portable reefs. Full article

Spherical buoys serve as water surface markers, and their location information can help unmanned surface vessels (USVs) identify navigation channel boundaries, avoid dangerous areas, and improve navigation accuracy. However, due to the presence of disturbances such as reflections, water obstruction, and changes in illumination for spherical buoys on the water surface, using binocular vision for positioning encounters difficulties in matching. To address this, this paper proposes a monocular vision-based localization method for spherical buoys using elliptical fitting. First, the edges of the spherical buoy are extracted through image preprocessing. Then, to address the issue of pseudo-edge points introduced by reflections that reduce the accuracy of elliptical fitting, a multi-step method for eliminating pseudo-edge points is proposed. This effectively filters out pseudo-edge points and obtains accurate elliptical parameters. Finally, based on these elliptical parameters, a monocular vision ranging model is established to solve the relative position between the USV and the buoy. The USV’s position from satellite observation is then fused with the relative position calculated using the method proposed in this paper to estimate the coordinates of the buoy in the geodetic coordinate system. Simulation experiments analyzed the impact of pixel noise, camera height, focal length, and rotation angle on localization accuracy. The results show that within a range of 40 m in width and 80 m in length, the coordinates calculated by this method have an average absolute error of less than 1.2 m; field experiments on actual ships show that the average absolute error remains stable within 2.57 m. This method addresses the positioning issues caused by disturbances such as reflections, water obstruction, and changes in illumination, achieving a positioning accuracy comparable to that of general satellite positioning. Full article

Digitalization is transforming the maritime sector, and the amount and variety of data generated is increasing rapidly. Effective data utilization is crucial for data-driven services such as for highly automated maritime systems and efficient traffic coordination. However, these applications depend on heterogeneous, distributed data sources managed by different actors, making secure and sovereign information sharing difficult. This paper investigates how maritime data can be exchanged reliably and securely without jeopardizing data sovereignty. Based on the existing literature, we identify the main challenges and current research gap in sharing maritime information, emphasizing the importance of data availability. From this, we derive requirements for a secure and sovereign infrastructure for data exchange. To address these challenges, we propose a fully decentralized architecture for the maritime sector based on the concept of a data space. Our approach integrates protocols to improve data availability while minimizing data volume, considering maritime constraints such as volatile connectivity, low bandwidth and existing standards. We evaluate our architecture through a maritime traffic management case study and demonstrate its ability to enable secure and sovereign exchange of heterogeneous data. The results confirm that our solution reliably supports distributed data collection and enables data-driven, value-added services, which in turn will improve the safety and efficiency of the maritime domain in the near future. Full article

A ship’s diesel–electric hybrid power system is complex, with hardware configuration and energy management parameters being crucial to its economic performance. However, existing optimization methods typically involve designing and optimizing the hardware configuration on the basis of typical operating conditions, followed by the design and optimization of the energy management parameters, which makes it difficult to achieve optimal system performance. Moreover, when co-optimizing hardware configurations and energy management parameters, the parameter relationships and complex constraints often lead conventional optimization algorithms to converge slowly and become trapped in local optima. To address this issue, a hybrid Ivy-SA algorithm is developed for the co-optimization of both the hardware configuration and energy management parameters. First, the main engine and hybrid ship models are established on the basis of the hardware configuration, and the accuracy of the models is validated. An energy management strategy based on the equivalent fuel consumption minimization strategy (ECMS) is then formulated, and energy management parameters are designed. A sensitivity analysis is conducted on the basis of both the hardware configuration and energy management parameters to evaluate their impacts under various conditions, enabling the selection of key optimization parameters, such as diesel engine parameters, battery configuration, and charge/discharge factors. The Ivy-SA algorithm, which integrates the advantages of both the Ivy algorithm (IVYA) and the simulated annealing algorithm (SA), is developed for the co-optimization. The algorithm is tested with the CEC2017 benchmark functions and outperforms 11 other algorithms. Furthermore, when the top five performing algorithms are applied for the co-optimization, the results show that the Ivy-SA algorithm outperforms the other four algorithms with a 14.49% increase in economic efficiency and successfully escapes local optima. Full article

As environmental pollution has become a global concern, regulations on carbon emissions from maritime activities are being implemented, and interest in using renewable energy as fuel for ships is growing. Hydrogen, which does not release carbon dioxide and has a high energy density, can potentially replace fossil fuels as a renewable energy source. Notably, storage of hydrogen in a liquid state is considered the most efficient. In this study, a 0.7 m³ liquid hydrogen fuel tank suitable for small vessels was designed, and a structural analysis was conducted to assess its structural integrity. The extremely low liquefaction temperature of hydrogen at −253 °C and the need for spatial efficiency in liquid hydrogen fuel tanks make vacuum insulation essential to minimize the heat transfer due to convection. A composite insulation system of sprayed-on foam insulation (SOFI) and multilayer insulation (MLI) was applied in the vacuum annular space between the inner and outer shells, and a tube-shaped supporter made of a G-11 cryogenic (CR) material with low thermal conductivity and high strength was employed. The material selected for the inner and outer layers of the tank was STS 316L, which exhibits sufficient ductility and strength at cryogenic temperatures and has low sensitivity to hydrogen embrittlement. The insulation performance was quantitatively assessed by calculating the boil-off rate (BOR) of the designed fuel tank. Structural integrity evaluations were conducted for nine load cases using heat transfer and structural analyses in accordance with the IGF code. Full article

The Bohai Sea is the only semi-enclosed inland sea in China. With active marine economic activities, it faces a persistently high risk of oil spill accidents. This study assesses the occurrence risk and pollution risk of oil spills by considering factors such as sea state, the location of oil platform, and shipping route density in the Bohai Sea. The results show that the central part of the Bohai Sea, the southern Liaodong Peninsula, and the Bohai Strait area have a relatively high occurrence risk of oil spills due to busy maritime traffic and harsh sea conditions. In contrast, some areas in the northern, western, and southern parts of the Bohai Sea have a relatively low occurrence risk of oil spills because of weak maritime activity intensity and relatively calm sea state. In terms of the oil pollution risk, its distribution in the Bohai Sea shows significant seasonal characteristics, which are mainly comprehensively affected by multiple dynamic factors such as circulation, monsoon, and seawater exchange. Based on the oil pollution risk distribution, seasonally targeted strategies are proposed, which can provide a scientific basis for oil spill prevention and emergency management in the Bohai Sea, and help relevant departments formulate targeted prevention and control strategies. Full article

Accurate wave height measurements are critical for offshore wind farm operations, marine navigation, and environmental monitoring. Wave buoys provide essential real-time data; however, their reliability is compromised by harsh marine conditions, resulting in frequent data gaps due to sensor failures, maintenance issues, or extreme weather events. These disruptions pose significant risks for decision-making in offshore logistics and safety planning. While numerical wave models and machine learning techniques have been explored for wave height prediction, most approaches rely heavily on historical data from the same buoy, limiting their applicability when the target sensor is offline. This study addresses these limitations by developing a virtual wave buoy model using a network-based data-driven approach with Random Forest Regression (RFR). By leveraging wave height measurements from surrounding buoys, the proposed model ensures continuous wave height estimation even in the case of malfunctioning physical sensors. The methodology is tested across four offshore sites, including operational wind farms, evaluating the sensitivity of predictions to buoy placement and feature selection. The model demonstrates high accuracy and incorporates a k-nearest neighbors (kNN) imputation strategy to mitigate data loss. These findings establish RFR as a scalable and computationally efficient alternative for virtual sensing, thereby enhancing offshore wind farm resilience, marine safety, and operational efficiency. Full article

Offshore wind turbines (OWTs) exhibit inherent directional variations in inertia, stiffness, and damping properties. This study examines the directionality effect of a 10 MW monopile-supported OWT using an integrated rotor-nacelle assembly (RNA) and support structure model. Through combined theoretical analysis and numerical simulations, this paper systematically investigates the following: (1) the anisotropic characteristics of RNA rotational inertia and blade stiffness, (2) the natural frequency and aerodynamic damping properties of the system, and (3) the directional mechanisms governing seismic responses of MOWTs during parked and running states. The key findings reveal substantial structural anisotropies. The second-order natural frequencies display a 15% disparity between fore–aft (1.43 Hz) and side–side (1.24 Hz) tower modes. The blade frequencies show over 50% differences between flap-wise (0.60 Hz/1.69 Hz) and edge-wise (0.91 Hz/2.71 Hz) modes in first-/second-order vibrations. Moreover, the aerodynamic damping ratios show marked directional contrast, with first-mode fore–aft damping (8%) exceeding side–side values (1.11%) by a factor of 7.2. Consequently, the seismic input directionality induces peak yaw-bearing bending moment variations of 38% (running condition) and 73% (parked condition). The directional effects in parked OWTs are attributed to RNA inertia anisotropy and blade stiffness disparities, while the running condition demonstrates combined influences from inherent system parameters (inertia, stiffness, aerodynamic damping) and wind–wave environmental loading. Full article

This study investigated the impact of different load distribution ratios between two rotors on the unsteady performance of dual-stage pump-jet propulsors using Computational Fluid Dynamics (CFDs) and experimental methods. The Shear Stress Transport (SST) k-ω model was employed to solve turbulence problems, and the numerical simulation method used was validated. The following conclusions were drawn: Different load distribution ratios of the dual-stage rotors have no significant impact on the overall propulsion performance of the propulsor. As the load distribution ratio is aft-shifted, the axial unsteady force of the entire propulsor continuously decreases, with a reduction of up to 53.6%. This is due to the gradual reduction in the energy of the first-stage rotor, leading to a more uniform Blade-Passing Frequency Velocity Harmonic Coefficient (BPFVHC) in front of the second-stage rotor, thereby gradually reducing the unsteady force of the second-stage rotor. The experimental results also indicate that the aft-shifted load model can reduce the sound pressure level of the propulsor. Compared to the prototype propulsor, the sound pressure level at the Blade-Passing Frequency decreases by 6.67 dB, or about 78.5%, in sound energy. This study has important implications for the low-excitation design of dual-stage pump-jet propulsors. Full article

To enhance the autonomy of semi-submersibles, a Model Predictive Control (MPC) strategy was proposed based on real-time Line-of-Sight (LOS) to address the issue of thruster saturation. By identifying parameters using experimental data from sea trials, the dynamic model of the semi-submersible was derived and established. The kinematic and dynamic models were combined to construct a complete MPC prediction model, and the LOS method was integrated into the MPC strategy to achieve trajectory-tracking functionality. Unlike prior research that was validated exclusively through simulations, this paper further validated the efficacy of the improved LOS-MPC in real path tracking through a series of sea trials. The experimental findings indicate that the improved LOS-MPC approach is capable of rapidly guiding the semi-submersible to precisely follow the reference trajectory. In comparison to conventional PID controllers, the LOS-MPC-based path-tracking controller demonstrates enhanced effectiveness in terms of response speed, tracking accuracy, and robustness. Full article

Under wideband interference conditions, traditional neural networks often suffer from low accuracy in single-frequency direction-of-arrival (DOA) estimation and face challenges in detecting single-frequency sound sources. To address this limitation, we propose a novel model called Parallel Net. The architecture adopts a frequency-parallel design: it first employs a recurrent neural network, the generalized feedback gated recurrent unit (GFGRU), to independently extract features from each frequency component, and then it fuses these features through an attention mechanism. This design significantly enhances the network’s capability in estimating the DOA of single-frequency signals. The simulation results demonstrate that when the signal-to-noise ratio (SNR) exceeds −10 dB, Parallel Net achieves a mean absolute error (MAE) below 2°, outperforming traditional frequency-coherent neural networks and the MUSIC algorithm, and reduces the error to half that of classical beamforming (CBF). Further validation on the SWellEx-96 experiment confirms the model’s effectiveness in detecting single-frequency sources under wideband interference. Parallel Net exhibits superior sidelobe suppression and fewer spurious peaks compared to CBF, achieves higher accuracy than MUSIC, and produces smoother and more continuous DOA trajectories than conventional neural network models. Full article

The drastic changes in the marine environment can induce the instability of seabed sediments, threatening the safety of marine engineering facilities such as offshore oil platforms, oil pipelines, and submarine optical cables. Due to the lack of long-term in situ observation equipment for the engineering properties of seabed sediments, most existing studies have focused on phenomena such as the erosion suspension of the seabed boundary layer and wave-induced liquefaction, leading to insufficient understanding of the dynamic processes affecting the seabed environment. In this study, a long-term in situ observation system for subsea engineering geological environments was developed and deployed for 36 days of continuous monitoring in the offshore area of Qingdao. It was found that wave action significantly altered sediment mechanical properties, with a 5% sound velocity increase correlating to 39% lower compression, 7% higher cohesion, 11% greater internal friction angle, and 50% reduced excess pore water pressure at 1.0–1.8 m depth. suggesting sustained 2.2 m wave loads of expelled pore water, driving dynamic mechanical property variations in seabed sediments. This long-term in situ observation lays the foundation for the monitoring and early warning of marine engineering geological disasters. Full article

Marine researchers rely heavily on ocean sound velocity, a crucial hydroacoustic environmental metric that exhibits large geographical and temporal changes. Nowadays, spatio-temporal series prediction algorithms are emerging, but their prediction accuracy requires improvement. Moreover, in terms of ocean sound speed, most of these models predict an ocean sound speed profile (SSP) at a single coordinate position, and only a few predict multi-spatial-scale SSPs. Hence, this paper proposes a new data-driven method called STA-Conv-LSTM that combines convolutional long short-term memory (Conv-LSTM) and spatio-temporal attention (STA) to predict SSPs. We used a 234-month dataset of monthly mean sound speeds in the eastern Pacific Ocean from January 2004 to June 2023 to train the prediction model. We found that using 24 months of SSPs as the inputs to predict the SSPs of the following month yielded the highest accuracy. The results demonstrate that STA-Conv-LSTM can achieve predictions with an accuracy of more than 95% for both single-point and three-dimensional scenarios. We compared it against recurrent neural network, LSTM, and Conv-LSTM models with optimal parameter settings to demonstrate the model’s superiority. With a fitting accuracy of 95.12% and the lowest root-mean-squared error of 0.8978, STA-Conv-LSTM clearly outperformed the competition with respect to prediction accuracy and stability. This model not only predicts SSPs well but also will improve the spatial and temporal forecasts of other marine environmental factors. Full article