Journal of Marine Science and Engineering

This study on total suspended matter (TSM) in the Pearl River Estuary established a regression analysis model using Landsat 8 reflectance and measured TSM data, crucial for environmental management and engineering projects. High coefficients of determination (>0.6) were reported for the selected models. TSM concentration was notably high in 2013, peaking at 180 mg/L during the flood season and 80 mg/L in the dry season. In contrast, 2020 saw lower concentrations. Similar spatial distribution patterns were observed during dry and flood seasons, with high nearshore and low offshore TSM concentrations. Statistical analyses revealed natural factors (precipitation and runoff) as major influencers of the TSM distribution, with human activities presenting localized, limited impacts, except under long-term and large-scale conditions. Over time, the influence of large-scale water-based construction, such as the Hong Kong–Zhuhai–Macao Bridge, on TSM distribution became significant. Full article

Under typical marine conditions of about 80% relative humidity, evaporation of water from the ocean is an irreversible process accompanied by entropy production. In this article, equations are derived for the latent heat of irreversible evaporation and the related nonequilibrium entropy balance at the sea surface. To achieve this, linear irreversible thermodynamics is considered in a conceptual ocean evaporation model. The equilibrium thermodynamic standard TEOS-10, the International Thermodynamic Equation of Seawater—2010, is applied to irreversible evaporation under the assumption of local thermodynamic equilibrium. The relevance of local equilibrium conditions for irreversible thermodynamics is briefly explained. New equations are derived for the mass flux of evaporation and for the associated nonequilibrium enthalpies and entropies. The estimated entropy production rate of ocean evaporation amounts to $0.004 \mathrm{W} {\mathrm{m}}^{-2} {\mathrm{K}}^{-1}$ as compared with the average terrestrial global entropy production of about $1 \mathrm{W} {\mathrm{m}}^{-2} {\mathrm{K}}^{-1}$. Full article

Sea ice affects the Earth’s energy balance and ocean circulation and is crucial to the global climate system. However, research on the decadal variations in the mean sea-level pressure patterns in recent winters (2001–2020) and the characteristics of sea ice motion (SIM) in the Western Arctic region is very limited. In this study, we utilized the Empirical Orthogonal Function (EOF) analysis method to investigate the potential impacts of Arctic Oscillation (AO) and Arctic Dipole (AD) on the Beaufort High (BH) during the period 2001–2020 and discuss the changes in SIM intensity in the Western Arctic. The results indicate that the negative phases of AO and AD are connected with (tend to bring about) a higher BH, strengthening anticyclonic circulation in the Arctic region. Conversely, the positive phases of AO and AD led to the collapse of the BH, resulting in a reversal of sea ice movement. Additionally, during the period 2001–2020, the BH consistently explained 67% of the sea ice motion (had the highest explanatory degree for sea ice advection within the region (weighted average 61.71%)). Meanwhile, the sea ice advection has become more sensitive to change in various atmospheric circulations. This study contributes to an in-depth understanding of the response of sea ice motion to atmospheric circulation in the Western Arctic in recent years, offering more explanations for the anomalous movement of sea ice in the Western Arctic. Full article

The deep sea harbors abundant mineral, oil, and gas resources, making it highly valuable for commercial development, including the extraction of minerals. Due to the relatively large particle size of these minerals, how they interact with fluids is significantly different from that of small particles. However, there has been limited simulation research on the impacts of large particles (the diameter of particles is at the level of centimeters) on the flow and wear characteristics in bends, because the simulation of the particles at such a size is difficult. Additionally, in the field of deep-sea mining, multiple bends are simultaneously connected in series, and the wear in such bends has garnered increasing attention. Based on an improved CFD-DEM model, this article solved the issue that traditional unresolved CFD-DEM methods cannot accurately simulate large particles in a hydraulic conveying pipe and bend. After validating the accuracy of this model against classical experiments, the paper comprehensively analyzes the modulation effect of large particles on turbulence, and the effects of different particle diameters, particle transport concentrations, and transport velocities on the wear of bends connected serially. Finally, the bends connected serially in various configurations are simulated to study the wear on the bent interior surfaces. Results indicate a pronounced modulation effect of large particles on turbulence at higher transport concentrations; the wear rate in the combined bends does not exhibit a linear correlation with the collision frequency of particles on the wall surface. Furthermore, different configurations of serially connected bends exhibit significant differences in the wear morphology of the second bend. Full article

Coasts are continually changing and remote sensing from satellites has the potential to both map and monitor coastal change at multiple scales. Unlike optical technology, synthetic aperture radar (SAR) is uninfluenced by darkness, clouds, and rain, potentially offering a higher revision period to map shoreline position and change, but this can only be feasible if we have a better interpretation of what shorelines as extracted from SAR imagery represent on the ground. This study aims to assess the application of shorelines extracted from SAR from publicly available satellite imagery to map and capture intra-annual to inter-annual shoreline variability. This is assessed in three tidally controlled coastal study areas that represent sand and gravel beaches with different backshore environments: low-lying dunes and marsh; steep, rocky cliff; and urban environments. We have found that SAR shorelines consistently corresponded to positions above the high-water mark across all three sites. We further discuss the influence of the scene geometry, meteorological and oceanographic conditions, and backshore environment and provide a conceptual interpretation of SAR-derived shorelines. In a low-lying coastal setting, the annual change rate derived through SAR presents a high degree of alignment with the known reference values. The present study contributes to our understanding of the poorly known aspect of using shorelines derived from publicly available SAR satellite missions. It outlines a quantitative approach to automatically assess their quality with a new automatic detection method that is transferable to shoreline evolution assessments worldwide. Full article

The onboard energy supply of Autonomous Underwater Vehicles (AUVs) is one of the main limiting factors for their development. The existing methods of deploying and retrieving AUVs from mother ships consume a significant amount of energy during submerging and surfacing, resulting in a small percentage of actual working time. Underwater docking chambers provide support to AUVs underwater, saving their precious energy and addressing this issue. When an AUV cluster is assigned multiple tasks, scheduling the cluster becomes essential, and task allocation and path planning are among the core problems in AUV cluster scheduling research. In this paper, based on the underwater docking chamber, an Improved Genetic Local Search Algorithm with Prior Knowledge (IGLSAPK) is proposed to simultaneously solve the task allocation and path planning problems. Under constraints such as onboard energy supply, AUV quantity, and AUV type, the algorithm groups AUVs, assigns tasks, and plans paths to accomplish tasks at different locations, aiming to achieve overall efficiency. The algorithm first generates an initial population using prior knowledge to improve its search efficiency. It then combines an improved local search algorithm to efficiently solve large-scale, complex, and highly coupled problems. The algorithm has been evaluated through simulation experiments and comparative experiments, and the results demonstrate that the proposed algorithm outperforms other algorithms in terms of speed and optimality. The algorithm presented in this paper addresses the grouping, task allocation, and path planning problems in heterogeneous AUV clusters. Its practical significance lies in its ability to handle tasks executed by a heterogeneous AUV group, making it more practical compared to previous algorithms. Full article

The world population is expected to grow to around 9 billion by 2050. The growing need for foods with high protein levels makes aquaculture one of the fastest-growing food industries in the world. Some challenges of fishing production are related to obsolete aquaculture techniques, overexploitation of marine species, and lack of water quality control. This research systematically analyzes aquaculture technologies, such as sensors, artificial intelligence (AI), and image processing. Through the systematic PRISMA process, 753 investigations published from 2012 to 2023 were analyzed based on a search in Scopus and Web of Science. It revealed a significant 70.5% increase in the number of articles published compared to the previous year, indicating a growing interest in this field. The results indicate that current aquaculture technologies are water monitoring sensors, AI methodologies such as K-means, and contour segmentation for computer vision. Also, it is reported that K means technologies offer an efficiency from 95% to 98%. These methods allow decisions based on data patterns and aquaculture insights. Improving aquaculture methodologies will allow adequate management of economic and environmental resources to promote fishing and satisfy nutritional needs. Full article

This study introduces global path planning for autonomous ships in port environments, with a focus on the Port of Ulsan, where various environmental factors are modeled for analysis. Global path planning is considered to take place from departure to berth, specifically accounting for scenarios involving a need to navigate via anchorage areas as waypoints due to unexpected increases in port traffic or when direct access to the berth is obstructed. In this study, a navigable grid for autonomous ships was constructed using land, breakwater, and water depth data. The modeling of the Port of Ulsan’s traffic lanes and anchorage areas reflects the port’s essential maritime characteristics for global path planning. In this study, an improved A* algorithm, along with grid-based path planning, was utilized to determine a global path plan. We used smoothing algorithms to refine the global paths for practical navigation, and the validation of these paths was achieved through conducting ship maneuvering simulations from model tests, which approximate real-world navigation in navigational simulation. This approach lays the groundwork for enhanced route generation studies in complex port environments. Full article

Water quality prediction, a well-established field with broad implications across various sectors, is thoroughly examined in this comprehensive review. Through an exhaustive analysis of over 170 studies conducted in the last five years, we focus on the application of machine learning for predicting water quality. The review begins by presenting the latest methodologies for acquiring water quality data. Categorizing machine learning-based predictions for water quality into two primary segments—indicator prediction and water quality index prediction—further distinguishes between single-indicator and multi-indicator predictions. A meticulous examination of each method’s technical details follows. This article explores current cutting-edge research trends in machine learning algorithms, providing a technical perspective on their application in water quality prediction. It investigates the utilization of algorithms in predicting water quality and concludes by highlighting significant challenges and future research directions. Emphasis is placed on key areas such as hydrodynamic water quality coupling, effective data processing and acquisition, and mitigating model uncertainty. The paper provides a detailed perspective on the present state of application and the principal characteristics of emerging technologies in water quality prediction. Full article

The safety assessment of ship cargo securing systems is of significant importance in preventing casualties, vessel instability, and economic losses resulting from the failure of securing systems during transportation in adverse sea conditions. In this study, an independently designed cylindrical cargo securing scheme with supporting structures was adopted for investigation. Utilizing a sway device, three-degree-of-freedom coupled motion encountered during ship transportation was obtained, and data regarding changes in the support forces at the foundations and tension forces in the lashing ropes were collected. Subsequently, numerical simulations were conducted using the multibody dynamics software ADAMS 2020. The results obtained from the simulations were compared with the experimental data. The overall tendencies were accurately predicted in the numerical analysis. It was observed that the difference of the peak support forces between the numerical simulation results and the experimental data were within a 10% margin. In terms of the lashing ropes, the difference was limited, within 9%. These findings demonstrate that numerical simulation techniques can provide valuable insights for verifying the safety of practical cargo securing systems. Full article

Trajectory data holds pivotal importance in the shipping industry and transcend their significance in various domains, including transportation, health care, tourism, surveillance, and security. In the maritime domain, improved predictions for estimated time of arrival (ETA) and optimal recommendations for alternate routes when the weather conditions deem it necessary can lead to lower costs, reduced emissions, and an increase in the overall efficiency of the industry. To this end, a methodology that yields optimal route recommendations for vessels is presented and evaluated in comparison with real-world vessel trajectories. The proposed approach utilizes historical vessel tracking data to extract maritime traffic patterns and implements an A* search algorithm on top of these patterns. The experimental results demonstrate that the proposed approach can lead to shorter vessel routes compared to another state-of-the-art routing methodology, resulting in cost savings for the maritime industry. This research not only enhances maritime routing but also demonstrates the broader applicability of trajectory mining, offering insights and solutions for diverse industries reliant on trajectory data. Full article

The most commonly used subsea pipeline installation method is the S-Lay method. A very important and complex task in an S-Lay installation engineering analysis is to find the optimal pipelay vessel installation configuration for every distinctive pipeline route section. Installation loads in the pipeline are very sensitive to small changes in the configuration of the pipeline supports during laying and other influential parameters, such as the tensioner force, stinger angle, trim and draft of the pipelay vessel. Therefore, the process of an engineering installation analysis is very demanding, and there is a need for an automated optimization process. For that purpose, installation engineering methodology criteria and requirements are formalized into a nonlinear optimization problem with mixed continuous and discrete variables. A special tailored multi-objective genetic algorithm is developed that can be adjusted to any desired combination of criteria and offshore standards’ requirements. The optimization algorithm is applied to the representative test cases. The optimization procedure efficiency and quality of the achieved solution prove that the developed genetic algorithm operators and the whole optimization approach are adequate for the presented application. Full article

Pollution caused by marine oil spills can lead to persistent ecological disasters and severe social and economic damages. Numerical simulations are useful and essential tools for accurate decision making during emergencies and planning response actions. In this study, we applied the Princeton Ocean Model (POM) to determine current data, including seawater velocity, salinity, and temperature, and we obtained the fate and trajectory of spilled oil using OpenOil. Several probable oil slicks around Taiwan were simulated over time (12 months) and space (four spill locations in the marine area of each coastal city or county) using the model. The percentage risk under the effect of an oil spill is estimated. The risk zone of the coastal waters of Taiwan was identified based on the frequency of simulated oil slicks hitting the coast and sensitive resources. This information not only helps authorities guide the preparation of effective plans to minimise the impacts of oil spill incidents but could also be used to improve regulations related to shipping and vessel navigation in regional seas. Full article

This paper proposes a new method for compensating current measurement errors in shipboard permanent magnet propulsion motors. The method utilizes cascade decoupling second-order generalized integrators (SOGIs) and adaptive linear neurons (ADALINEs) as the current harmonic extractor and the compensator, respectively. It can compensate for the dq-axes offset and scaling errors simultaneously, improving phase current distortion while reducing the ripples of motor speed and torque. Compared to the traditional motor model-based compensation strategies, the proposed method is robust against the changes in motor parameters with the online adaptive capability of the ADALINE algorithm. Furthermore, due to the good real-time performance of SOGIs and ADALINEs, the proposed compensation strategy can effectively operate in both the steady state and transient state of the motor. Finally, the effectiveness of the proposed method is verified through the physical and hardware-in-the-loop (HIL) experiments. After compensating for the current measurement errors of a 1 kW test motor with the propeller-characteristics load, the torque ripple and speed ripple are reduced by more than 65% and 80%, respectively. At the same time, the DC component and the second-order and third-order harmonics in the phase currents are also significantly reduced. Similar test results can be also obtained on the HIL platform with a 100 kW permanent magnet motor. Full article

The incorporation of machine learning (ML) has yielded substantial benefits in detecting nonlinear patterns across a wide range of applications, including offshore engineering. Existing ML works, specifically supervised regression models, have not undergone exhaustive scrutiny, and there are no potential or concurrent models for improving the performance of wave energy converter (WEC) devices. This study employs supervised regression ML models, including multi-layer perceptron, support vector regression, and XGBoost, to optimize the geometric aspects of an asymmetric WEC inspired by Salter’s duck, based on key parameters. These important parameters, the ballast weight and its position, vary along a guided line within the available geometric resilience of the asymmetric WEC. Each supervised regression ML model was fine-tuned through hyperparameter optimization using Grid cross-validation. When evaluating the performance of each ML model, it became evident that the tuned hyperparameters of XGBoost led to predictions that strongly aligned with the actual values compared to other models. Furthermore, the study extended to assess the performance of the optimized WEC at the designated deployment test site location. Full article

This research introduces an online system for monitoring maritime traffic, aimed at tracking vessels in water routes and predicting their subsequent locations in real time. The proposed framework utilizes an Extract, Transform, and Load (ETL) pipeline to dynamically process AIS data by cleaning, compressing, and enhancing it with additional attributes such as online traffic volume, origin/destination, vessel trips, trip direction, and vessel routing. This processed data, enriched with valuable details, serves as an alternative to raw AIS data stored in a centralized database. For user interactions, a user interface is designed to query the database and provide real-time information on a map-based interface. To deal with false or missing AIS records, two methods, dead reckoning and machine learning techniques, are employed to anticipate the trajectory of the vessel in the next time steps. To evaluate each method, several metrics are used, including R squared, mean absolute error, mean offset, and mean offset from the centerline. The functionality of the proposed system is showcased through a case study conducted in the Gulf Intracoastal Waterway (GIWW). Three years of AIS data are collected and processed as a simulated API to transmit AIS records every five minutes. According to our results, the Seq2Seq model exhibits strong performance (0.99 R squared and an average offset of ~1400 ft). However, the second scenario, dead reckoning, proves comparable to the Seq2Seq model as it involves recalculating vessel headings by comparing each data point with the previous one. Full article

Marine monitoring equipment such as Argo profiling buoys and underwater gliders are important devices for oceanographic research and marine resource exploration. In this study, a novel mobile buoy capable of vertical profiling motion like Argo profiling buoys and sawtooth gliding motion like underwater gliders is proposed. The proposed mobile buoy can switch between the two motion modes with controllable wings. To verify the feasibility of the proposed mobile buoy, a fluid–multibody coupling model considering multibody dynamics and hydrodynamics was developed to investigate the dynamic response. A scaled-down buoy prototype was fabricated and the feasibility of the two motion modes was experimentally investigated in a laboratory tank. The experimental results agree well with the results of numerical simulation. This work can be helpful for the design and analysis of this kind of mobile buoy. Full article

Annex VI of the International Convention for the Prevention of Pollution from Ships (MARPOL Convention), adopted in October 2008, was dedicated to addressing environmental issues caused by ships, especially in ports, inland waterways, and some sea areas with concentrated routes and high navigational density. This study utilizes a regional-level ship dataset to assess the influences of emission-control areas (ECAs) on the ecological shipbuilding industry by fitting the policy utility through the synthetic control method and testing robustness via the difference-in-differences method. The outcomes of this study show that the cumulative new orders for eco-designed ships in China, The Netherlands, Republic of Korea, the UK, and the USA increased by 3401, 81, 234, 549, and −1435, respectively, after the implementation of ECAs. Compared to the implementation of ECAs, the increases were about 32%, 20%, 41%, 66%, and −83%, respectively. Full article