Journal of Marine Science and Engineering

21 pages, 2077 KiB

Open AccessArticle

Quantitative Risk Assessment of Liquefied Natural Gas Bunkering Hoses in Maritime Operations: A Case of Shenzhen Port

by Yimiao Gu, Yanmin Zeng and Hui Shan Loh

J. Mar. Sci. Eng. 2025, 13(8), 1494; https://doi.org/10.3390/jmse13081494 (registering DOI) - 2 Aug 2025

The widespread adoption of liquefied natural gas (LNG) as a marine fuel has driven the development of LNG bunkering operations in global ports. Major international hubs, such as Shenzhen Port, have implemented ship-to-ship (STS) bunkering practices. However, this process entails unique safety risks, [...] Read more.

The widespread adoption of liquefied natural gas (LNG) as a marine fuel has driven the development of LNG bunkering operations in global ports. Major international hubs, such as Shenzhen Port, have implemented ship-to-ship (STS) bunkering practices. However, this process entails unique safety risks, particularly hazards associated with vapor cloud dispersion caused by bunkering hose releases. This study employs the Phast software developed by DNV to systematically simulate LNG release scenarios during STS operations, integrating real-world meteorological data and storage conditions. The dynamic effects of transfer flow rates, release heights, and release directions on vapor cloud dispersion are quantitatively analyzed under daytime and nighttime conditions. The results demonstrate that transfer flow rate significantly regulates dispersion range, with recommendations to limit the rate below 1500 m³/h and prioritize daytime operations to mitigate risks. Release heights exceeding 10 m significantly amplify dispersion effects, particularly at night (nighttime dispersion area at a height of 20 m is 3.5 times larger than during the daytime). Optimizing release direction effectively suppresses dispersion, with vertically downward releases exhibiting minimal impact. Horizontal releases require avoidance of downwind alignment, and daytime operations are prioritized to reduce lateral dispersion risks. Full article

(This article belongs to the Section Ocean Engineering)

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19 pages, 18533 KiB

Open AccessArticle

Modeling of Marine Assembly Logistics for an Offshore Floating Photovoltaic Plant Subject to Weather Dependencies

by Lu-Jan Huang, Simone Mancini and Minne de Jong

J. Mar. Sci. Eng. 2025, 13(8), 1493; https://doi.org/10.3390/jmse13081493 (registering DOI) - 2 Aug 2025

Abstract

Floating solar technology has gained significant attention as part of the global expansion of renewable energy due to its potential for installation in underutilized water bodies. Several countries, including the Netherlands, have initiated efforts to extend this technology from inland freshwater applications to [...] Read more.

Floating solar technology has gained significant attention as part of the global expansion of renewable energy due to its potential for installation in underutilized water bodies. Several countries, including the Netherlands, have initiated efforts to extend this technology from inland freshwater applications to open offshore environments, particularly within offshore wind farm areas. This development is motivated by the synergistic benefits of increasing site energy density and leveraging the existing offshore grid infrastructure. The deployment of offshore floating photovoltaic (OFPV) systems involves assembling multiple modular units in a marine environment, introducing operational risks that may give rise to safety concerns. To mitigate these risks, weather windows must be considered prior to the task execution to ensure continuity between weather-sensitive activities, which can also lead to additional time delays and increased costs. Consequently, optimizing marine logistics becomes crucial to achieving the cost reductions necessary for making OFPV technology economically viable. This study employs a simulation-based approach to estimate the installation duration of a 5 MWp OFPV plant at a Dutch offshore wind farm site, started in different months and under three distinct risk management scenarios. Based on 20 years of hindcast wave data, the results reveal the impacts of campaign start months and risk management policies on installation duration. Across all the scenarios, the installation duration during the autumn and winter period is 160% longer than the one in the spring and summer period. The average installation durations, based on results from 12 campaign start months, are 70, 80, and 130 days for the three risk management policies analyzed. The result variation highlights the additional time required to mitigate operational risks arising from potential discontinuity between highly interdependent tasks (e.g., offshore platform assembly and mooring). Additionally, it is found that the weather-induced delays are mainly associated with the campaigns of pre-laying anchors and platform and mooring line installation compared with the other campaigns. In conclusion, this study presents a logistics modeling methodology for OFPV systems, demonstrated through a representative case study based on a state-of-the-art truss-type design. The primary contribution lies in providing a framework to quantify the performance of OFPV installation strategies at an early design stage. The findings of this case study further highlight that marine installation logistics are highly sensitive to local marine conditions and the chosen installation strategy, and should be integrated early in the OFPV design process to help reduce the levelized cost of electricity. Full article

(This article belongs to the Special Issue Design, Modeling, and Development of Marine Renewable Energy Devices)

26 pages, 1567 KiB

Open AccessArticle

A CDC–ANFIS-Based Model for Assessing Ship Collision Risk in Autonomous Navigation

by Hee-Jin Lee and Ho Namgung

J. Mar. Sci. Eng. 2025, 13(8), 1492; https://doi.org/10.3390/jmse13081492 (registering DOI) - 1 Aug 2025

Abstract

To improve collision risk prediction in high-traffic coastal waters and support real-time decision-making in maritime navigation, this study proposes a regional collision risk prediction system integrating the Computed Distance at Collision (CDC) method with an Adaptive Neuro-Fuzzy Inference System (ANFIS). Unlike Distance at [...] Read more.

To improve collision risk prediction in high-traffic coastal waters and support real-time decision-making in maritime navigation, this study proposes a regional collision risk prediction system integrating the Computed Distance at Collision (CDC) method with an Adaptive Neuro-Fuzzy Inference System (ANFIS). Unlike Distance at Closest Point of Approach (DCPA), which depends on the position of Global Positioning System (GPS) antennas, Computed Distance at Collision (CDC) directly reflects the actual hull shape and potential collision point. This enables a more realistic assessment of collision risk by accounting for the hull geometry and boundary conditions specific to different ship types. The system was designed and validated using ship motion simulations involving bulk and container ships across varying speeds and crossing angles. The CDC method was used to define collision, almost-collision, and near-collision situations based on geometric and hydrodynamic criteria. Subsequently, the FIS–CDC model was constructed using the ANFIS by learning patterns in collision time and distance under each condition. A total of four input variables—ship speed, crossing angle, remaining time, and remaining distance—were used to infer the collision risk index (CRI), allowing for a more nuanced and vessel-specific assessment than traditional CPA-based indicators. Simulation results show that the time to collision decreases with higher speeds and increases with wider crossing angles. The bulk carrier exhibited a wider collision-prone angle range and a greater sensitivity to speed changes than the container ship, highlighting differences in maneuverability and risk response. The proposed system demonstrated real-time applicability and accurate risk differentiation across scenarios. This research contributes to enhancing situational awareness and proactive risk mitigation in Maritime Autonomous Surface Ship (MASS) and Vessel Traffic System (VTS) environments. Future work will focus on real-time CDC optimization and extending the model to accommodate diverse ship types and encounter geometries. Full article

(This article belongs to the Special Issue Advanced Research in Guidance, Navigation, and Control for Autonomous Surface Vehicle)

18 pages, 5167 KiB

Open AccessArticle

Comparative Study of Local Stress Approaches for Fatigue Strength Assessment of Longitudinal Web Connections

by Ji Hoon Kim, Jae Sung Lee and Myung Hyun Kim

J. Mar. Sci. Eng. 2025, 13(8), 1491; https://doi.org/10.3390/jmse13081491 (registering DOI) - 1 Aug 2025

Abstract

Ship structures are subjected to cyclic loading from waves and currents during operation, which can lead to fatigue failure, particularly at locations with structural discontinuities such as welds. Although various fatigue assessment methods have been developed, there is a lack of experimental data [...] Read more.

Ship structures are subjected to cyclic loading from waves and currents during operation, which can lead to fatigue failure, particularly at locations with structural discontinuities such as welds. Although various fatigue assessment methods have been developed, there is a lack of experimental data and comparative studies for actual ship structure details. This study addresses this limitation by evaluating the fatigue strength of longi-web connections in hull structures using local stress approaches, including hot spot stress, effective notch stress, notch stress intensity factor, and structural stress methods. Finite element analyses were conducted, and the predicted fatigue lives and failure locations were compared with experimental results. Although there are some differences between each method, all methods are valid and reasonable for predicting the primary failure locations and evaluating fatigue life. These findings provide a basis for considering suitable fatigue assessment methods for welded ship structures with respect to joint geometry and failure mechanisms. Full article

(This article belongs to the Section Ocean Engineering)

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23 pages, 10936 KiB

Open AccessArticle

Towards Autonomous Coordination of Two I-AUVs in Submarine Pipeline Assembly

by Salvador López-Barajas, Alejandro Solis, Raúl Marín-Prades and Pedro J. Sanz

J. Mar. Sci. Eng. 2025, 13(8), 1490; https://doi.org/10.3390/jmse13081490 (registering DOI) - 1 Aug 2025

Abstract

Inspection, maintenance, and repair (IMR) operations on underwater infrastructure remain costly and time-intensive because fully teleoperated remote operated vehicle s(ROVs) lack the range and dexterity necessary for precise cooperative underwater manipulation, and the alternative of using professional divers is ruled out due to [...] Read more.

Inspection, maintenance, and repair (IMR) operations on underwater infrastructure remain costly and time-intensive because fully teleoperated remote operated vehicle s(ROVs) lack the range and dexterity necessary for precise cooperative underwater manipulation, and the alternative of using professional divers is ruled out due to the risk involved. This work presents and experimentally validates an autonomous, dual-I-AUV (Intervention–Autonomous Underwater Vehicle) system capable of assembling rigid pipeline segments through coordinated actions in a confined underwater workspace. The first I-AUV is a Girona 500 (4-DoF vehicle motion, pitch and roll stable) fitted with multiple payload cameras and a 6-DoF Reach Bravo 7 arm, giving the vehicle 10 total DoF. The second I-AUV is a BlueROV2 Heavy equipped with a Reach Alpha 5 arm, likewise yielding 10 DoF. The workflow comprises (i) detection and grasping of a coupler pipe section, (ii) synchronized teleoperation to an assembly start pose, and (iii) assembly using a kinematic controller that exploits the Girona 500’s full 10 DoF, while the BlueROV2 holds position and orientation to stabilize the workspace. Validation took place in a 12 m × 8 m × 5 m water tank. Results show that the paired I-AUVs can autonomously perform precision pipeline assembly in real water conditions, representing a significant step toward fully automated subsea construction and maintenance. Full article

(This article belongs to the Section Ocean Engineering)

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20 pages, 1907 KiB

Open AccessArticle

Multi-Innovation-Based Parameter Identification for Vertical Dynamic Modeling of AUV Under High Maneuverability and Large Attitude Variations

by Jianping Yuan, Zhixun Luo, Lei Wan, Cenan Wang, Chi Zhang and Qingdong Chen

J. Mar. Sci. Eng. 2025, 13(8), 1489; https://doi.org/10.3390/jmse13081489 (registering DOI) - 1 Aug 2025

Abstract

The parameter identification of Autonomous Underwater Vehicles (AUVs) serves as a fundamental basis for achieving high-precision motion control, state monitoring, and system development. Currently, AUV parameter identification typically relies on the complete motion information obtained from onboard sensors. However, in practical applications, it [...] Read more.

The parameter identification of Autonomous Underwater Vehicles (AUVs) serves as a fundamental basis for achieving high-precision motion control, state monitoring, and system development. Currently, AUV parameter identification typically relies on the complete motion information obtained from onboard sensors. However, in practical applications, it is often challenging to accurately measure key state variables such as velocity and angular velocity, resulting in incomplete measurement data that compromises identification accuracy and model reliability. This issue is particularly pronounced in vertical motion tasks involving low-speed, large pitch angles, and highly maneuverable conditions, where the strong coupling and nonlinear characteristics of underwater vehicles become more significant. Traditional hydrodynamic models based on full-state measurements often suffer from limited descriptive capability and difficulties in parameter estimation under such conditions. To address these challenges, this study investigates a parameter identification method for AUVs operating under vertical, large-amplitude maneuvers with constrained measurement information. A control autoregressive (CAR) model-based identification approach is derived, which requires only pitch angle, vertical velocity, and vertical position data, thereby reducing the dependence on complete state observations. To overcome the limitations of the conventional Recursive Least Squares (RLS) algorithm—namely, its slow convergence and low accuracy under rapidly changing conditions—a Multi-Innovation Least Squares (MILS) algorithm is proposed to enable the efficient estimation of nonlinear hydrodynamic characteristics in complex dynamic environments. The simulation and experimental results validate the effectiveness of the proposed method, demonstrating high identification accuracy and robustness in scenarios involving large pitch angles and rapid maneuvering. The results confirm that the combined use of the CAR model and MILS algorithm significantly enhances model adaptability and accuracy, providing a solid data foundation and theoretical support for the design of AUV control systems in complex operational environments. Full article

(This article belongs to the Section Ocean Engineering)

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18 pages, 8099 KiB

Open AccessArticle

Machine Learning-Based Recursive Prediction and Application of Green’s Function of Water-Wave Radiation and Diffraction

by Minmin Zheng, Xinsheng Fan, Chuanqing Li, Jianpeng Li, Duolun He and Renchuan Zhu

J. Mar. Sci. Eng. 2025, 13(8), 1488; https://doi.org/10.3390/jmse13081488 (registering DOI) - 1 Aug 2025

Abstract

The frequency-domain free-surface Green’s function method is widely used in solving ship hydrodynamic problems, with its core challenge lying in the computation of the Green’s function and its partial derivatives. This study analyzes the relationship between the free-surface Green’s function and its derivatives, [...] Read more.

The frequency-domain free-surface Green’s function method is widely used in solving ship hydrodynamic problems, with its core challenge lying in the computation of the Green’s function and its partial derivatives. This study analyzes the relationship between the free-surface Green’s function and its derivatives, proposing a machine learning-based recursive prediction method termed the pulsating source recursive prediction method. The accuracy and efficiency of this method under various parameter settings are investigated, and its application to the hydrodynamic calculations of container ship S175 and a bulk carrier is demonstrated. Results show that the predicted Green’s function achieves an accuracy of 3–6 decimals, with computational efficiency surpassing numerical methods and matching analytical approaches. The hydrodynamic results are reliable, confirming the method’s practical value. Full article

(This article belongs to the Special Issue Advancements in Marine Hydrodynamics and Structural Optimization)

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48 pages, 2506 KiB

Open AccessArticle

Enhancing Ship Propulsion Efficiency Predictions with Integrated Physics and Machine Learning

by Hamid Reza Soltani Motlagh, Seyed Behbood Issa-Zadeh, Md Redzuan Zoolfakar and Claudia Lizette Garay-Rondero

J. Mar. Sci. Eng. 2025, 13(8), 1487; https://doi.org/10.3390/jmse13081487 - 31 Jul 2025

Abstract

This research develops a dual physics-based machine learning system to forecast fuel consumption and CO₂ emissions for a 100 m oil tanker across six operational scenarios: Original, Paint, Advanced Propeller, Fin, Bulbous Bow, and Combined. The combination of hydrodynamic calculations with Monte [...] Read more.

This research develops a dual physics-based machine learning system to forecast fuel consumption and CO₂ emissions for a 100 m oil tanker across six operational scenarios: Original, Paint, Advanced Propeller, Fin, Bulbous Bow, and Combined. The combination of hydrodynamic calculations with Monte Carlo simulations provides a solid foundation for training machine learning models, particularly in cases where dataset restrictions are present. The XGBoost model demonstrated superior performance compared to Support Vector Regression, Gaussian Process Regression, Random Forest, and Shallow Neural Network models, achieving near-zero prediction errors that closely matched physics-based calculations. The physics-based analysis demonstrated that the Combined scenario, which combines hull coatings with bulbous bow modifications, produced the largest fuel consumption reduction (5.37% at 15 knots), followed by the Advanced Propeller scenario. The results demonstrate that user inputs (e.g., engine power: 870 kW, speed: 12.7 knots) match the Advanced Propeller scenario, followed by Paint, which indicates that advanced propellers or hull coatings would optimize efficiency. The obtained insights help ship operators modify their operational parameters and designers select essential modifications for sustainable operations. The model maintains its strength at low speeds, where fuel consumption is minimal, making it applicable to other oil tankers. The hybrid approach provides a new tool for maritime efficiency analysis, yielding interpretable results that support International Maritime Organization objectives, despite starting with a limited dataset. The model requires additional research to enhance its predictive accuracy using larger datasets and real-time data collection, which will aid in achieving global environmental stewardship. Full article

(This article belongs to the Special Issue Machine Learning for Prediction of Ship Motion)

37 pages, 10062 KiB

Open AccessArticle

A Preliminary Assessment of Offshore Winds at the Potential Organized Development Areas of the Greek Seas Using CERRA Dataset

by Takvor Soukissian, Natalia-Elona Koutri, Flora Karathanasi, Kimon Kardakaris and Aristofanis Stefatos

J. Mar. Sci. Eng. 2025, 13(8), 1486; https://doi.org/10.3390/jmse13081486 - 31 Jul 2025

Abstract

Τhe Greek Seas are one of the most favorable locations for offshore wind energy development in the Mediterranean basin. In 2023, the Hellenic Hydrocarbons & Energy Resources Management Company SA published the draft National Offshore Wind Farm Development Programme (NDP-OWF), including the main [...] Read more.

Τhe Greek Seas are one of the most favorable locations for offshore wind energy development in the Mediterranean basin. In 2023, the Hellenic Hydrocarbons & Energy Resources Management Company SA published the draft National Offshore Wind Farm Development Programme (NDP-OWF), including the main pillars for the design, development, siting, installation, and exploitation of offshore wind farms, along with the Strategic Environmental Impact Assessment. The NDP-OWF is under assessment by the relevant authorities and is expected to be finally approved through a Joint Ministerial Decision. In this work, the preliminary offshore wind energy assessment of the Greek Seas is performed using the CERRA wind reanalysis data and in situ measurements from six offshore locations of the Greek Seas. The in situ measurements are used in order to assess the performance of the reanalysis datasets. The results reveal that CERRA is a reliable source for preliminary offshore wind energy assessment studies. Taking into consideration the potential offshore wind farm organized development areas (OWFODA) according to the NDP-OWF, the study of the local wind characteristics is performed. The local wind speed and wind power density are assessed, and the wind energy produced from each OWFODA is estimated based on three different capacity density settings. According to the balanced setting (capacity density of 5.0 MW/km²), the annual energy production will be 17.5 TWh, which is equivalent to 1509.1 ktoe. An analysis of the wind energy correlation, synergy, and complementarity between the OWFODA is also performed, and a high degree of wind energy synergy is identified, with a very low degree of complementarity. Full article

(This article belongs to the Section Marine Energy)

18 pages, 723 KiB

Open AccessArticle

A Machine Learning-Based Model for Predicting High Deficiency Risk Ships in Port State Control: A Case Study of the Port of Singapore

by Ming-Cheng Tsou

J. Mar. Sci. Eng. 2025, 13(8), 1485; https://doi.org/10.3390/jmse13081485 - 31 Jul 2025

Abstract

This study developed a model to predict ships with high deficiency risk under Port State Control (PSC) through machine learning techniques, particularly the Random Forest algorithm. The study utilized actual ship inspection data from the Port of Singapore, comprehensively considering various operational and [...] Read more.

This study developed a model to predict ships with high deficiency risk under Port State Control (PSC) through machine learning techniques, particularly the Random Forest algorithm. The study utilized actual ship inspection data from the Port of Singapore, comprehensively considering various operational and safety indicators of ships, including but not limited to flag state, ship age, past deficiencies, and detention history. By analyzing these factors in depth, this research enhances the efficiency and accuracy of PSC inspections, provides decision support for port authorities, and offers strategic guidance for shipping companies to comply with international safety standards. During the research process, I first conducted detailed data preprocessing, including data cleaning and feature selection, to ensure the effectiveness of model training. Using the Random Forest algorithm, I identified key factors influencing the detention risk of ships and established a risk prediction model accordingly. The model validation results indicated that factors such as ship age, tonnage, company performance, and flag state significantly affect whether a ship exhibits a high deficiency rate. In addition, this study explored the potential and limitations of applying the Random Forest model in predicting high deficiency risk under PSC, and proposed future research directions, including further model optimization and the development of real-time prediction systems. By achieving these goals, I hope to provide valuable experience for other global shipping hubs, promote higher international maritime safety standards, and contribute to the sustainable development of the global shipping industry. This research not only highlights the importance of machine learning in the maritime domain but also demonstrates the potential of data-driven decision-making in improving ship safety management and port inspection efficiency. It is hoped that this study will inspire more maritime practitioners and researchers to explore advanced data analytics techniques to address the increasingly complex challenges of global shipping. Full article

(This article belongs to the Topic Digital Technologies in Supply Chain Risk Management)

31 pages, 3379 KiB

Open AccessReview

The Adoption of Technological Innovations in the Maritime Industry: A Bibliometric Review

by Armand Djoumessi, Alessio Tei and Claudio Ferrari

J. Mar. Sci. Eng. 2025, 13(8), 1484; https://doi.org/10.3390/jmse13081484 - 31 Jul 2025

Abstract

The adoption of technological innovations in the maritime industry is of interest to business, policy, and academic communities. In the last group, this interest has translated into the publication of a large but scattered literature, making it difficult to compare findings and identify [...] Read more.

The adoption of technological innovations in the maritime industry is of interest to business, policy, and academic communities. In the last group, this interest has translated into the publication of a large but scattered literature, making it difficult to compare findings and identify the dynamics, structures, and patterns that might inform future research. A comprehensive review of past research on this topic might help achieve this. To date, no such review has been carried out, which is an important gap in the literature that this paper contributes to bridging. Two bibliometric review techniques—co-citation analysis of cited references and bibliographic coupling of documents—are applied to 171 journal articles published between 1999 and February 2025 to answer the following questions: 1. What is the knowledge base of this literature? 2. What are the recent research trends (research fronts) in this literature? The analysis reveals that research on “shore power” dominates both the knowledge base and research fronts. Other key research themes centre on “autonomous shipping”, “blockchain”, and “alternative fuels”. Based on these results, implications for future research are drawn. Full article

(This article belongs to the Special Issue Sustainable and Efficient Maritime Operations)

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25 pages, 25022 KiB

Open AccessArticle

Research on Underwater Laser Communication Channel Attenuation Model Analysis and Calibration Device

by Wenyu Cai, Hengmei Wang, Meiyan Zhang and Yu Wang

J. Mar. Sci. Eng. 2025, 13(8), 1483; https://doi.org/10.3390/jmse13081483 - 31 Jul 2025

Abstract

To investigate the influence of different water quality conditions on the underwater transmission performance of laser communication signals, this paper systematically analyzes the absorption and scattering characteristics of the underwater laser communication channel, and constructs a transmission model of laser propagation in water, [...] Read more.

To investigate the influence of different water quality conditions on the underwater transmission performance of laser communication signals, this paper systematically analyzes the absorption and scattering characteristics of the underwater laser communication channel, and constructs a transmission model of laser propagation in water, so as to explore the transmission influence mechanism under typical water quality environments. On this basis, a system of in situ measurements for underwater laser channel attenuation is designed and constructed, and several sets of experiments are carried out to verify the rationality and applicability of the model. The collected experimental data are denoised by the fusion of wavelet analysis and adaptive Kalman filtering (DWT-AKF in short) algorithm, and compared with the data measured by an underwater hyperspectral Absorption Coefficient Spectrophotometer (ACS in short), which shows that the channel attenuation coefficients of the model inversion and the measured values are in high agreement. The research results provide a reliable theoretical basis and experimental support for the performance optimization and engineering design of the underwater laser communication system. Full article

(This article belongs to the Section Ocean Engineering)

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18 pages, 1643 KiB

Open AccessArticle

Precise Tracking Control of Unmanned Surface Vehicles for Maritime Sports Course Teaching Assistance

by Wanting Tan, Lei Liu and Jiabao Zhou

J. Mar. Sci. Eng. 2025, 13(8), 1482; https://doi.org/10.3390/jmse13081482 - 31 Jul 2025

Abstract

With the rapid advancement of maritime sports, the integration of auxiliary unmanned surface vehicles (USVs) has emerged as a promising solution to enhance the efficiency and safety of maritime education, particularly in tasks such as buoy deployment and escort operations. This paper presents [...] Read more.

With the rapid advancement of maritime sports, the integration of auxiliary unmanned surface vehicles (USVs) has emerged as a promising solution to enhance the efficiency and safety of maritime education, particularly in tasks such as buoy deployment and escort operations. This paper presents a novel high-precision trajectory tracking control algorithm designed to ensure stable navigation of the USVs along predefined competition boundaries, thereby facilitating the reliable execution of buoy placement and escort missions. First, the paper proposes an improved adaptive fractional-order nonsingular fast terminal sliding mode control (AFONFTSMC) algorithm to achieve precise trajectory tracking of the reference path. To address the challenges posed by unknown environmental disturbances and unmodeled dynamics in marine environments, a nonlinear lumped disturbance observer (NLDO) with exponential convergence properties is proposed, ensuring robust and continuous navigation performance. Additionally, an artificial potential field (APF) method is integrated to dynamically mitigate collision risks from both static and dynamic obstacles during trajectory tracking. The efficacy and practical applicability of the proposed control framework are rigorously validated through comprehensive numerical simulations. Experimental results demonstrate that the developed algorithm achieves superior trajectory tracking accuracy under complex sea conditions, thereby offering a reliable and efficient solution for maritime sports education and related applications. Full article

(This article belongs to the Section Ocean Engineering)

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21 pages, 4761 KiB

Open AccessArticle

Enhanced Dynamic Game Method for Offshore Wind Turbine Airfoil Optimization Design

by Rui Meng, Jintao Song, Xueqing Ren and Xuhui Chen

J. Mar. Sci. Eng. 2025, 13(8), 1481; https://doi.org/10.3390/jmse13081481 - 31 Jul 2025

Abstract

The novel enhanced dynamic game method (EDGM) is proposed to advance game-based design approaches, with a focus on enhancing solution distribution, precision, and the ability to reveal the dynamic influence sensitivity of design variables on objective functions. An integrated mathematical model is developed [...] Read more.

The novel enhanced dynamic game method (EDGM) is proposed to advance game-based design approaches, with a focus on enhancing solution distribution, precision, and the ability to reveal the dynamic influence sensitivity of design variables on objective functions. An integrated mathematical model is developed by combining EDGM with PARSEC and CST parameterization methods, forming a systematic framework for offshore wind turbine airfoil optimization. Targeting airfoils with approximately 30% and 35% thickness, the study aims to improve annual energy production (AEP) and optimize the polar moment of inertia. Redesigned airfoils using the EDGM-integrated model exhibit significant enhancements in aerodynamic performance and anti-flutter capability compared to baseline airfoils DU97W300 and DU99W350. The methodology’s superiority is validated through analyses of pressure distributions, lift-to-drag ratios, and streamline patterns, as well as comparative evaluations using HV and Spacing metrics, demonstrating EDGM’s potential for broader engineering applications in complex multi-objective optimization scenarios. Full article

(This article belongs to the Section Coastal Engineering)

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4 pages, 164 KiB

Open AccessEditorial

Interaction Between Atmospheric and Oceanic Dynamics at Mesoscale and Small Scale

by Weizeng Shao and Jian Shi

J. Mar. Sci. Eng. 2025, 13(8), 1480; https://doi.org/10.3390/jmse13081480 - 31 Jul 2025

Abstract

The exchange of momentum and heat between ocean dynamic processes at meso- and small scales is an interesting issue for the oceanography community [...] Full article

(This article belongs to the Special Issue Interaction between Atmospheric and Oceanic Dynamics at Mesoscale and Small Scale)

21 pages, 2799 KiB

Open AccessArticle

Structural Integrity Assessments of an IMO Type C LCO₂ Cargo Tank

by Joon Kim, Kyu-Sik Park, Inhwan Cha and Joonmo Choung

J. Mar. Sci. Eng. 2025, 13(8), 1479; https://doi.org/10.3390/jmse13081479 - 31 Jul 2025

Abstract

With the rise of carbon capture and storage, liquefied carbon dioxide (LCO₂) has emerged as a promising medium for large-scale marine transport. This study evaluates the structural integrity of an IMO Type C cargo tank for a medium-range LCO₂ carrier [...] Read more.

With the rise of carbon capture and storage, liquefied carbon dioxide (LCO₂) has emerged as a promising medium for large-scale marine transport. This study evaluates the structural integrity of an IMO Type C cargo tank for a medium-range LCO₂ carrier under four conditions: ultimate limit state, accidental limit state, hydrostatic pressure test, and fatigue limit state, based on IGC Code and classification rules. Seventeen load cases were analyzed using finite element methods with multi-step loading to ensure stability. The highest stress occurred at the pump dome–shell junction due to geometric discontinuities, but all stress and buckling criteria were satisfied. The fatigue damage from wave-induced loads was negligible, with low-cycle fatigue from loading/unloading operations governing the fatigue life, which exceeded 31,000 years. The findings confirm the tank’s structural robustness and its suitability for safe, efficient medium-pressure LCO₂ transport. Full article

(This article belongs to the Special Issue New Advances in the Analysis and Design of Marine Structures)

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21 pages, 16422 KiB

Open AccessArticle

DCE-Net: An Improved Method for Sonar Small-Target Detection Based on YOLOv8

by Lijun Cao, Zhiyuan Ma, Qiuyue Hu, Zhongya Xia and Meng Zhao

J. Mar. Sci. Eng. 2025, 13(8), 1478; https://doi.org/10.3390/jmse13081478 - 31 Jul 2025

Abstract

Sonar is the primary tool used for detecting small targets at long distances underwater. Due to the influence of the underwater environment and imaging mechanisms, sonar images face challenges such as a small number of target pixels, insufficient data samples, and uneven category [...] Read more.

Sonar is the primary tool used for detecting small targets at long distances underwater. Due to the influence of the underwater environment and imaging mechanisms, sonar images face challenges such as a small number of target pixels, insufficient data samples, and uneven category distribution. Existing target detection methods are unable to effectively extract features from sonar images, leading to high false positive rates and affecting the accuracy of target detection models. To counter these challenges, this paper presents a novel sonar small-target detection framework named DCE-Net that refines the YOLOv8 architecture. The Detail Enhancement Attention Block (DEAB) utilizes multi-scale residual structures and channel attention mechanism (AM) to achieve image defogging and small-target structure completion. The lightweight spatial variation convolution module (CoordGate) reduces false detections in complex backgrounds through dynamic position-aware convolution kernels. The improved efficient multi-scale AM (MH-EMA) performs scale-adaptive feature reweighting and combines cross-dimensional interaction strategies to enhance pixel-level feature representation. Experiments on a self-built sonar small-target detection dataset show that DCE-Net achieves an mAP@0.5 of 87.3% and an mAP@0.5:0.95 of 41.6%, representing improvements of 5.5% and 7.7%, respectively, over the baseline YOLOv8. This demonstrates that DCE-Net provides an efficient solution for underwater detection tasks. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Underwater Sonar Images)

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25 pages, 21958 KiB

Open AccessArticle

ESL-YOLO: Edge-Aware Side-Scan Sonar Object Detection with Adaptive Quality Assessment

by Zhanshuo Zhang, Changgeng Shuai, Chengren Yuan, Buyun Li, Jianguo Ma and Xiaodong Shang

J. Mar. Sci. Eng. 2025, 13(8), 1477; https://doi.org/10.3390/jmse13081477 - 31 Jul 2025

Abstract

Focusing on the problem of insufficient detection accuracy caused by blurred target boundaries, variable scales, and severe noise interference in side-scan sonar images, this paper proposes a high-precision detection network named ESL-YOLO, which integrates edge perception and adaptive quality assessment. Firstly, an Edge [...] Read more.

Focusing on the problem of insufficient detection accuracy caused by blurred target boundaries, variable scales, and severe noise interference in side-scan sonar images, this paper proposes a high-precision detection network named ESL-YOLO, which integrates edge perception and adaptive quality assessment. Firstly, an Edge Fusion Module (EFM) is designed, which integrates the Sobel operator into depthwise separable convolution. Through a dual-branch structure, it realizes effective fusion of edge features and spatial features, significantly enhancing the ability to recognize targets with blurred boundaries. Secondly, a Self-Calibrated Dual Attention (SCDA) Module is constructed. By means of feature cross-calibration and multi-scale channel attention fusion mechanisms, it achieves adaptive fusion of shallow details and deep-rooted semantic content, improving the detection accuracy for small-sized targets and targets with elaborate shapes. Finally, a Location Quality Estimator (LQE) is introduced, which quantifies localization quality using the statistical characteristics of bounding box distribution, effectively reducing false detections and missed detections. Experiments on the SIMD dataset show that the mAP@0.5 of ESL-YOLO reaches 84.65%. The precision and recall rate reach 87.67% and 75.63%, respectively. Generalization experiments on additional sonar datasets further validate the effectiveness of the proposed method across different data distributions and target types, providing an effective technical solution for side-scan sonar image target detection. Full article

(This article belongs to the Section Ocean Engineering)

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21 pages, 3353 KiB

Open AccessArticle

Automated Machine Learning-Based Significant Wave Height Prediction for Marine Operations

by Yuan Zhang, Hao Wang, Bo Wu, Jiajing Sun, Mingli Fan, Shu Dai, Hengyi Yang and Minyi Xu

J. Mar. Sci. Eng. 2025, 13(8), 1476; https://doi.org/10.3390/jmse13081476 - 31 Jul 2025

Abstract

Determining/predicting the environment dominates a variety of marine operations, such as route planning and offshore installation. Significant wave height (Hs) is a critical parameter-defining wave, a dominating marine load. Data-driven machine learning methods have been increasingly applied to Hs prediction, but challenges remain [...] Read more.

Determining/predicting the environment dominates a variety of marine operations, such as route planning and offshore installation. Significant wave height (Hs) is a critical parameter-defining wave, a dominating marine load. Data-driven machine learning methods have been increasingly applied to Hs prediction, but challenges remain in hyperparameter tuning and spatial generalization. This study explores a novel effective approach for intelligent Hs forecasting for marine operations. Multiple automated machine learning (AutoML) frameworks, namely H2O, PyCaret, AutoGluon, and TPOT, have been systematically evaluated on buoy-based Hs prediction tasks, which reveal their advantages and limitations under various forecast horizons and data quality scenarios. The results indicate that PyCaret achieves superior accuracy in short-term forecasts, while AutoGluon demonstrates better robustness in medium-term and long-term predictions. To address the limitations of single-point prediction models, which often exhibit high dependence on localized data and limited spatial generalization, a multi-point data fusion framework incorporating Principal Component Analysis (PCA) is proposed. The framework utilizes Hs data from two stations near the California coast to predict Hs at another adjacent station. The results indicate that it is possible to realize cross-station predictions based on the data from adjacent (high relevance) stations. Full article

(This article belongs to the Section Physical Oceanography)

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Journal Description

Journal of Marine Science and Engineering

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