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Volume 13
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J. Mar. Sci. Eng., Volume 13, Issue 7 (July 2025) – 164 articles

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13 pages, 1434 KiB  
Article
Intra-Seasonal Acoustic Variation in Humpback Whale Songs in the North Colombian Pacific
by Juliana López-Marulanda and Hector Fabio Rivera-Gutierrez
J. Mar. Sci. Eng. 2025, 13(7), 1360; https://doi.org/10.3390/jmse13071360 (registering DOI) - 17 Jul 2025
Abstract
Humpback whales (Megaptera novaeangliae) are well known for their complex acoustic communication, which plays a critical role in social interactions and reproduction. Understanding the variability in humpback whale songs is crucial to deciphering their communication strategies and the factors that influence [...] Read more.
Humpback whales (Megaptera novaeangliae) are well known for their complex acoustic communication, which plays a critical role in social interactions and reproduction. Understanding the variability in humpback whale songs is crucial to deciphering their communication strategies and the factors that influence these changes, which may affect reproductive success and population dynamics. While most studies of humpback whale song behavior have focused on annual variation, intra-seasonal changes remain underexplored. This study investigates intra-seasonal song variation in the Colombian Pacific humpback whale population, a unique and diverse breeding stock. We analyzed 37 h of recordings collected during two distinct periods of the 2019 breeding season (July and August–September) in the northern Colombian Pacific. Song repertoires were compared between periods, and the acoustic structure of a common song unit (Unit1) was analyzed using spectrographic cross-correlation. Results revealed a decrease in repertoire diversity over the course of the season, along with an increase in the song rate and the acoustic consistency of Unit1 during the second period. These findings highlight the dynamic nature of humpback whale song production and suggest potential influences of social learning and hormonal modulation. Such insights may be useful for the conservation and monitoring of humpback whale populations in breeding areas. Full article
(This article belongs to the Special Issue Recent Advances in Marine Bioacoustics)
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32 pages, 5641 KiB  
Review
Review of the Research on Underwater Explosion Ice-Breaking Technology
by Xiao Huang, Zi-Xian Zhong, Xiao Luo and Yuan-Dong Wang
J. Mar. Sci. Eng. 2025, 13(7), 1359; https://doi.org/10.3390/jmse13071359 (registering DOI) - 17 Jul 2025
Abstract
Underwater explosion ice-breaking technology is critical for Arctic development and ice disaster prevention due to its high efficiency, yet it faces challenges in understanding the coupled dynamics of shock waves, pulsating bubbles, and heterogeneous ice fracture. This review synthesizes theoretical models, experimental studies, [...] Read more.
Underwater explosion ice-breaking technology is critical for Arctic development and ice disaster prevention due to its high efficiency, yet it faces challenges in understanding the coupled dynamics of shock waves, pulsating bubbles, and heterogeneous ice fracture. This review synthesizes theoretical models, experimental studies, and numerical simulations investigating damage mechanisms. Key findings establish that shock waves initiate brittle fracture via stress superposition while bubble pulsation drives crack propagation through pressure oscillation; optimal ice fragmentation depends critically on charge weight, standoff distance, and ice thickness. However, significant limitations persist in modeling sea ice heterogeneity, experimental replication of polar conditions, and computational efficiency. Future advancements require multiscale fluid–structure interaction models integrating brine migration effects, enhanced experimental diagnostics for transient processes, and optimized numerical algorithms to enable reliable predictions for engineering applications. Full article
(This article belongs to the Section Ocean Engineering)
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17 pages, 4656 KiB  
Article
Improved Super-Twisting Sliding Mode Control of a Brushless Doubly Fed Induction Generator for Standalone Ship Shaft Power Generation Systems
by Xueran Fei, Minghao Zhou, Yingyi Jiang, Longbin Jiang, Yi Liu and Yan Yan
J. Mar. Sci. Eng. 2025, 13(7), 1358; https://doi.org/10.3390/jmse13071358 (registering DOI) - 17 Jul 2025
Abstract
This study proposes an improved super-twisting sliding mode (STSM) control method for a brushless doubly fed induction generator (BDFIG) used in standalone ship shaft power generation systems. Focusing on the problem of the low tracking accuracy of the power winding (PW) voltages caused [...] Read more.
This study proposes an improved super-twisting sliding mode (STSM) control method for a brushless doubly fed induction generator (BDFIG) used in standalone ship shaft power generation systems. Focusing on the problem of the low tracking accuracy of the power winding (PW) voltages caused by the parameter perturbation of BDFIG systems, a mismatched uncertain model of the BDFIG is constructed. Additionally, an improved STSM control method is proposed to address the power load variation and compensate for the mismatched uncertainty through virtual control technology. Based on the direct vector control of the control winding (CW), the proposed method ensured that the voltage amplitude error of the power winding could converge to the equilibrium point rather than the neighborhood. Finally, in the experimental investigation of the BDFIG-based ship shaft independent power system, the dynamic performance in the startup and power load changing conditions were analyzed. The experimental results show that the proposed improved STSM controller has a faster dynamic response and higher steady-state accuracy than the proportional integral control and the linear sliding mode control, with strong robustness to the mismatched uncertainties caused by parameter perturbations. Full article
(This article belongs to the Special Issue Control and Optimization of Ship Propulsion System)
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25 pages, 12171 KiB  
Article
Multi-Strategy Fusion Path Planning Algorithm for Autonomous Surface Vessels with Dynamic Obstacles
by Yongshun Xie, Chengyong Liu, Yixiong He, Yong Ma and Kang Liu
J. Mar. Sci. Eng. 2025, 13(7), 1357; https://doi.org/10.3390/jmse13071357 (registering DOI) - 17 Jul 2025
Abstract
Considering the complexity and variability inherent in maritime environments, path planning algorithms for navigation have consistently been a subject of intense research interest. Nonetheless, single-algorithm approaches often exhibit inherent limitations. Consequently, this study introduces a path planning algorithm for autonomous surface vessels (ASVs) [...] Read more.
Considering the complexity and variability inherent in maritime environments, path planning algorithms for navigation have consistently been a subject of intense research interest. Nonetheless, single-algorithm approaches often exhibit inherent limitations. Consequently, this study introduces a path planning algorithm for autonomous surface vessels (ASVs) that integrates an improved fast marching method (FMM) with the dynamic window approach (DWA) for underactuated ASVs. The enhanced FMM improves the overall optimality and safety of the determined path in comparison to the conventional approach. Concurrently, it effectively merges the local planning strengths of the DWA algorithm, addressing the safety re-planning needs of the global path when encountering dynamic obstacles, thus augmenting path tracking accuracy and navigational stability. The efficient hybrid algorithm yields notable improvements in the path planning success rate, obstacle avoidance efficacy, and path smoothness compared with the isolated employment of either FMM or DWA, demonstrating superiority and practical applicability in maritime scenarios. Through a comprehensive analysis of its control output, the proposed integrated algorithm accomplishes efficient obstacle avoidance via agile control of angular velocity while preserving navigational stability and achieves path optimization through consistent acceleration adjustments, thereby asserting its superiority and practical worth in challenging maritime environments. Full article
(This article belongs to the Section Ocean Engineering)
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26 pages, 8299 KiB  
Article
Experimental and Numerical Study on the Temperature Rise Characteristics of Multi-Layer Winding Non-Metallic Armored Optoelectronic Cable
by Shanying Lin, Xihong Kuang, Yujie Zhang, Gen Li, Wenhua Li and Weiwei Shen
J. Mar. Sci. Eng. 2025, 13(7), 1356; https://doi.org/10.3390/jmse13071356 - 16 Jul 2025
Abstract
The non-metallic armored optoelectronic cable (NAOC) serves as a critical component in deep-sea scientific winch systems. Due to its low density and excellent corrosion resistance, it has been widely adopted in marine exploration. However, as the operational water depth increases, the NAOC is [...] Read more.
The non-metallic armored optoelectronic cable (NAOC) serves as a critical component in deep-sea scientific winch systems. Due to its low density and excellent corrosion resistance, it has been widely adopted in marine exploration. However, as the operational water depth increases, the NAOC is subjected to multi-layer winding on the drum, resulting in a cumulative temperature rise that can severely impair insulation performance and compromise the safety of deep-sea operations. To address this issue, this paper conducts temperature rise experiments on NAOCs using a distributed temperature sensing test rig to investigate the effects of the number of winding layers and current amplitude on their temperature rise characteristics. Based on the experimental results, an electromagnetic thermal multi-physics field coupling simulation model is established to further examine the influence of these factors on the maximum operation time of the NAOC. Finally, a multi-variable predictive model for maximum operation time is developed, incorporating current amplitude, the number of winding layers, and ambient temperature, with a fitting accuracy of 97.92%. This research provides theoretical and technical support for ensuring the safety of deep-sea scientific operations and improving the reliability of deep-sea equipment. Full article
(This article belongs to the Section Ocean Engineering)
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30 pages, 3959 KiB  
Article
Hindcasting Extreme Significant Wave Heights Under Fetch-Limited Conditions with Tree-Based Models
by Damjan Bujak, Hanna Miličević, Goran Lončar and Dalibor Carević
J. Mar. Sci. Eng. 2025, 13(7), 1355; https://doi.org/10.3390/jmse13071355 - 16 Jul 2025
Abstract
Accurately hindcasting waves in semi-enclosed, fetch-limited basins remains challenging for reanalysis models, which tend to underestimate storm peaks near the coast. We developed interpretable ML models for Rijeka Bay (northern Adriatic) using only wind observations from two land-based wind stations to predict buoy [...] Read more.
Accurately hindcasting waves in semi-enclosed, fetch-limited basins remains challenging for reanalysis models, which tend to underestimate storm peaks near the coast. We developed interpretable ML models for Rijeka Bay (northern Adriatic) using only wind observations from two land-based wind stations to predict buoy Hm0 measurements spanning 2009–2011 (testing) and 2019–2021 (training and validation). The tested tree-based models included Random Forest, XGBoost, and Explainable Boosting Machine. This study introduces a novel approach in the literature by employing weighted schemes and feature engineering to enhance the predictive performance of interpretable, low-complexity machine learning models in hindcasting waves. Representing wind direction as sine–cosine components generally reduced RMSE and BIAS relative to traditional speed–direction inputs, while an exponential sample weight scheme that emphasized storm waves halved extreme Hm0 underprediction without inflating overall RMSE. The best-performing model, a Random Forest model, achieved an RMSE of 0.096 m and a correlation of 0.855 on the unseen test set—30% lower overall RMSE and 50% lower extreme wave RMSE than the MEDSEA and COEXMED hindcasts. Additionally, the underprediction was reduced by 90% compared to these reanalysis models. The method offers a computationally lightweight, transferable supplement to numerical wave guidance for coastal engineering and harbor operations. Full article
(This article belongs to the Special Issue Machine Learning in Coastal Engineering)
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23 pages, 15163 KiB  
Article
3D Dubins Curve-Based Path Planning for UUV in Unknown Environments Using an Improved RRT* Algorithm
by Feng Pan, Peng Cui, Bo Cui, Weisheng Yan and Shouxu Zhang
J. Mar. Sci. Eng. 2025, 13(7), 1354; https://doi.org/10.3390/jmse13071354 - 16 Jul 2025
Abstract
The autonomous navigation of an Unmanned Underwater Vehicle (UUV) in unknown 3D underwater environments remains a challenging task due to the presence of complex terrain, uncertain obstacles, and strict kinematic constraints. This paper proposes a novel smooth path planning framework that integrates improved [...] Read more.
The autonomous navigation of an Unmanned Underwater Vehicle (UUV) in unknown 3D underwater environments remains a challenging task due to the presence of complex terrain, uncertain obstacles, and strict kinematic constraints. This paper proposes a novel smooth path planning framework that integrates improved Rapidly-exploring Random Tree* (RRT*) with 3D Dubins curves to efficiently generate feasible and collision-free trajectories for nonholonomic UUVs. A fast curve-length estimation approach based on a backpropagation neural network is introduced to reduce computational burden during path evaluation. Furthermore, the improved RRT* algorithm incorporates pseudorandom sampling, terminal node backtracking, and goal-biased exploration strategies to enhance convergence and path quality. Extensive simulation results in unknown underwater scenarios with static and moving obstacles demonstrate that the proposed method significantly outperforms state-of-the-art planning algorithms in terms of smoothness, path length, and computational efficiency. Full article
(This article belongs to the Special Issue Intelligent Measurement and Control System of Marine Robots)
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18 pages, 3550 KiB  
Article
Monitoring and Assessment of the Trace Element Accumulation in the Polychaete Hediste diversicolor from Tunisian Coastal Localities (Southwest of Mediterranean Sea)
by Ali Annabi, Walid Ben Ameur, Nermine Akermi and Mauro Marini
J. Mar. Sci. Eng. 2025, 13(7), 1353; https://doi.org/10.3390/jmse13071353 - 16 Jul 2025
Abstract
The study of the impact of anthropogenic and natural pollution on living organisms has become a major social issue. In this context, the objective of this work is to assess the use of the polychaete annelid Hediste diversicolor as a bioindicator organism for [...] Read more.
The study of the impact of anthropogenic and natural pollution on living organisms has become a major social issue. In this context, the objective of this work is to assess the use of the polychaete annelid Hediste diversicolor as a bioindicator organism for the quality of the marine environment. The concentration of four heavy metals (lead, copper, zinc, and cadmium) was determined in natural populations of H. diversicolor captured from four locations along the Tunisian coast using atomic absorption spectroscopy. Concentration ranges (µg/g dry weight) across all sites were as follows: Cd (0.12–0.43), Cu (3.80–6.45), Zn (18.35–42.78), and Pb (22.64–63.91). Statistical analysis confirmed significant spatial variation (Pb: F = 12.15, p < 0.001; Zn: F = 3.32, p = 0.04; Cd: F = 48.66, p < 0.001; Cu: F = 9.08, p < 0.001), with peak Pb in Bizerte and Cu in Sfax. These results highlight the influence of local environmental factors, such as industrial and urban pollution on metal accumulation in Hediste diversicolor. In this study, the accumulation of the analyzed elements in the tissues of H. diversicolor follows an increasing order as follows: Cd < Cu < Zn < Pb. Additionally, lead metal concentrations were higher than those of cadmium, zinc, and copper for all four studied locations. To our knowledge, this is the first study in Tunisia to assess heavy metal accumulation in H. diversicolor. The recorded levels were similar to, or lower than, those reported in other studies worldwide. These findings underscore the potential of H. diversicolor as a sensitive and effective bioindicator for monitoring coastal contamination and guiding environmental management strategies in Tunisia. Full article
(This article belongs to the Topic Conservation and Management of Marine Ecosystems)
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36 pages, 8048 KiB  
Article
Characterization and Automated Classification of Underwater Acoustic Environments in the Western Black Sea Using Machine Learning Techniques
by Maria Emanuela Mihailov
J. Mar. Sci. Eng. 2025, 13(7), 1352; https://doi.org/10.3390/jmse13071352 - 16 Jul 2025
Abstract
Growing concern over anthropogenic underwater noise, highlighted by initiatives like the Marine Strategy Framework Directive (MSFD) and its Technical Group on Underwater Noise (TG Noise), emphasizes regions like the Western Black Sea, where increasing activities threaten marine habitats. This region is experiencing rapid [...] Read more.
Growing concern over anthropogenic underwater noise, highlighted by initiatives like the Marine Strategy Framework Directive (MSFD) and its Technical Group on Underwater Noise (TG Noise), emphasizes regions like the Western Black Sea, where increasing activities threaten marine habitats. This region is experiencing rapid growth in maritime traffic and resource exploitation, which is intensifying concerns over the noise impacts on its unique marine habitats. While machine learning offers promising solutions, a research gap persists in comprehensively evaluating diverse ML models within an integrated framework for complex underwater acoustic data, particularly concerning real-world data limitations like class imbalance. This paper addresses this by presenting a multi-faceted framework using passive acoustic monitoring (PAM) data from fixed locations (50–100 m depth). Acoustic data are processed using advanced signal processing (broadband Sound Pressure Level (SPL), Power Spectral Density (PSD)) for feature extraction (Mel-spectrograms for deep learning; PSD statistical moments for classical/unsupervised ML). The framework evaluates Convolutional Neural Networks (CNNs), Random Forest, and Support Vector Machines (SVMs) for noise event classification, alongside Gaussian Mixture Models (GMMs) for anomaly detection. Our results demonstrate that the CNN achieved the highest classification accuracy of 0.9359, significantly outperforming Random Forest (0.8494) and SVM (0.8397) on the test dataset. These findings emphasize the capability of deep learning in automatically extracting discriminative features, highlighting its potential for enhanced automated underwater acoustic monitoring. Full article
(This article belongs to the Section Ocean Engineering)
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26 pages, 9214 KiB  
Article
Fishing-Related Plastic Pollution on Bocassette Spit (Northern Adriatic): Distribution Patterns and Stakeholder Perspectives
by Corinne Corbau, Alexandre Lazarou and Umberto Simeoni
J. Mar. Sci. Eng. 2025, 13(7), 1351; https://doi.org/10.3390/jmse13071351 - 16 Jul 2025
Abstract
Plastic pollution in marine environments is a globally recognized concern that poses ecological and economic threats. While 80% of plastic originates from land, 20% comes from sea-based sources like shipping and fishing. Comprehensive assessments of fishing-related plastics are limited but crucial for mitigation. [...] Read more.
Plastic pollution in marine environments is a globally recognized concern that poses ecological and economic threats. While 80% of plastic originates from land, 20% comes from sea-based sources like shipping and fishing. Comprehensive assessments of fishing-related plastics are limited but crucial for mitigation. This study analyzed the distribution and temporal evolution of three fishing-related items (EPS fish boxes, fragments, and buoys) along the Bocassette spit in the northern Adriatic Sea, a region with high fishing and aquaculture activity. UAV monitoring (November 2019, June/October 2020) and structured interviews with Po Delta fishermen were conducted. The collected debris was mainly EPS, with boxes (54.8%) and fragments (39.6%). Fishermen showed strong awareness of degradation, identifying plastic as the primary litter type and reporting gear loss. Litter concentrated in active dunes and the southern sector indicates human and riverine influence. Persistent items (61%) at higher elevations suggest longer residence times. Mapped EPS boxes could generate billions of micro-particles (e.g., ~1013). The results reveal a complex interaction between natural processes and human activities in litter distribution. This highlights the need for integrated management strategies, like improved waste management, targeted cleanup, and community involvement, to reduce long-term impacts on vulnerable coastal ecosystems. Full article
(This article belongs to the Section Marine Environmental Science)
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21 pages, 14257 KiB  
Article
Shallow-Water Submarine Landslide Susceptibility Map: The Example in a Sector of Capo d’Orlando Continental Margin (Southern Tyrrhenian Sea)
by Elena Scacchia, Daniele Casalbore, Fabiano Gamberi, Daniele Spatola, Marco Bianchini and Francesco Latino Chiocci
J. Mar. Sci. Eng. 2025, 13(7), 1350; https://doi.org/10.3390/jmse13071350 - 16 Jul 2025
Abstract
Active continental margins, generally characterized by narrow shelves incised by canyons, are pervasively shaped by submarine landslides that can occur near coastal areas. In this context, creating landslide susceptibility maps is the first step in landslide geohazard assessment. This paper focuses on shallow-water [...] Read more.
Active continental margins, generally characterized by narrow shelves incised by canyons, are pervasively shaped by submarine landslides that can occur near coastal areas. In this context, creating landslide susceptibility maps is the first step in landslide geohazard assessment. This paper focuses on shallow-water submarine landslides along the Capo d’Orlando continental margin and presents a related susceptibility map using the Weight of Evidence method. This method quantifies the strength of the association between a landslide inventory and predisposing factors. A geomorphological analysis of the continental shelf and upper slope yielded a landslide inventory of 450 initiation points, which were combined with five specifically selected preconditioning factors. The results revealed that the most favourable conditions for shallow-water landslides include slopes between 5° and 15°, proximity to faults (<1 km), proximity to river mouths (<2 km), the presence of consolidated lithologies and sandy terraces, and slopes facing NE and E. The landslide susceptibility map indicates that susceptible areas are in canyon heads and flanks, as well as in undisturbed slope portions near canyon heads where retrogressive landslides are likely. The model results are robust (AUC = 0.88), demonstrating that this method can be effectively applied in areas with limited geological data for preliminary susceptibility assessments. Full article
(This article belongs to the Section Coastal Engineering)
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25 pages, 6057 KiB  
Article
Physical Implementation and Experimental Validation of the Compensation Mechanism for a Ramp-Based AUV Recovery System
by Zhaoji Qi, Lingshuai Meng, Haitao Gu, Ziyang Guo, Jinyan Wu and Chenghui Li
J. Mar. Sci. Eng. 2025, 13(7), 1349; https://doi.org/10.3390/jmse13071349 - 16 Jul 2025
Abstract
In complex marine environments, ramp-based recovery systems for autonomous underwater vehicles (AUVs) often encounter engineering challenges such as reduced docking accuracy and success rate due to disturbances in the capture window attitude. In this study, a desktop-scale physical experimental platform for recovery compensation [...] Read more.
In complex marine environments, ramp-based recovery systems for autonomous underwater vehicles (AUVs) often encounter engineering challenges such as reduced docking accuracy and success rate due to disturbances in the capture window attitude. In this study, a desktop-scale physical experimental platform for recovery compensation was designed and constructed. The system integrates attitude feedback provided by an attitude sensor and dual-motor actuation to achieve active roll and pitch compensation of the capture window. Based on the structural and geometric characteristics of the platform, a dual-channel closed-loop control strategy was proposed utilizing midpoint tracking of the capture window, accompanied by multi-level software limit protection and automatic centering mechanisms. The control algorithm was implemented using a discrete-time PID structure, with gain parameters optimized through experimental tuning under repeatable disturbance conditions. A first-order system approximation was adopted to model the actuator dynamics. Experiments were conducted under various disturbance scenarios and multiple control parameter configurations to evaluate the attitude tracking performance, dynamic response, and repeatability of the system. The results show that, compared to the uncompensated case, the proposed compensation mechanism reduces the MSE by up to 76.4% and the MaxAE by 73.5%, significantly improving the tracking accuracy and dynamic stability of the recovery window. The study also discusses the platform’s limitations and future optimization directions, providing theoretical and engineering references for practical AUV recovery operations. Full article
(This article belongs to the Section Coastal Engineering)
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18 pages, 12793 KiB  
Article
A Mainlobe Interference Suppression Method for Small Hydrophone Arrays
by Wenbo Wang, Ye Li, Luwen Meng, Tongsheng Shen and Dexin Zhao
J. Mar. Sci. Eng. 2025, 13(7), 1348; https://doi.org/10.3390/jmse13071348 - 16 Jul 2025
Abstract
In order to solve the problem of mainlobe interference in small hydroacoustic array signal processing, this paper proposes a beamforming method based on the high-resolution direction of arrival (DOA) estimation and interference coherence matrix (ICM) reconstruction. The DOA estimation is first performed using [...] Read more.
In order to solve the problem of mainlobe interference in small hydroacoustic array signal processing, this paper proposes a beamforming method based on the high-resolution direction of arrival (DOA) estimation and interference coherence matrix (ICM) reconstruction. The DOA estimation is first performed using an improved sparse iterative covariance-based (SPICE) method, unaffected by the coherent signal, and it can provide highly accurate DOA estimation for multiple targets. The fitted signal energy distribution obtained from the SPICE is then utilized for the reconstruction of the signal coherence matrix. The reconstructed ICM matrix is used to construct a blocking masking matrix and an eigen-projection matrix to suppress the mainlobe interference signal. Compared with existing methods, the method in this paper possesses better mainlobe interference suppression ability. Within the mainlobe interference interval angle of 3° to 13.5° from the signal of interest (SOI) based on eight-element uniform linear arrays, the method in this paper can enhance the signal-to-interference ratio (SIR) by about 15.59 dB on average compared with the interference-free suppression of conventional beamforming (CBF) and outperforms the other interference suppression methods simultaneously. Simulations and experiments demonstrate the effectiveness of this method in mainlobe interference scenarios. Full article
(This article belongs to the Topic Advances in Underwater Signal Processing and Communication: Challenges, Innovations, and Applications)
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19 pages, 3225 KiB  
Article
Autonomous Tracking of Steel Lazy Wave Risers Using a Hybrid Vision–Acoustic AUV Framework
by Ali Ghasemi and Hodjat Shiri
J. Mar. Sci. Eng. 2025, 13(7), 1347; https://doi.org/10.3390/jmse13071347 - 15 Jul 2025
Viewed by 49
Abstract
Steel lazy wave risers (SLWRs) are critical in offshore hydrocarbon transport for linking subsea wells to floating production facilities in deep-water environments. The incorporation of buoyancy modules reduces curvature-induced stress concentrations in the touchdown zone (TDZ); however, extended operational exposure under cyclic environmental [...] Read more.
Steel lazy wave risers (SLWRs) are critical in offshore hydrocarbon transport for linking subsea wells to floating production facilities in deep-water environments. The incorporation of buoyancy modules reduces curvature-induced stress concentrations in the touchdown zone (TDZ); however, extended operational exposure under cyclic environmental and operational loads results in repeated seabed contact. This repeated interaction modifies the seabed soil over time, gradually forming a trench and altering the riser configuration, which significantly impacts stress patterns and contributes to fatigue degradation. Accurately reconstructing the riser’s evolving profile in the TDZ is essential for reliable fatigue life estimation and structural integrity evaluation. This study proposes a simulation-based framework for the autonomous tracking of SLWRs using a fin-actuated autonomous underwater vehicle (AUV) equipped with a monocular camera and multibeam echosounder. By fusing visual and acoustic data, the system continuously estimates the AUV’s relative position concerning the riser. A dedicated image processing pipeline, comprising bilateral filtering, edge detection, Hough transform, and K-means clustering, facilitates the extraction of the riser’s centerline and measures its displacement from nearby objects and seabed variations. The framework was developed and validated in the underwater unmanned vehicle (UUV) Simulator, a high-fidelity underwater robotics and pipeline inspection environment. Simulated scenarios included the riser’s dynamic lateral and vertical oscillations, in which the system demonstrated robust performance in capturing complex three-dimensional trajectories. The resulting riser profiles can be integrated into numerical models incorporating riser–soil interaction and non-linear hysteretic behavior, ultimately enhancing fatigue prediction accuracy and informing long-term infrastructure maintenance strategies. Full article
(This article belongs to the Section Ocean Engineering)
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23 pages, 2892 KiB  
Article
Investigation of Bolt Grade Influence on the Structural Integrity of L-Type Flange Joints Using Finite Element Analysis
by Muhammad Waleed and Daeyong Lee
J. Mar. Sci. Eng. 2025, 13(7), 1346; https://doi.org/10.3390/jmse13071346 - 15 Jul 2025
Viewed by 50
Abstract
Critical components in support structures for wind turbines, flange joints, are fundamental to ensure the structural integrity of mechanical assemblies under varying operational conditions. This paper investigates the structural performance of L-type flange joints, focusing on the influence of bolt grades and bolt [...] Read more.
Critical components in support structures for wind turbines, flange joints, are fundamental to ensure the structural integrity of mechanical assemblies under varying operational conditions. This paper investigates the structural performance of L-type flange joints, focusing on the influence of bolt grades and bolt pretension through a finite element analysis (FEA) study of its key performance indicators, including stress distribution, deformation, and force–displacement behaviors. This paper studies two high-strength bolt grades, Grade 10.9 and Grade 12.9, and two main steps—first, bolt pretension and, second, external loading (tower shell tensile load)—to investigate the influence on joint reliability and safety margins. The novelty of this study lies in its specific focus on static axial loading conditions, unlike the existing literature that emphasizes fatigue or dynamic loads. Results show that the specimen carrying a higher bolt grade (12.9) has 18% more ultimate load carrying capacity than the specimen with a lower bolt grade (10.9). Increased pretension increases the stability of the joint and reduces the micro-movements between A and B (on model specimen), but could result in material fatigue if over-pretensioned. Comparative analysis of the different bolt grades has provided practical guidance on material selection and bolt pretension in L-type flange joints for wind turbine support structures. The findings of this work offer insights into the proper design of robust flange connections for high-demand applications by highlighting a balance among material properties, bolt pretension, and operational conditions, while also proposing optimized pretension and material recommendations validated against classical analytical models. Full article
(This article belongs to the Section Ocean Engineering)
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21 pages, 1661 KiB  
Article
Performance Assessment of B-Series Marine Propellers with Cupping and Face Camber Ratio Using Machine Learning Techniques
by Mina Tadros and Evangelos Boulougouris
J. Mar. Sci. Eng. 2025, 13(7), 1345; https://doi.org/10.3390/jmse13071345 - 15 Jul 2025
Viewed by 55
Abstract
This study investigates the performance of B-series marine propellers enhanced through geometric modifications, namely face camber ratio (FCR) and cupping percentage modifications, using a machine learning (ML)-driven optimization framework. A large dataset of over 7000 open-water propeller configurations is curated, incorporating variations in [...] Read more.
This study investigates the performance of B-series marine propellers enhanced through geometric modifications, namely face camber ratio (FCR) and cupping percentage modifications, using a machine learning (ML)-driven optimization framework. A large dataset of over 7000 open-water propeller configurations is curated, incorporating variations in blade number, expanded area ratio (EAR), pitch-to-diameter ratio (P/D), FCR, and cupping percentage. A multi-layer artificial neural network (ANN) is trained to predict thrust, torque, and open-water efficiency (ηo) with a high coefficient of determination (R2), greater than 0.9999. The ANN is integrated into an optimization algorithm to identify optimal propeller designs for the KRISO Container Ship (KCS) using empirical constraints for cavitation and tip speed. Unlike prior studies that rely on boundary element method (BEM)-ML hybrids or multi-fidelity simulations, this study introduces a geometry-coupled analysis of FCR and cupping—parameters often treated independently—and applies empirical cavitation and acoustic (tip speed) limits to guide the design process. The results indicate that incorporating 1.0–1.5% cupping leads to a significant improvement in efficiency, up to 9.3% above the reference propeller, while maintaining cavitation safety margins and acoustic limits. Conversely, designs with non-zero FCR values (0.5–1.5%) show a modest efficiency penalty (up to 4.3%), although some configurations remain competitive when compensated by higher EAR, P/D, or blade count. The study confirms that the combination of cupping with optimized geometric parameters yields high-efficiency, cavitation-safe propellers. Furthermore, the ML-based framework demonstrates excellent potential for rapid, accurate, and scalable propeller design optimization that meets both performance and regulatory constraints. Full article
(This article belongs to the Special Issue Advanced Research on the Sustainable Maritime Transportation (2nd Edition))
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15 pages, 3145 KiB  
Article
Probabilistic Prediction of Spudcan Bearing Capacity in Stiff-over-Soft Clay Based on Bayes’ Theorem
by Zhaoyu Sun, Pan Gao, Yanling Gao, Jianze Bi and Qiang Gao
J. Mar. Sci. Eng. 2025, 13(7), 1344; https://doi.org/10.3390/jmse13071344 - 14 Jul 2025
Viewed by 106
Abstract
During offshore operations of jack-up platforms, the spudcan may experience sudden punch-through failure when penetrating from an overlying stiff clay layer into the underlying soft clay, posing significant risks to platform safety. Conventional punch-through prediction methods, which rely on predetermined soil parameters, exhibit [...] Read more.
During offshore operations of jack-up platforms, the spudcan may experience sudden punch-through failure when penetrating from an overlying stiff clay layer into the underlying soft clay, posing significant risks to platform safety. Conventional punch-through prediction methods, which rely on predetermined soil parameters, exhibit limited accuracy as they fail to account for uncertainties in seabed stratigraphy and soil properties. To address this limitation, based on a database of centrifuge model tests, a probabilistic prediction framework for the peak resistance and corresponding depth is developed by integrating empirical prediction formulas based on Bayes’ theorem. The proposed Bayesian methodology effectively refines prediction accuracy by quantifying uncertainties in soil parameters, spudcan geometry, and computational models. Specifically, it establishes prior probability distributions of peak resistance and depth through Monte Carlo simulations, then updates these distributions in real time using field monitoring data during spudcan penetration. The results demonstrate that both the recommended method specified in ISO 19905-1 and an existing deterministic model tend to yield conservative estimates. This approach can significantly improve the predicted accuracy of the peak resistance compared with deterministic methods. Additionally, it shows that the most probable failure zone converges toward the actual punch-through point as more monitoring data is incorporated. The enhanced prediction capability provides critical decision support for mitigating punch-through potential during offshore jack-up operations, thereby advancing the safety and reliability of marine engineering practices. Full article
(This article belongs to the Section Ocean Engineering)
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18 pages, 3853 KiB  
Article
Methyltrimethyltridecylchromans (MTTCs) in Mature Crude Oils: Implications for Oil Family Classification and Palaeoenvironmental Diagnosis
by Youjun Tang, Mengyue Han, Xiaoyong Yang, Ke Liu, Lian Chen, Yahao Mei, Yulu Han, Tianwu Xu and Chengfu Zhang
J. Mar. Sci. Eng. 2025, 13(7), 1343; https://doi.org/10.3390/jmse13071343 - 14 Jul 2025
Viewed by 93
Abstract
Methyltrimethyltridecylchromans (MTTCs), a class of oxygen-containing aromatic derivatives, have been used as indicators of paleosalinity in source rocks and crude oils. However, the reliability of these compounds as indicators in mature organic matter remains unclear, hindering a definitive assessment of their significance for [...] Read more.
Methyltrimethyltridecylchromans (MTTCs), a class of oxygen-containing aromatic derivatives, have been used as indicators of paleosalinity in source rocks and crude oils. However, the reliability of these compounds as indicators in mature organic matter remains unclear, hindering a definitive assessment of their significance for oil–oil or oil–source rock correlation. In this study, a suite of mature oils and associated source rocks from the Paleogene Shahejie (E2s) Formation in the Machang area, Dongpu Depression, Bohai Bay Basin, were analyzed. The distribution of bulk compositions and biomarkers in the oils and source rock extracts suggests a genetic relationship, indicating that the oils were derived from similar organic matter (predominantly algae and aquatic macrophytes) and depositional environments (low salinity), with comparable maturity levels (within the middle oil window). The β/γ-MTTC ratio, a proposed maturity indicator, appears unreliable in mature organic matter, as evidenced by its poor correlation with established maturity proxies (e.g., C29 24-ethylcholestanes αββ/(ααα + αββ)) in the studied samples. In contrast, MTTC-based salinity paraments (α/δ, α/γ, MTTCI, and the cross-plot of MTTCI versus Pr/Ph) consistently reflect a low-salinity depositional environment for these crude oils and source rocks, except in the ternary diagram of relative alkylation abundances. These findings suggest that MTTC-derived paleosalinity indicators may serve as effective tools for oil–oil or oil–source rock correlation within the middle oil window. This study provides evidence supporting the broader applicability of MTTC-based proxies for paleosalinity reconstruction and correlation studies, particularly in mature organic matter under geological conditions. The results also offer insights for regional petroleum exploration in saline lacustrine basins. Full article
(This article belongs to the Section Geological Oceanography)
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16 pages, 2652 KiB  
Article
Evaluation of the Effect of Floating Treatment Wetlands Planted with Sesuvium portulacastrum on the Dynamics of Dissolved Inorganic Nitrogen, CO2, and N2O in Grouper Aquaculture Systems
by Shenghua Zheng, Man Wu, Jian Liu, Wangwang Ye, Yongqing Lin, Miaofeng Yang, Huidong Zheng, Fang Yang, Donglian Luo and Liyang Zhan
J. Mar. Sci. Eng. 2025, 13(7), 1342; https://doi.org/10.3390/jmse13071342 - 14 Jul 2025
Viewed by 142
Abstract
Aquaculture expansion to meet global protein demand has intensified concerns over nutrient pollution and greenhouse gas (GHG) emissions. While floating treatment wetlands (FTWs) are proven for water quality improvement, their potential to mitigate GHG emissions in marine aquaculture remains poorly understood. This study [...] Read more.
Aquaculture expansion to meet global protein demand has intensified concerns over nutrient pollution and greenhouse gas (GHG) emissions. While floating treatment wetlands (FTWs) are proven for water quality improvement, their potential to mitigate GHG emissions in marine aquaculture remains poorly understood. This study quantitatively evaluated the dual capacity of Sesuvium portulacastrum FTWs to (a) regulate dissolved inorganic nitrogen (DIN) and (b) reduce CO2/N2O emissions in grouper aquaculture systems. DIN speciation (NH4+, NO2−, NO3−) and CO2/N2O fluxes of six controlled ponds (three FTW and three control) were monitored for 44 days. DIN in the FTW group was approximately 90 μmol/L lower than that in the control group, and the water in the plant group was more “oxidative” than that in the control group. The former groups were dominated by NO3−, with lower dissolved inorganic carbon (DIC) and N2O concentrations, whereas the latter were dominated by NH4+ during the first 20 days of the experiment and by NO2− at the end of the experiment, with higher DIC and N2O concentrations on average. Higher primary production may be the reason that the DIC concentration was lower in the plant group than in the control group, whereas efficient nitrification and uptake by plants reduced the availability of NH4+ in the plant group, thereby reducing the production of N2O. A comparison of the CO2 and N2O flux potentials in the plant group and control group revealed that, in the presence of FTWs, the CO2 and N2O emissions decreased by 14% and 36%, respectively. This showed that S. portulacastrum FTWs effectively couple DIN removal with GHG mitigation, offering a nature-based solution for sustainable aquaculture. Their low biomass requirement enhances practical scalability. Full article
(This article belongs to the Special Issue Coastal Geochemistry: The Processes of Water–Sediment Interaction)
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28 pages, 11429 KiB  
Article
Trajectory Tracking of Unmanned Surface Vessels Based on Robust Neural Networks and Adaptive Control
by Ziming Wang, Chunliang Qiu, Zaopeng Dong, Shaobo Cheng, Long Zheng and Shunhuai Chen
J. Mar. Sci. Eng. 2025, 13(7), 1341; https://doi.org/10.3390/jmse13071341 - 13 Jul 2025
Viewed by 123
Abstract
In this paper, a robust neural adaptive controller is proposed for the trajectory tracking control problem of unmanned surface vessels (USVs), considering model uncertainty, time-varying environmental disturbance, and actuator saturation. First, measurement errors in acceleration signals are eliminated through filtering techniques and a [...] Read more.
In this paper, a robust neural adaptive controller is proposed for the trajectory tracking control problem of unmanned surface vessels (USVs), considering model uncertainty, time-varying environmental disturbance, and actuator saturation. First, measurement errors in acceleration signals are eliminated through filtering techniques and a series of auxiliary variables, and after linearly parameterizing the USV dynamic model, a parameter adaptive update law is developed based on Lyapunov’s second method to estimate unknown dynamic parameters in the USV dynamics model. This parameter adaptive update law enables online identification of all USV dynamic parameters during trajectory tracking while ensuring convergence of the estimation errors. Second, a radial basis function neural network (RBF-NN) is employed to approximate unmodeled dynamics in the USV system, and on this basis, a robust damping term is designed based on neural damping technology to compensate for environmental disturbances and unmodeled dynamics. Subsequently, a trajectory tracking controller with parameter adaptation law and robust damping term is proposed using Lyapunov theory and adaptive control techniques. In addition, finite-time auxiliary variables are also added to the controller to handle the actuator saturation problem. Signal delay compensators are designed to compensate for input signal delays in the control system, thereby enhancing controller reliability. The proposed controller ensures robustness in trajectory tracking under model uncertainties and time-varying environmental disturbances. Finally, the convergence of each signal of the closed-loop system is proved based on Lyapunov theory. And the effectiveness of the control system is verified by numerical simulation experiments. Full article
(This article belongs to the Section Ocean Engineering)
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22 pages, 3279 KiB  
Article
HA-CP-Net: A Cross-Domain Few-Shot SAR Oil Spill Detection Network Based on Hybrid Attention and Category Perception
by Dongmei Song, Shuzhen Wang, Bin Wang, Weimin Chen and Lei Chen
J. Mar. Sci. Eng. 2025, 13(7), 1340; https://doi.org/10.3390/jmse13071340 - 13 Jul 2025
Viewed by 156
Abstract
Deep learning models have obvious advantages in detecting oil spills, but the training of deep learning models heavily depends on a large number of samples of high quality. However, due to the accidental nature, unpredictability, and urgency of oil spill incidents, it is [...] Read more.
Deep learning models have obvious advantages in detecting oil spills, but the training of deep learning models heavily depends on a large number of samples of high quality. However, due to the accidental nature, unpredictability, and urgency of oil spill incidents, it is difficult to obtain a large number of labeled samples in real oil spill monitoring scenarios. Surprisingly, few-shot learning can achieve excellent classification performance with only a small number of labeled samples. In this context, a new cross-domain few-shot SAR oil spill detection network is proposed in this paper. Significantly, the network is embedded with a hybrid attention feature extraction block, which consists of a coordinate attention module to perceive the channel information and spatial location information, as well as a global self-attention transformer module capturing the global dependencies and a multi-scale self-attention module depicting the local detailed features, thereby achieving deep mining and accurate characterization of image features. In addition, to address the problem that it is difficult to distinguish between the suspected oil film in seawater and real oil film using few-shot due to the small difference in features, this paper proposes a double loss function category determination block, which consists of two parts: a well-designed category-perception loss function and a traditional cross-entropy loss function. The category-perception loss function optimizes the spatial distribution of sample features by shortening the distance between similar samples while expanding the distance between different samples. By combining the category-perception loss function with the cross-entropy loss function, the network’s performance in discriminating between real and suspected oil films is thus maximized. The experimental results effectively demonstrate that this study provides an effective solution for high-precision oil spill detection under few-shot conditions, which is conducive to the rapid identification of oil spill accidents. Full article
(This article belongs to the Section Marine Environmental Science)
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29 pages, 1474 KiB  
Review
Berth Allocation and Quay Crane Scheduling in Port Operations: A Systematic Review
by Ndifelani Makhado, Thulane Paepae, Matthews Sejeso and Charis Harley
J. Mar. Sci. Eng. 2025, 13(7), 1339; https://doi.org/10.3390/jmse13071339 (registering DOI) - 13 Jul 2025
Viewed by 126
Abstract
Container terminals are facing significant challenges in meeting the increasing demands for volume and throughput, with limited space often presenting as a critical constraint. Key areas of concern at the quayside include the berth allocation problem, the quay crane assignment, and the scheduling [...] Read more.
Container terminals are facing significant challenges in meeting the increasing demands for volume and throughput, with limited space often presenting as a critical constraint. Key areas of concern at the quayside include the berth allocation problem, the quay crane assignment, and the scheduling problem. Effectively managing these issues is essential for optimizing port operations; failure to do so can lead to substantial operational and economic ramifications, ultimately affecting competitiveness within the global shipping industry. Optimization models, encompassing both mathematical frameworks and metaheuristic approaches, offer promising solutions. Additionally, the application of machine learning and reinforcement learning enables real-time solutions, while robust optimization and stochastic models present effective strategies, particularly in scenarios involving uncertainties. This study expands upon earlier foundational analyses of berth allocation, quay crane assignment, and scheduling issues, which have laid the groundwork for port optimization. Recent developments in uncertainty management, automation, real-time decision-making approaches, and environmentally sustainable objectives have prompted this review of the literature from 2015 to 2024, exploring emerging challenges and opportunities in container terminal operations. Recent research has increasingly shifted toward integrated approaches and the utilization of continuous berthing for better wharf utilization. Additionally, emerging trends, such as sustainability and green infrastructure in port operations, and policy trade-offs are gaining traction. In this review, we critically analyze and discuss various aspects, including spatial and temporal attributes, crane handling, sustainability, model formulation, policy trade-offs, solution approaches, and model performance evaluation, drawing on a review of 94 papers published between 2015 and 2024. Full article
(This article belongs to the Section Ocean Engineering)
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28 pages, 1536 KiB  
Review
Remote Non-Destructive Testing of Port Cranes: A Review of Vibration and Acoustic Sensors with IoT Integration
by Jan Lean Tai, Mohamed Thariq Hameed Sultan, Rafał Grzejda and Farah Syazwani Shahar
J. Mar. Sci. Eng. 2025, 13(7), 1338; https://doi.org/10.3390/jmse13071338 - 13 Jul 2025
Viewed by 309
Abstract
Safe and efficient operation of port cranes is vital for maintaining the efficiency of global maritime logistics. However, traditional non-destructive testing methods face significant limitations in harsh port environments, such as periodic inspection intervals, restricted access to structural components, and a lack of [...] Read more.
Safe and efficient operation of port cranes is vital for maintaining the efficiency of global maritime logistics. However, traditional non-destructive testing methods face significant limitations in harsh port environments, such as periodic inspection intervals, restricted access to structural components, and a lack of real-time monitoring. This review explores the emerging paradigm of remote non-destructive testing through the integration of vibration and acoustic emission sensors with Internet of Things platforms. By enabling continuous, real-time monitoring, these sensor systems can detect early indicators of mechanical degradation, structural fatigue, and corrosion. This study synthesizes findings from over 100 peer-reviewed sources and identifies a significant gap in the application of these technologies to port cranes. Although vibration and acoustic emission sensors have been widely studied in various fields, their application to port cranes remains underexplored, presenting a novel and promising avenue for future research and practical applications. The unique operational demands and structural complexities of port cranes, coupled with their critical role in global trade logistics, make them ideal for leveraging these sensors in tandem with Internet of Things solutions. This integration not only overcomes the limitations of traditional non-destructive testing methods, but also offers substantial benefits, including enhanced safety, reduced inspection costs, and improved operational efficiency. This review concludes by proposing future research directions to enhance sensor performance, data analytics, and Internet of Things integration, paving the way for predictive maintenance strategies that increase operational uptime and improve safety in port crane operations. Full article
(This article belongs to the Section Ocean Engineering)
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33 pages, 7266 KiB  
Article
Temperature Prediction and Fault Warning of High-Speed Shaft of Wind Turbine Gearbox Based on Hybrid Deep Learning Model
by Min Zhang, Jijie Wei, Zhenli Sui, Kun Xu and Wenyong Yuan
J. Mar. Sci. Eng. 2025, 13(7), 1337; https://doi.org/10.3390/jmse13071337 - 13 Jul 2025
Viewed by 195
Abstract
Gearbox failure represents one of the most time-consuming maintenance challenges in wind turbine operations. Abnormal temperature variations in the gearbox high-speed shaft (GHSS) serve as reliable indicators of potential faults. This study proposes a Spatio-Temporal Attentive (STA) synergistic architecture for GHSS fault detection [...] Read more.
Gearbox failure represents one of the most time-consuming maintenance challenges in wind turbine operations. Abnormal temperature variations in the gearbox high-speed shaft (GHSS) serve as reliable indicators of potential faults. This study proposes a Spatio-Temporal Attentive (STA) synergistic architecture for GHSS fault detection and early warning by utilizing the in situ monitoring data from a wind farm. This comprehensive architecture involves five modules: data preprocessing, multi-dimensional spatial feature extraction, temporal dependency modeling, global relationship learning, and hyperparameter optimization. It was achieved by using real-time monitoring data to predict the GHSS temperature in 10 min, with an accuracy of 1 °C. Compared to the long short-term memory (LSTM) and convolutional neural network and LSTM hybrid models, the STA architecture reduces the root mean square error of the prediction by approximately 37% and 13%, respectively. Furthermore, the architecture establishes a normal operating condition model and provides benchmark eigenvalues for subsequent fault warnings. The model was validated to issue early warnings up to seven hours before the fault alert is triggered by the supervisory control and data acquisition system of the wind turbine. By offering reliable, cost-effective prognostics without additional hardware, this approach significantly improves wind turbine health management and fault prevention. Full article
(This article belongs to the Section Ocean Engineering)
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19 pages, 3316 KiB  
Article
Optimization Design of Dynamic Cable Configuration Considering Thermo-Mechanical Coupling Effects
by Ying Li, Guanggen Zou, Suchun Yang, Dongsheng Qiao and Bin Wang
J. Mar. Sci. Eng. 2025, 13(7), 1336; https://doi.org/10.3390/jmse13071336 - 13 Jul 2025
Viewed by 194
Abstract
During operation, dynamic cables endure coupled thermo-mechanical loads (mechanical: tension/bending; thermal: power transmission) that degrade stiffness, amplifying extreme responses and impairing configuration optimization. To address this, this study pioneers a multi-objective optimization framework integrating stiffness characteristics from mechanical/thermo-mechanical analyses, with objectives to minimize [...] Read more.
During operation, dynamic cables endure coupled thermo-mechanical loads (mechanical: tension/bending; thermal: power transmission) that degrade stiffness, amplifying extreme responses and impairing configuration optimization. To address this, this study pioneers a multi-objective optimization framework integrating stiffness characteristics from mechanical/thermo-mechanical analyses, with objectives to minimize dynamic extreme tension and curvature under constraints of global configuration variables and safety thresholds. The framework employs a Radial Basis Function (RBF) surrogate model coupled with NSGA-II algorithm, yielding validated Pareto solutions (≤6.15% max error vs. simulations). Results demonstrate universal reduction in extreme responses across optimized configurations, with the thermo-mechanically optimized solution achieving 20.24% fatigue life enhancement. This work establishes the first methodology quantifying thermo-mechanical coupling effects on offshore cable safety and fatigue performance. This configuration design scheme exhibits better safety during actual service conditions. Full article
(This article belongs to the Special Issue Advanced Studies in Marine Structures)
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14 pages, 2164 KiB  
Article
Research on Operational Risk for Northwest Passage Cruise Ships Using POLARIS
by Long Ma, Jiemin Fan, Xiaoguang Mou, Sihan Qian, Jin Xu, Liang Cao, Bo Xu, Boxi Yao, Xiaowen Li and Yabin Li
J. Mar. Sci. Eng. 2025, 13(7), 1335; https://doi.org/10.3390/jmse13071335 - 12 Jul 2025
Viewed by 144
Abstract
In the context of global warming, polar tourism is developing rapidly, and the demand for polar cruise travel in the Northwest Passage continues to increase, while sea ice has long been a key factor limiting the development of polar cruise tourism. This study [...] Read more.
In the context of global warming, polar tourism is developing rapidly, and the demand for polar cruise travel in the Northwest Passage continues to increase, while sea ice has long been a key factor limiting the development of polar cruise tourism. This study focuses on the operational risk of sea ice on cruise ships in the Northwest Passage (NWP), aiming to provide a scientific basis for ensuring the safety of cruise ship navigation and promoting the sustainable development of polar tourism. Based on ice data from 2015 to 2024, this study used the Polar Operational Limit Assessment Risk Indexing System (POLARIS) methodology recommended by the International Maritime Organization (IMO) to establish three scenarios for the route of ice class IC cruise ships: light ice, normal ice, and heavy ice. The navigable windows were systematically analyzed and critical waters along the route were identified. The results indicate that the navigable windows for IC ice-class cruise ships under light ice conditions are from mid-July to early December, while the navigable period under normal ice conditions is only from mid- to late September, and navigation is not possible under heavy ice conditions. The study identified Larsen Sound, Barrow Strait, Bellot Strait and Eastern Beaufort Sea as critical waters on the NWP cruise route. Among them, Larsen Sound and Eastern Beaufort Sea have a more prominent impact on voyage scheduling because their navigation weeks overlap less with other waters. This study provides a new idea for the risk assessment of polar cruise ships in ice regions. The research results can provide an important reference for the safe operation of polar cruise ships in the NWP and the decision-making of relevant parties. Full article
(This article belongs to the Special Issue Navigation Performance and Experimental Research of Ships in Ice-Covered Areas)
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20 pages, 1865 KiB  
Article
A Robust Cross-Band Network for Blind Source Separation of Underwater Acoustic Mixed Signals
by Xingmei Wang, Peiran Wu, Haisu Wei, Yuezhu Xu and Siyu Wang
J. Mar. Sci. Eng. 2025, 13(7), 1334; https://doi.org/10.3390/jmse13071334 - 11 Jul 2025
Viewed by 172
Abstract
Blind source separation (BSS) of underwater acoustic mixed signals aims to improve signal clarity by separating noise components from aliased underwater signal sources. This enhancement directly increases target detection accuracy in underwater acoustic perception systems, particularly in scenarios involving multi-vessel interference or biological [...] Read more.
Blind source separation (BSS) of underwater acoustic mixed signals aims to improve signal clarity by separating noise components from aliased underwater signal sources. This enhancement directly increases target detection accuracy in underwater acoustic perception systems, particularly in scenarios involving multi-vessel interference or biological sound coexistence. Deep learning-based BSS methods have gained wide attention for their superior nonlinear modeling capabilities. However, existing approaches in underwater acoustic scenarios still face two key challenges: limited feature discrimination and inadequate robustness against non-stationary noise. To overcome these limitations, we propose a novel Robust Cross-Band Network (RCBNet) for the BSS of underwater acoustic mixed signals. To address insufficient feature discrimination, we decompose mixed signals into sub-bands aligned with ship noise harmonics. For intra-band modeling, we apply a parallel gating mechanism that strengthens long-range dependency learning so as to enhance robustness against non-stationary noise. For inter-band modeling, we design a bidirectional-frequency RNN to capture the global dependency relationships of the same signal across sub-bands. Our experiment demonstrates that RCBNet achieves a 0.779 dB improvement in the SDR compared to the advanced model. Additionally, the anti-noise experiment demonstrates that RCBNet exhibits satisfactory robustness across varying noise environments. Full article
(This article belongs to the Section Ocean Engineering)
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16 pages, 5423 KiB  
Article
Effect of Nonlinear Constitutive Models on Seismic Site Response of Soft Reclaimed Soil Deposits
by Sadiq Shamsher, Myoung-Soo Won, Young-Chul Park, Yoon-Ho Park and Mohamed A. Sayed
J. Mar. Sci. Eng. 2025, 13(7), 1333; https://doi.org/10.3390/jmse13071333 - 11 Jul 2025
Viewed by 128
Abstract
This study investigates the impact of nonlinear constitutive models on one-dimensional seismic site response analysis (SRA) for soft, reclaimed soil deposits in Saemangeum, South Korea. Two widely used models, MKZ and GQ/H, were applied to three representative soil profiles using the DEEPSOIL program. [...] Read more.
This study investigates the impact of nonlinear constitutive models on one-dimensional seismic site response analysis (SRA) for soft, reclaimed soil deposits in Saemangeum, South Korea. Two widely used models, MKZ and GQ/H, were applied to three representative soil profiles using the DEEPSOIL program. Ground motions were scaled to bedrock peak ground accelerations (PGAs) corresponding to annual return periods (ARPs) of 1000, 2400, and 4800 years. Seismic response metrics include the ratio of GQ/H to MKZ shear strain, effective PGA (EPGA), and short- and long-term amplification factors (Fa and Fv). The results highlight the critical role of the site-to-motion period ratio (Tg/Tm) in controlling seismic behavior. Compared to the MKZ, the GQ/H model, which features strength correction and improved stiffness retention, predicts lower shear strains and higher surface spectral accelerations, particularly under strong shaking and shallow conditions. Model differences are most pronounced at low Tg/Tm values, where MKZ tends to underestimate amplification and overestimate strain due to its limited ability to reflect site-specific shear strength. Relative to code-based amplification factors, the GQ/H model yields lower short-term estimates, reflecting the disparity between stiff inland reference sites and the soft reclaimed conditions at Saemangeum. These findings emphasize the need for strength-calibrated constitutive models to improve the accuracy of site-specific seismic hazard assessments. Full article
(This article belongs to the Section Marine Hazards)
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26 pages, 2603 KiB  
Article
Determining Non-Dimensional Group of Parameters Governing the Prediction of Penetration Depth and Holding Capacity of Drag Embedment Anchors Using Linear Regression
by Mojtaba Olyasani, Hamed Azimi and Hodjat Shiri
J. Mar. Sci. Eng. 2025, 13(7), 1332; https://doi.org/10.3390/jmse13071332 - 11 Jul 2025
Viewed by 186
Abstract
Drag embedment anchors (DEAs) provide reliable and cost-effective mooring solutions for floating structures, e.g., platforms, ships, offshore wind turbines, etc., in offshore engineering. Structural stability and operational safety require accurate predictions of their penetration depths and holding capacities across various seabed conditions. In [...] Read more.
Drag embedment anchors (DEAs) provide reliable and cost-effective mooring solutions for floating structures, e.g., platforms, ships, offshore wind turbines, etc., in offshore engineering. Structural stability and operational safety require accurate predictions of their penetration depths and holding capacities across various seabed conditions. In this study, explicit linear regression (LR) models were developed for the first time to predict the penetration depth and holding capacity of DEAs on clay and sand seabed. Buckingham’s theorem was also applied to identify dimensionless groups of parameters that influence DEA behavior, e.g., the penetration depth and holding capacity of the DEAs. LR models were developed and validated against experimental data from the literature for both clay and sand seabed. To evaluate model performance and identify the most accurate LR models to predict DEA behavior, comprehensive sensitivity, error, and uncertainty analyses were performed. Additionally, LR analysis revealed the most influential input parameters impacting penetration depth and holding capacity. Regarding offshore mooring design and geotechnical engineering applications, the proposed LR models offered a practical and efficient approach to estimating DEA performance across various seabed conditions. Full article
(This article belongs to the Section Ocean Engineering)
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18 pages, 3402 KiB  
Article
Synergistic Detrital Zircon U-Pb and REE Analysis for Provenance Discrimination of the Beach-Bar System in the Oligocene Dongying Formation, HHK Depression, Bohai Bay Basin, China
by Jing Wang, Youbin He, Hua Li, Tao Guo, Dayong Guan, Xiaobo Huang, Bin Feng, Zhongxiang Zhao and Qinghua Chen
J. Mar. Sci. Eng. 2025, 13(7), 1331; https://doi.org/10.3390/jmse13071331 - 11 Jul 2025
Viewed by 191
Abstract
The Oligocene Dongying Formation beach-bar system, widely distributed in the HHK Depression of the Bohai Bay Basin, constitutes a key target for mid-deep hydrocarbon exploration, though its provenance remains controversial due to complex peripheral source terrains. To address this, we developed an integrated [...] Read more.
The Oligocene Dongying Formation beach-bar system, widely distributed in the HHK Depression of the Bohai Bay Basin, constitutes a key target for mid-deep hydrocarbon exploration, though its provenance remains controversial due to complex peripheral source terrains. To address this, we developed an integrated methodology combining LA-ICP-MS zircon U-Pb dating with whole-rock rare earth element (REE) analysis, facilitating provenance studies in areas with limited drilling and heavy mineral data. Analysis of 849 high-concordance zircons (concordance >90%) from 12 samples across 5 wells revealed that Geochemical homogeneity is evidenced by strongly consistent moving-average trendlines of detrital zircon U-Pb ages among the southern/northern provenances and the central uplift zone, complemented by uniform REE patterns characterized by HREE (Gd-Lu) enrichment and LREE depletion; geochemical disparities manifest as dual dominant age peaks (500–1000 Ma and 1800–3100 Ma) in the southern provenance and central uplift samples, contrasting with three distinct peaks (65–135 Ma, 500–1000 Ma, and 1800–3100 Ma) in the northern provenance; spatial quantification via multidimensional scaling (MDS) demonstrates closer affinity between the southern provenance and central uplift (dij = 4.472) than to the northern provenance (dij = 6.708). Collectively, these results confirm a dual (north–south) provenance system for the central uplift beach-bar deposits, with the southern provenance dominant and the northern acting as a subsidiary source. This work establishes a dual-provenance beach-bar model, providing a universal theoretical and technical framework for provenance analysis in hydrocarbon exploration within analogous settings. Full article
(This article belongs to the Section Geological Oceanography)
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