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Volume 13
Issue 6
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J. Mar. Sci. Eng., Volume 13, Issue 6 (June 2025) – 194 articles

Cover Story (view full-size image): This study explores long-term changes in phytoplankton size classes across the Yellow Sea, South Sea of Korea, and East/Japan Sea by using 20 years (2003–2022) of satellite ocean color data and a regionally optimized deep neural network model. The results reveal a marked expansion of pico-sized phytoplankton, particularly under warmer, stratified, and nutrient-depleted conditions, driven by rising sea surface temperatures and altered nutrient stoichiometry. This shift toward smaller phytoplankton may reduce primary production and disrupt marine food webs, with implications for fishery yields. Our findings underscore the ecological consequences of climate-driven changes in phytoplankton communities and highlight the importance of long-term ecosystem monitoring. View this paper
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21 pages, 8446 KiB  
Article
Regional Wave Analysis in the East China Sea Based on the SWAN Model
by Songnan Ma, Fuwu Ji, Qunhui Yang, Zhinan Mi and Wenhui Cao
J. Mar. Sci. Eng. 2025, 13(6), 1196; https://doi.org/10.3390/jmse13061196 - 19 Jun 2025
Viewed by 350
Abstract
High-precision wave data serve as a foundation for investigating the wave characteristics of the East China Sea (ECS) and wave energy development. Based on the simulating waves nearshore (SWAN) model, this study uses the ERA5 (ECMWF Reanalysis v5) reanalysis wind field data and [...] Read more.
High-precision wave data serve as a foundation for investigating the wave characteristics of the East China Sea (ECS) and wave energy development. Based on the simulating waves nearshore (SWAN) model, this study uses the ERA5 (ECMWF Reanalysis v5) reanalysis wind field data and ETOPO1 bathymetric data to perform high-precision simulations at a resolution of 0.05° × 0.05° for the waves in the area of 25–35° N and 120–130° E in the ECS from 2009 to 2023. The simulation results indicate that the application of the whitecapping dissipation parameter Komen and the bottom friction parameter Collins yields an average RMSE of 0.374 m and 0.369 m when compared to satellite-measured data, demonstrating its superior suitability for wave simulation in shallow waters such as the ESC over the other whitecapping dissipation parameter, Westhuysen, and the other two bottom friction parameters, Jonswap and Madsen, in the SWAN model. The monthly average significant wave height (SWH) ranges from 0 to 3 m, exhibiting a trend that it is more important in autumn and winter than in spring and summer and gradually increases from the northwest to the southeast. Due to the influence of the Kuroshio current, topography, and events such as typhoons, areas with significant wave heights are found in the northwest of the Ryukyu Islands and north of the Taiwan Strait. The wave energy flux density in most areas of the ECS is >2 kW/m, particularly in the north of the Ryukyu Islands, where the annual average value remains above 8 kW/m. Because of the influence of climate events such as El Niño and extreme heatwaves, the wave energy flux density decreased significantly in some years (a 21% decrease in 2015). The coefficient of variation of wave energy in the East China Sea exhibits pronounced regional heterogeneity, which can be categorized into four distinct patterns: high mean wave energy with high variation coefficient, high mean wave energy with low variation coefficient, low mean wave energy with high variation coefficient, and low mean wave energy with low variation coefficient. This classification fundamentally reflects the intrinsic differences in dynamic environments across various maritime regions. These high-precision numerical simulation results provide methodological and theoretical support for exploring the spatiotemporal variation laws of waves in the ECS region, the development and utilization of wave resources, and marine engineering construction. Full article
(This article belongs to the Section Physical Oceanography)
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24 pages, 3570 KiB  
Article
Semi-Supervised Underwater Image Enhancement Method Using Multimodal Features and Dynamic Quality Repository
by Mu Ding, Gen Li, Yu Hu, Hangfei Liu, Qingsong Hu and Xiaohua Huang
J. Mar. Sci. Eng. 2025, 13(6), 1195; https://doi.org/10.3390/jmse13061195 - 19 Jun 2025
Viewed by 247
Abstract
Obtaining clear underwater images is crucial for smart aquaculture, so it is necessary to repair degraded underwater images. Although underwater image restoration techniques have achieved remarkable results in recent years, the scarcity of labeled data poses a significant challenge to continued advancement. It [...] Read more.
Obtaining clear underwater images is crucial for smart aquaculture, so it is necessary to repair degraded underwater images. Although underwater image restoration techniques have achieved remarkable results in recent years, the scarcity of labeled data poses a significant challenge to continued advancement. It is well known that semi-supervised learning can make use of unlabeled data. In this study, we proposed a semi-supervised underwater image enhancement method, MCR-UIE, which utilized multimodal contrastive learning and a dynamic quality reliability repository to leverage the unlabeled data during training. This approach used multimodal feature contrast regularization to prevent the overfitting of incorrect labels, and secondly, introduced a dynamic quality reliability repository to update the output as pseudo ground truth. The robustness and generalization of the model in pseudo-label generation and unlabeled data learning were improved. Extensive experiments conducted on the UIEB and LSUI datasets demonstrated that the proposed method consistently outperformed existing traditional and deep learning-based approaches in both quantitative and qualitative evaluations. Furthermore, its successful application to images captured from deep-sea cage aquaculture environments validated its practical value. These results indicated that MCR-UIE held strong potential for real-world deployment in intelligent monitoring and visual perception tasks in complex underwater scenarios. Full article
(This article belongs to the Special Issue Selection of Deep-Sea Aquaculture Species and Development of Supporting Technologies and Equipment)
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25 pages, 4997 KiB  
Article
Use of Machine-Learning Techniques to Estimate Long-Term Wave Power at a Target Site Where Short-Term Data Are Available
by María José Pérez-Molina and José A. Carta
J. Mar. Sci. Eng. 2025, 13(6), 1194; https://doi.org/10.3390/jmse13061194 - 19 Jun 2025
Viewed by 288
Abstract
Wave energy is a promising renewable resource supporting the decarbonization of energy systems. However, its significant temporal variability necessitates long-term datasets for accurate resource assessment. A common approach to obtaining such data is through climate reanalysis datasets. Nevertheless, reanalysis data may not accurately [...] Read more.
Wave energy is a promising renewable resource supporting the decarbonization of energy systems. However, its significant temporal variability necessitates long-term datasets for accurate resource assessment. A common approach to obtaining such data is through climate reanalysis datasets. Nevertheless, reanalysis data may not accurately capture the local characteristics of wave energy at specific sites. This study proposes a supervised machine-learning (ML) approach to estimate long-term wave energy at locations with only short-term in situ measurements. The method involves training ML models using concurrent short-term buoy data and ERA5 reanalysis data, enabling the extension of wave energy estimates over longer periods using only reanalysis inputs. As a case study, hourly mean significant wave height and energy period data from 2000 to 2023 were analyzed, collected by a deep-water buoy off the coast of Gran Canaria (Canary Islands, Spain). Among the ML techniques evaluated, Multiple Linear Regression (MLR) and Support Vector Regression yielded the most favorable error metrics. MLR was selected due to its lower computational complexity, greater interpretability, and ease of implementation, aligning with the principle of parsimony, particularly in contexts where model transparency is essential. The MLR model achieved a mean absolute error (MAE) of 2.56 kW/m and a root mean square error (RMSE) of 4.49 kW/m, significantly outperforming the direct use of ERA5 data, which resulted in an MAE of 4.38 kW/m and an RMSE of 7.1 kW/m. These findings underscore the effectiveness of the proposed approach in enhancing long-term wave energy estimations using limited in situ data. Full article
(This article belongs to the Special Issue Development and Utilization of Offshore Renewable Energy)
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20 pages, 10753 KiB  
Article
Physics-Guided Self-Supervised Learning Full Waveform Inversion with Pretraining on Simultaneous Source
by Qiqi Zheng, Meng Li and Bangyu Wu
J. Mar. Sci. Eng. 2025, 13(6), 1193; https://doi.org/10.3390/jmse13061193 - 19 Jun 2025
Viewed by 278
Abstract
Full waveform inversion (FWI) is an established precise velocity estimation tool for seismic exploration. Machine learning-based FWI could plausibly circumvent the long-standing cycle-skipping problem of traditional model-driven methods. The physics-guided self-supervised FWI is appealing in that it avoids having to make tedious efforts [...] Read more.
Full waveform inversion (FWI) is an established precise velocity estimation tool for seismic exploration. Machine learning-based FWI could plausibly circumvent the long-standing cycle-skipping problem of traditional model-driven methods. The physics-guided self-supervised FWI is appealing in that it avoids having to make tedious efforts in terms of label generation for supervised methods. One way is to employ an inversion network to convert the seismic shot gathers into a velocity model. The objective function is to minimize the difference between the recorded seismic data and the synthetic data by solving the wave equation using the inverted velocity model. To further improve the efficiency, we propose a two-stage training strategy for the self-supervised learning FWI. The first stage is to pretrain the inversion network using a simultaneous source for a large-scale velocity model with high efficiency. The second stage is switched to modeling the separate shot gathers for an accurate measurement of the seismic data to invert the velocity model details. The inversion network is a partial convolution attention modified UNet (PCAMUNet), which combines local feature extraction with global information integration to achieve high-resolution velocity model estimation from seismic shot gathers. The time-domain 2D acoustic wave equation serves as the physical constraint in this self-supervised framework. Different loss functions are used for the two stages, that is, the waveform loss with time weighting for the first stage (simultaneous source) and the hybrid waveform with time weighting and logarithmic envelope loss for the second stage (separate source). Comparative experiments demonstrate that the proposed approach improves both inversion accuracy and efficiency on the Marmousi2 model, Overthrust model, and BP model tests. Moreover, the method exhibits excellent noise resistance and stability when low-frequency data component is missing. Full article
(This article belongs to the Special Issue Modeling and Waveform Inversion of Marine Seismic Data)
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22 pages, 3923 KiB  
Article
Optimizing Fuel Efficiency and Emissions of Marine Diesel Engines When Using Biodiesel Mixtures Under Diverse Load/Temperature Conditions: Predictive Model and Comprehensive Life Cycle Analysis
by Kwang-Sik Jo, Kyeong-Ju Kong and Seung-Hun Han
J. Mar. Sci. Eng. 2025, 13(6), 1192; https://doi.org/10.3390/jmse13061192 - 19 Jun 2025
Viewed by 244
Abstract
Marine transportation contributes approximately 2.5% of global greenhouse gas emissions. While previous studies have examined biodiesel effects on automotive engines, research on marine applications reveals critical gaps: (1) existing studies focus on single-parameter analysis without considering the complex interactions between biodiesel ratio, engine [...] Read more.
Marine transportation contributes approximately 2.5% of global greenhouse gas emissions. While previous studies have examined biodiesel effects on automotive engines, research on marine applications reveals critical gaps: (1) existing studies focus on single-parameter analysis without considering the complex interactions between biodiesel ratio, engine load, and operating conditions; (2) most research lacks comprehensive lifecycle assessment integration with real-time operational data; (3) previous optimization models demonstrate insufficient accuracy (R2 < 0.80) for practical marine applications; and (4) no adaptive algorithms exist for dynamic biodiesel ratio adjustment based on operational conditions. These limitations prevent effective biodiesel implementation in maritime operations, necessitating an integrated multi-parameter optimization approach. This study addresses this research gap by proposing an integrated optimization model for fuel efficiency and emissions of marine diesel engines using biodiesel mixtures under diverse operating conditions. Based on extensive experimental data from two representative marine engines (YANMAR 6HAL2-DTN 200 kW and Niigatta Engineering 6L34HX 2471 kW), this research analyzes correlations between biodiesel blend ratios (pure diesel, 20%, 50%, and 100% biodiesel), engine load conditions (10–100%), and operating temperature with nitrogen oxides, carbon dioxide, and carbon monoxide emissions. Multivariate regression models were developed, allowing prediction of emission levels with high accuracy (R2 = 0.89–0.94). The models incorporated multiple parameters, including engine characteristics, fuel properties, and ambient conditions, to provide a comprehensive analytical framework. Life cycle assessment (LCA) results show that the B50 biodiesel ratio achieves optimal environmental efficiency, reducing greenhouse gases by 15% compared to B0 while maintaining stable engine performance across operational profiles. An adaptive optimization algorithm for operating conditions is proposed, providing detailed reference charts for ship operators on ideal biodiesel ratios based on load conditions, ambient temperature, and operational priorities in different maritime zones. The findings demonstrate significant potential for emissions reduction in the maritime sector through strategic biodiesel implementation. Full article
(This article belongs to the Section Ocean Engineering)
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11 pages, 335 KiB  
Article
Consistent Models of Flexural-Gravity Waves in Floating Ice
by Alexander Korobkin and Tatiana Khabakhpasheva
J. Mar. Sci. Eng. 2025, 13(6), 1191; https://doi.org/10.3390/jmse13061191 - 19 Jun 2025
Viewed by 179
Abstract
Classification of flexural-gravity waves in floating ice is discussed, using the dispersion relation of these waves and the criterion of sea ice breaking, based on the concept of yield strain. It is shown that flexural-gravity waves, in terms of their wavelength and amplitude, [...] Read more.
Classification of flexural-gravity waves in floating ice is discussed, using the dispersion relation of these waves and the criterion of sea ice breaking, based on the concept of yield strain. It is shown that flexural-gravity waves, in terms of their wavelength and amplitude, are divided into waves with both inertia and ice rigidity being of major importance, waves with negligible ice inertia, waves with negligible ice rigidity (broken ice) and gravity waves. The effects of water depth and ice thickness on the waves are also investigated. Full article
(This article belongs to the Special Issue Hydroelastic Behaviour of Floating Offshore Structures)
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18 pages, 5615 KiB  
Article
Experimental Investigation on IceBreaking Resistance and Ice Load Distribution for Comparison of Icebreaker Bows
by Xuhao Gang, Yukui Tian, Chaoge Yu, Ying Kou and Weihang Zhao
J. Mar. Sci. Eng. 2025, 13(6), 1190; https://doi.org/10.3390/jmse13061190 - 18 Jun 2025
Viewed by 220
Abstract
During icebreaker navigation in ice-covered waters, icebreaking resistance and dynamic ice loads acting on the bow critically determine the vessel’s icebreaking performance. Quantitative characterization of the icebreaking resistance behavior and ice load distribution on the bow is essential for elucidating ship-ice interaction mechanisms, [...] Read more.
During icebreaker navigation in ice-covered waters, icebreaking resistance and dynamic ice loads acting on the bow critically determine the vessel’s icebreaking performance. Quantitative characterization of the icebreaking resistance behavior and ice load distribution on the bow is essential for elucidating ship-ice interaction mechanisms, assessing icebreaking capability, and optimizing structural design. This study conducted comparative icebreaking tests on two icebreaker bow models with distinct geometries in the small ice model basin of China Ship Scientific Research Center (CSSRC SIMB). Systematic measurements were performed to quantify icebreaking resistance, capture spatiotemporal ice load distributions, and document ice failure patterns under level ice conditions. The analysis reveals that bow geometry profoundly influences icebreaking efficiency: the stem angle governs the proportion of bending failure during vertical ice penetration, while the flare angle modulates circumferential failure modes along the hull-ice interface. Notably, the sunken keel configuration enhances ice clearance by mechanically expelling fractured ice blocks. Ice load distributions exhibit pronounced nonlinearity, with localized pressure concentrations and stochastic load center migration driven by ice fracture dynamics. Furthermore, icebreaking patterns—such as fractured ice dimensions and kinematic behavior during ship-ice interaction—are quantitatively correlated with the bow designs. These experimentally validated findings provide critical insights into ice-structure interaction physics, offering an empirical foundation for performance prediction and bow-form optimization in the preliminary design of icebreakers. Full article
(This article belongs to the Special Issue Navigation Performance and Experimental Research of Ships in Ice-Covered Areas)
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17 pages, 4356 KiB  
Article
Impact of High-Concentration Biofuels on Cylinder Lubricating Oil Performance in Low-Speed Two-Stroke Marine Diesel Engines
by Enrui Zhao, Guichen Zhang, Qiuyu Li and Saihao Zhu
J. Mar. Sci. Eng. 2025, 13(6), 1189; https://doi.org/10.3390/jmse13061189 - 18 Jun 2025
Viewed by 672
Abstract
With the implementation of the ISO 8217-2024 marine fuel standard, the use of high-concentration biofuels in ships has become viable. However, relatively few studies have been conducted on the effects of biofuels on cylinder lubrication performance in low-speed, two-stroke marine diesel engines. In [...] Read more.
With the implementation of the ISO 8217-2024 marine fuel standard, the use of high-concentration biofuels in ships has become viable. However, relatively few studies have been conducted on the effects of biofuels on cylinder lubrication performance in low-speed, two-stroke marine diesel engines. In this study, catering waste oil was blended with 180 cSt low-sulfur fuel oil (LSFO) to prepare biofuels with volume fractions of 24% (B24) and 50% (B50). These biofuels were evaluated in a MAN marine diesel engine under load conditions of 25%, 50%, 75%, and 90%. The experimental results showed that, at the same engine load, the use of B50 biofuel led to lower kinematic viscosity and oxidation degree of the cylinder residual oil, but higher total base number (TBN), nitration level, PQ index, and concentrations of wear elements (Fe, Cu, Cr, Mo). These results indicate that the wear of the cylinder liner–piston ring interface was more severe when using B50 biofuel than when using B24 biofuel. For the same type of fuel, as the engine load increased, the kinematic viscosity and TBN of the residual oil decreased, while the PQ index and the concentrations of Fe, Cu, Cr, and Mo increased, reflecting the aggravated wear severity. Ferrographic analysis further revealed that ferromagnetic wear particles in the oil mainly consisted of normal wear debris. When using B50 biodiesel, a small amount of fatigue wear particles were detected. These findings offer crucial insights for optimizing biofuel utilization and improving cylinder lubrication systems in marine engines. Full article
(This article belongs to the Section Ocean Engineering)
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20 pages, 1743 KiB  
Article
Understanding Wave Attenuation Across Marshes: Insights from Numerical Modeling
by Madeline R. Foster-Martinez, Ioannis Y. Georgiou, Duncan M. FitzGerald, Zoe J. Hughes, Alyssa Novak and Md Mohiuddin Sakib
J. Mar. Sci. Eng. 2025, 13(6), 1188; https://doi.org/10.3390/jmse13061188 - 18 Jun 2025
Viewed by 362
Abstract
Marsh vegetation dampens wave energy, providing protection to coastal communities from storms. A new modeling framework was applied to study wave height evolution over the saltmarsh bordering Newbury, MA. A regional Delft3D hydrodynamic model generated wind driver waves in the open water portions [...] Read more.
Marsh vegetation dampens wave energy, providing protection to coastal communities from storms. A new modeling framework was applied to study wave height evolution over the saltmarsh bordering Newbury, MA. A regional Delft3D hydrodynamic model generated wind driver waves in the open water portions of the study area, which were then one-way coupled with an analytical model, the Marsh Transect Wave Attenuation (MTWA) model, which tracked wave evolution along select transects throughout the marsh. Field observations of vegetation and wave height evolution were used to calibrate MTWA. Seven scenarios were run covering a range of possible future management and environmental conditions, in addition to projected sea level rise. Results underscore the importance of vegetation and elevation to wave attenuation. Full article
(This article belongs to the Special Issue Learning from Geomorphological Adaptation of Coasts at Different Time Scales (2nd Edition))
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16 pages, 3012 KiB  
Review
Application of Large-Scale Rotating Platforms in the Study of Complex Oceanic Dynamic Processes
by Xiaojie Lu, Guoqing Han, Yifan Lin, Qian Cao, Zhiwei You, Jingyuan Xue, Xinyuan Zhang and Changming Dong
J. Mar. Sci. Eng. 2025, 13(6), 1187; https://doi.org/10.3390/jmse13061187 - 18 Jun 2025
Viewed by 675
Abstract
As the core components of geophysical dynamic system, oceans and atmospheres are dominated by the Coriolis force, which governs complex dynamic phenomena such as internal waves, gravity currents, vortices, and others involving multi-scale spatiotemporal coupling. Due to the limitations of in situ observations, [...] Read more.
As the core components of geophysical dynamic system, oceans and atmospheres are dominated by the Coriolis force, which governs complex dynamic phenomena such as internal waves, gravity currents, vortices, and others involving multi-scale spatiotemporal coupling. Due to the limitations of in situ observations, large-scale rotating tanks have emerged as critical experimental platforms for simulating Earth’s rotational effects. This review summarizes recent advancements in rotating tank applications for studying oceanic flow phenomena, including mesoscale eddies, internal waves, Ekman flows, Rossby waves, gravity currents, and bottom boundary layer dynamics. Advanced measurement techniques, such as particle image velocimetry (PIV) and planar laser-induced fluorescence (PLIF), have enabled quantitative analyses of internal wave breaking-induced mixing and refined investigations of vortex merging dynamics. The findings demonstrate that large-scale rotating tanks provide a controllable experimental framework for unraveling the physical essence of geophysical fluid motions. Such laboratory experimental endeavors in a rotating tank can be applied to more extensive scientific topics, in which the rotation and stratification play important roles, offering crucial support for climate model parameterization and coupled ocean–land–atmosphere mechanisms. Full article
(This article belongs to the Section Physical Oceanography)
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32 pages, 5632 KiB  
Article
One-Dimensional Plume Dispersion Modeling in Marine Conditions (SEDPLUME1D-Model)
by L. C. van Rijn
J. Mar. Sci. Eng. 2025, 13(6), 1186; https://doi.org/10.3390/jmse13061186 - 18 Jun 2025
Viewed by 228
Abstract
Dredging of fine sediments and dumping of fines at disposal sites produce passive plumes behind the dredging equipment. Each type of dredging method has its own plume characteristics. All types of dredging operations create some form of turbidity (spillage of dredged materials) in [...] Read more.
Dredging of fine sediments and dumping of fines at disposal sites produce passive plumes behind the dredging equipment. Each type of dredging method has its own plume characteristics. All types of dredging operations create some form of turbidity (spillage of dredged materials) in the water column, depending on (i) the applied method (mechanical grab/backhoe, hydraulic suction dredging with/without overflow), (ii) the nature of the sediment bed, and (iii) the hydrodynamic conditions. A simple parameter to represent the spillage of dredged materials is the spill percentage (Rspill) of the initial load. In the case of cutter dredging and hopper dredging without overflow, sediment spillage is mostly low, with values in the range of 1% to 3%, The spill percentage is higher, in the range of 3% to 30%, for hopper dredging of mud with intensive overflow. Spilling of dredged materials also occurs at disposal sites. The spill percentage is generally low, with values in the range of 1% to 3%, if the load is dumped through bottom doors in deep water, creating a dynamic plume which descends rapidly to the bottom with cloud velocities of 1 m/s. The most accurate approach to study passive plume behavior is the application of a 3D model, which, however, is a major, time-consuming effort. A practical 1D plume dispersion model can help to identify the best parameter settings involved and to conduct fast scan studies. The proposed 1D model represents equations for dynamic plume behavior, as well as passive plume behavior including advection, diffusion and settling processes. Full article
(This article belongs to the Section Marine Environmental Science)
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28 pages, 7802 KiB  
Article
Anomalous Behavior in Weather Forecast Uncertainty: Implications for Ship Weather Routing
by Marijana Marjanović, Jasna Prpić-Oršić, Anton Turk and Marko Valčić
J. Mar. Sci. Eng. 2025, 13(6), 1185; https://doi.org/10.3390/jmse13061185 - 17 Jun 2025
Viewed by 615
Abstract
Ship weather routing is heavily dependent on weather forecasts. However, the predictive nature of meteorological models introduces an unavoidable level of uncertainty which, if not accounted for, can compromise navigational safety, operational efficiency, and environmental impact. This study examines the temporal degradation of [...] Read more.
Ship weather routing is heavily dependent on weather forecasts. However, the predictive nature of meteorological models introduces an unavoidable level of uncertainty which, if not accounted for, can compromise navigational safety, operational efficiency, and environmental impact. This study examines the temporal degradation of forecast accuracy across certain oceanographic and atmospheric variables, using a six-month dataset for the area of North Atlantic provided by the National Oceanic and Atmospheric Administration (NOAA). The analysis reveals distinct variable-specific uncertainty trends with wind speed forecasts exhibiting significant temporal fluctuation (RMSE increasing from 0.5 to 4.0 m/s), while significant wave height forecasts degrade in a more stable and predictable pattern (from 0.2 to 0.9 m). Confidence intervals also exhibit non-monotonic evolution, narrowing by up to 15% between 96–120-h lead times. To address these dynamics, a Python-based framework combines distribution-based modeling with calibrated confidence intervals to generate uncertainty bounds that evolve with forecast lead time (R2 = 0.87–0.93). This allows uncertainty to be quantified not as a static estimate, but as a function sensitive to both variable type and prediction horizon. When integrated into routing algorithms, such representations allow for route planning strategies that are not only more reflective of real-world meteorological limitations but also more robust to evolving weather conditions, demonstrated by a 3–7% increase in travel time in exchange for improved safety margins across eight test cases. Full article
(This article belongs to the Section Ocean Engineering)
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28 pages, 4445 KiB  
Article
Link Availability-Aware Routing Metric Design for Maritime Mobile Ad Hoc Network
by Shuaiheng Huai, Tianrui Liu, Yi Jiang, Yanpeng Dai, Feng Xue and Qing Hu
J. Mar. Sci. Eng. 2025, 13(6), 1184; https://doi.org/10.3390/jmse13061184 - 17 Jun 2025
Viewed by 448
Abstract
A maritime mobile ad hoc network (M-MANET) is an essential part of the maritime communication network and plays a key role in many maritime scenarios. However, the topology of M-MANET dynamically changes with the movement of vessels, which leads to unstable link states [...] Read more.
A maritime mobile ad hoc network (M-MANET) is an essential part of the maritime communication network and plays a key role in many maritime scenarios. However, the topology of M-MANET dynamically changes with the movement of vessels, which leads to unstable link states and poses the risk of data transmission interruption. In this paper, a mobility model for small unmanned surface vessels based on smooth Gaussian semi-Markovian and a trajectory prediction method for large vessels based on a bi-directional long short-term memory network are proposed to better simulate the nodes’ movement in the M-MANET. Then, a link available based routing metric is proposed for M-MANET scenarios, which incorporates factors of mobility model and vessel trajectory. Experiments demonstrate that compared with the benchmark methods, the proposed mobility model depicts the movement characteristics of vessels more accurately, the proposed trajectory prediction method achieves higher prediction accuracy and stability, the proposed routing metric scheme has a reduction of 14.59% in end-to-end delay, a 1.54% increase in packet delivery fraction, and a 4.43% increase in network throughput on average. Full article
(This article belongs to the Special Issue Advanced Studies in Marine Data Analysis)
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20 pages, 4048 KiB  
Article
Hydrodynamic Calculation and Analysis of a Complex-Shaped ROV Moving near the Wall Based on CFDs
by Mengjie Jiang, Chaohe Chen, Zhijia Suo and Yingkai Dong
J. Mar. Sci. Eng. 2025, 13(6), 1183; https://doi.org/10.3390/jmse13061183 - 17 Jun 2025
Viewed by 267
Abstract
Remotely operated vehicles (ROVs) face challenges in maneuvering and rapidly detecting and repairing large offshore platforms. The accurate research on the hydrodynamics of the ROV, which moves close to the wall, is of great significance for its maneuverability. This study uses computational fluid [...] Read more.
Remotely operated vehicles (ROVs) face challenges in maneuvering and rapidly detecting and repairing large offshore platforms. The accurate research on the hydrodynamics of the ROV, which moves close to the wall, is of great significance for its maneuverability. This study uses computational fluid dynamics (CFDs) to analyze the hydrodynamic characteristics of an ROV when it is moving near the wall, considering factors such as structural asymmetry, speed, and distance from the wall. This study applies multiple linear regression to extract relevant hydrodynamic coefficients and develops a mathematical model that simulates the impact of these factors on ROV performance. The results indicate that the wall’s influence on hydrodynamic forces is significant. Total resistance increases as the ROV moves closer to the wall, and the effect becomes more pronounced at higher speeds. Pressure differential resistance is the dominant factor affecting ROV performance, while viscous resistance remains low and is mostly unaffected by wall proximity. These findings provide valuable insights into calculating hydrodynamic coefficients and modeling the dynamics of ROVs with complex shapes operating near the wall. Full article
(This article belongs to the Section Ocean Engineering)
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12 pages, 3259 KiB  
Article
An Experimental Study on the Performance of Proton Exchange Membrane Fuel Cells with Marine Ion Contamination
by Shian Li, Li Zhang, Gaokui Chen, Ruiyang Zhang, Aolong Liu, Guogang Yang and Qiuwan Shen
J. Mar. Sci. Eng. 2025, 13(6), 1182; https://doi.org/10.3390/jmse13061182 - 17 Jun 2025
Viewed by 230
Abstract
Proton exchange membrane fuel cells (PEMFCs) have the advantages of high efficiency, a low operating temperature, and a pollution-free reaction. Therefore, PEMFCs have emerged as a viable clean energy solution for ships to reduce their carbon emissions. When PEMFCs operate in marine salt [...] Read more.
Proton exchange membrane fuel cells (PEMFCs) have the advantages of high efficiency, a low operating temperature, and a pollution-free reaction. Therefore, PEMFCs have emerged as a viable clean energy solution for ships to reduce their carbon emissions. When PEMFCs operate in marine salt spray environments, foreign ions entering the cathodes of fuel cells with air can cause a decline in cell performance. In this study, the effects of the cation type (K+, Na+, Mg2+, and Ca2+) and concentration (0.25 M and 0.5 M) on cell performance in terms of the polarization curve were systematically investigated using a fuel cell test system. Cell performance degradation was observed due to the existence of cations. The influence of the four cations on cell performance followed the rule of Ca2+ > Mg2+ > Na+ > K+. Meanwhile, cell performance decreased with an increase in concentration. When the fuel cell was not contaminated, the voltage was 0.645 V at a current density of 1 A/cm2. When the concentration was 0.5 M, the corresponding voltages were 0.594 V, 0.583 V, 0.559 V, and 0.300 V, respectively. In addition, fuel cells contaminated by NaNO3 and NaCl were compared. Due to the existence of Cl−, more severe performance degradation was observed when the fuel cells were contaminated by NaCl. Full article
(This article belongs to the Special Issue Research and Development of Green Ship Energy)
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18 pages, 21015 KiB  
Article
Machine Learning Beach Attendance Forecast Modelling from Automatic Video-Derived Counting
by Bruno Castelle, David Carayon, Jeoffrey Dehez, Sylvain Liquet, Vincent Marieu, Nadia Sénéchal, Sandrine Lyser, Jean-Philippe Savy and Stéphanie Barneix
J. Mar. Sci. Eng. 2025, 13(6), 1181; https://doi.org/10.3390/jmse13061181 - 17 Jun 2025
Viewed by 199
Abstract
Accurate predictions of beach user numbers are important for coastal management, resource allocation, and minimising safety risks, especially when considering surf-zone hazards. The present work applies an XGBoost model to predict beach attendance from automatically video-derived data, incorporating input variables such as weather, [...] Read more.
Accurate predictions of beach user numbers are important for coastal management, resource allocation, and minimising safety risks, especially when considering surf-zone hazards. The present work applies an XGBoost model to predict beach attendance from automatically video-derived data, incorporating input variables such as weather, waves, tide, and time (e.g., day hour, weekday). This approach is applied to data collected from Biscarrosse Beach during the summer of 2023, where beach attendance varied significantly (from 0 to 2031 individuals). Results indicate that the optimal XGBoost model achieved high predictive accuracy, with a coefficient of determination (R2) of 0.97 and an RMSE of 70.4 users, using daily mean weather data, tide and time as input variables, i.e., disregarding wave data. The model skilfully captures both day-to-day and hourly variability in attendance, with time of day (hour) and daily mean air temperature being the most influential variables. An XGBoost model using only daily mean temperature and hour of the day even shows good predictive accuracy (R2 = 0.90). The study emphasises the importance of daily mean weather data over instantaneous measurements, as beach users tend to plan visits based on forecasts. This model offers reliable, computationally inexpensive, and high-frequency (e.g., every 10 min) beach user predictions which, combined with existing surf-zone hazard forecast models, can be used to anticipate life risk at the beach. Full article
(This article belongs to the Section Coastal Engineering)
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19 pages, 4003 KiB  
Article
The Risk to the Undersea Engineering Ecosystem of Systems: Understanding Implosion in Confined Environments
by Craig Tilton and Arun Shukla
J. Mar. Sci. Eng. 2025, 13(6), 1180; https://doi.org/10.3390/jmse13061180 - 17 Jun 2025
Viewed by 277
Abstract
As humans continue to develop the undersea engineering ecosystem of systems, the consequences of catastrophic events must continue to be investigated and understood. Almost every undersea pressure vessel, from pipelines to sensors to unmanned vehicles, has the potential to experience a catastrophic collapse, [...] Read more.
As humans continue to develop the undersea engineering ecosystem of systems, the consequences of catastrophic events must continue to be investigated and understood. Almost every undersea pressure vessel, from pipelines to sensors to unmanned vehicles, has the potential to experience a catastrophic collapse, known as an implosion. This collapse can be caused by hydrostatic pressure or any combination of external loadings from natural disasters to pressure waves imparted by other implosion or explosion events. During an implosion, high-magnitude pressure waves can be emitted, which can cause adverse effects on surrounding structures, marine life, or even people. The imploding structure, known as an implodable volume, can be in a free-field or confined environment. Confined implosion is characterized by a surrounding structure that significantly affects the flow of fluid around the implodable volume. Often, the confining structure is cylindrical, with one closed end and one open end. This work seeks to understand the effect of fluid flow restriction on the physics of implosion inside a confining tube. To do so, a comprehensive experimental study is conducted using a unique experimental facility. Thin-walled aluminum cylinders are collapsed inside a confining tube within a large pressure vessel. High-speed photography and 3D Digital Image Correlation are used to gather structural displacement and velocities during the event while an array of dynamic pressure sensors capture the pressure data inside the confining tube. The results of this work show that by changing the size of the open end, referred to as the flow area ratio, there can be a significant effect on the structural deformations and implosion severity. It also reveals that only certain configurations of holes at the open end of the tube play a role in the dynamic pressure pulse measured at the closed end of the tube. By understanding the consequences of an implosion, designers can make decisions about where these pressure vessels should be in relation to other pressure vessels, critical infrastructure, marine life, or people. In the same way that engineers design for earthquakes and analyze the impact their structures have on the environment around them, contributors to the undersea engineering ecosystem should design with implosion in mind. Full article
(This article belongs to the Section Ocean Engineering)
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17 pages, 12011 KiB  
Article
Folding of Oceanic Crust Along the Davie Fracture Zone, Offshore Tanzania
by Xi Peng, Yuanyuan Zhou, Li Wang and Zhaoqian Liu
J. Mar. Sci. Eng. 2025, 13(6), 1179; https://doi.org/10.3390/jmse13061179 - 16 Jun 2025
Viewed by 238
Abstract
The Davie Fracture Zone (Davie FZ)—among the longest offshore transform systems in East Africa—mediated Madagascar’s southward displacement following Gondwana’s Early Jurassic breakup. This giant structure has a distinct topography and gravity field signals. However, it is buried by thick sediments in its northern [...] Read more.
The Davie Fracture Zone (Davie FZ)—among the longest offshore transform systems in East Africa—mediated Madagascar’s southward displacement following Gondwana’s Early Jurassic breakup. This giant structure has a distinct topography and gravity field signals. However, it is buried by thick sediments in its northern segment offshore Tanzania, hindering understanding of the internal structures and their origin. In this study, we applied 2-D multichannel seismic to analyze the structural characteristics and evolution of the Davie FZ. The Davie FZ is located in the oceanic domain, which is bordered by the landwards-dipping overthrust fault at the continent–ocean boundary. Volcano sediments atop the basement with undulating Moho reflection below depict a typical oceanic domain. Distinct compressive deformation characterized by the crustal undulation of around 40 km wavelength forms folded oceanic crust, and Late Jurassic sediments onlap onto the crest of the folded basement. The Davie FZ is localized in a corridor with the thickened oceanic crust and is presented by positive flower structures with faulted uplifted basement and deepened Moho. The Davie FZ evolved from a proto-transform fault located in Gondwana before the spreading of the West Somali Basin. During the Late Jurassic, a kinematic change shifted the spreading direction from NW–SE to N–S, resulting in a strike-slip of the Davie FZ and contemporaneous transpressional deformation offshore Tanzania. The Davie FZ is an excellent case to understand the tectonic-magmatic process forming this transform margin. Full article
(This article belongs to the Section Geological Oceanography)
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17 pages, 15281 KiB  
Article
Oil Film Detection for Marine Radar Image Using SBR Feature and Adaptive Threshold
by Yulong Yang, Jin Yan, Jin Xu, Xinqi Zhong, Yumiao Huang, Jianxun Rui, Min Cheng, Yuanyuan Huang, Yimeng Wang, Tao Liang, Zisen Lin and Peng Liu
J. Mar. Sci. Eng. 2025, 13(6), 1178; https://doi.org/10.3390/jmse13061178 - 16 Jun 2025
Viewed by 283
Abstract
Marine oil spills pose a serious and persistent threat to marine ecosystems, coastal resources, and global environmental health. These incidents not only disrupt ecological balance by damaging marine flora and fauna but also lead to long-term economic consequences for fisheries, tourism, and maritime [...] Read more.
Marine oil spills pose a serious and persistent threat to marine ecosystems, coastal resources, and global environmental health. These incidents not only disrupt ecological balance by damaging marine flora and fauna but also lead to long-term economic consequences for fisheries, tourism, and maritime industries. Owing to their rapid spread and often unpredictable occurrence, timely and accurate detection is essential for effective containment and mitigation. An efficient detection system can significantly enhance the responsiveness of emergency teams, enabling targeted interventions that minimize ecological damage and economic loss. This paper proposes a marine radar-based oil spill detection method that combines the Significance-to-Boundary Ratio (SBR) feature with an improved Sauvola adaptive thresholding algorithm. The raw radar data was firstly preprocessed through mean and median filtering, grayscale correction, and contrast enhancement. SBR features were then employed to extract coarse oil spill regions, which were further refined using an improved Sauvola thresholding algorithm followed by a denoising step to obtain fine-grained segmentation. Comparative experiments using different threshold values demonstrate that the proposed method achieves superior segmentation performance by better preserving oil spill boundaries and reducing background noise. Overall, the approach provides a robust and efficient solution for marine oil spill detection and monitoring. Full article
(This article belongs to the Special Issue Remote Sensing for Maritime Monitoring and Ship Surveillance)
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20 pages, 22127 KiB  
Article
Performance Analysis of Poppet Valves in Deep-Sea Hydraulic Systems: Considering Viscosity–Pressure Characteristics
by Pin-Jian Wang and Jia-Bin Wu
J. Mar. Sci. Eng. 2025, 13(6), 1177; https://doi.org/10.3390/jmse13061177 - 16 Jun 2025
Viewed by 261
Abstract
Deep-sea hydraulic systems, powering a wide range of numerous deep-sea operating equipment, employ many poppet valves to adjust the pressure and flow rate, thereby realizing precise movements of the actuators. With greater depths and ambient pressures, the hydraulic oil viscosity increases exponentially, leading [...] Read more.
Deep-sea hydraulic systems, powering a wide range of numerous deep-sea operating equipment, employ many poppet valves to adjust the pressure and flow rate, thereby realizing precise movements of the actuators. With greater depths and ambient pressures, the hydraulic oil viscosity increases exponentially, leading to a significant difference in the performance of the poppet valve compared to on-land usage and across varying depths. Based on the shear stress transport (SST) k-ω turbulence model and the dynamic mesh method, a computational fluid dynamics (CFD) model of the poppet valve was established. With the viscosity–pressure characteristics considered, the performance of the poppet valve was analyzed under different depths, different inlet flow rates, and different cracking pressures. The results indicate significant performance deterioration in poppet valves at increased depths, characterized by increased pressure loss and extended response rise time. At 11 km underwater, the pressure loss can be 7 MPa larger than the preset cracking pressure of 10 MPa, and the rise time is doubled compared with the land condition. It is recommended to use hydraulic oils with a lower initial viscosity and a slower increase in viscosity with pressure in deep sea conditions. Full article
(This article belongs to the Section Ocean Engineering)
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18 pages, 2944 KiB  
Article
The Development of a Coconut-Oil-Based Derived Polyol in a Polyurethane Matrix: A Potential Sorbent Material for Marine Oil Spill Applications
by John Louie L. Tefora, Tomas Ralph B. Tomon, Joy Ian Dan S. Ungang, Roberto M. Malaluan, Arnold A. Lubguban and Hernando P. Bacosa
J. Mar. Sci. Eng. 2025, 13(6), 1176; https://doi.org/10.3390/jmse13061176 - 16 Jun 2025
Viewed by 822
Abstract
Marine oil spills have caused significant environmental problems. Among the array of clean-up methods, the utilization of sorbents emerges as promising for removing and recovering oil from spills. Developing cost-effective, reliable, and eco-friendly material that efficiently and sustainably removes oil from water is [...] Read more.
Marine oil spills have caused significant environmental problems. Among the array of clean-up methods, the utilization of sorbents emerges as promising for removing and recovering oil from spills. Developing cost-effective, reliable, and eco-friendly material that efficiently and sustainably removes oil from water is increasingly seen as crucial and pressing. In the present study, we report the development of coco-polyurethane (PU) foam (CCF) through the conventional foaming process using varying amounts of coconut-oil-derived polyol (CODP) in a PU matrix. Characterization of the foams showed an increased ester band with the incorporation of COPD into the polyurethane networks and no direct influence of the cell size distribution on the surface morphology. Furthermore, this study highlighted the increasing CODP in every CCF formulation, showing high oil sorption and low water uptake due to its porous structure. The experimental results revealed that CCF is a potential candidate sorbent for the recovery of spilled oil. This signifies a significant leap towards reducing the dependency on petroleum in developing sorbent materials and advancing sustainable responses to oil spills in marine environments. Full article
(This article belongs to the Section Marine Pollution)
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27 pages, 8969 KiB  
Article
Sedimentary Environment and Organic Matter Enrichment Mechanism of the Lower Cambrian Shale in the Northern Margin of the Yangtze Platform
by Yineng Tan, Guangming Meng, Yue Feng, Wei Liu, Qiang Wang, Ping Gao and Xianming Xiao
J. Mar. Sci. Eng. 2025, 13(6), 1175; https://doi.org/10.3390/jmse13061175 - 15 Jun 2025
Viewed by 1062
Abstract
Current models of sedimentary environments and organic matter (OM) enrichment for the Lower Cambrian black shales in the Yangtze Platform have not yet incorporated its northern carbonate platform margin where the related research is lacked. This study focuses on the SNZ1 well in [...] Read more.
Current models of sedimentary environments and organic matter (OM) enrichment for the Lower Cambrian black shales in the Yangtze Platform have not yet incorporated its northern carbonate platform margin where the related research is lacked. This study focuses on the SNZ1 well in the northern carbonate platform margin, utilizing total organic carbon (TOC) content and major and trace element data to reveal the main controlling factors of OM enrichment during the Early Cambrian. The results show that the shale stratum is tentatively ascribed to the Lower Cambrian Stage 3 and that, during its deposition, the redox transitioned from anoxic to suboxic–oxic conditions, the hydrodynamic conditions weakened initially and then strengthened, the primary productivity first increased and then decreased, the paleoclimate shifted from arid–cold to warm–humid conditions, and the terrigenous clastic input gradually diminished. Overall, the OM enrichment is primarily controlled by preservation conditions. By systematically analyzing the data from the intraplatform basin to the deep-sea basin across the Yangtze Block, a model of the sedimentary environments and OM enrichment during the Early Cambrian was suggested. Additionally, this study highlights the intrinsic link between the expansion of oxygenated surface water and the Cambrian explosion. These results provide critical insights for shale gas exploration in this region. Full article
(This article belongs to the Section Geological Oceanography)
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19 pages, 9718 KiB  
Article
Structural Safety Assessment Based on Stress-Life Fatigue Analysis for T/C Nozzle Ring Blade
by Woo-Seok Jeon and Haechang Jeong
J. Mar. Sci. Eng. 2025, 13(6), 1174; https://doi.org/10.3390/jmse13061174 - 15 Jun 2025
Viewed by 713
Abstract
The performance of the turbocharger nozzle ring is a key factor in the overall operation of the main engine of the ship. Minimizing failure and damage caused by high exhaust gas temperature and pressure is essential. As a first step toward improving turbocharger [...] Read more.
The performance of the turbocharger nozzle ring is a key factor in the overall operation of the main engine of the ship. Minimizing failure and damage caused by high exhaust gas temperature and pressure is essential. As a first step toward improving turbocharger safety, this study performed 3D scanning of an aged nozzle ring to obtain its precise geometry and developed a corresponding numerical model. The boundary conditions of the numerical model were defined by the exhaust gas temperature and pressure at various engine output loads. Structural safety was assessed using static structural and stress-life fatigue analyses. A sharp increase in maximum equivalent stress and strain was observed at output loads of 85% and higher. At 25% load, the maximum fatigue life indicated 1.76 × 108 cycles, while at 100% load, the maximum damage index reached 1. A field performance test conducted at 85% of the main engine’s output load revealed severe damage under high-load conditions. Specifically, damage occurred at the contact area between the outer hoop and the tip of the blade’s trailing edge. This observed damage pattern closely aligned with the results predicted by the fatigue life analysis. The validity of the present study was confirmed through a comparative analysis of the fatigue life predictions and the field test results. Full article
(This article belongs to the Section Ocean Engineering)
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30 pages, 2673 KiB  
Article
Maritime Port Freight Flow Optimization with Underground Container Logistics Systems Under Demand Uncertainty
by Miaomiao Sun, Chengji Liang, Yu Wang and Salvatore Antonio Biancardo
J. Mar. Sci. Eng. 2025, 13(6), 1173; https://doi.org/10.3390/jmse13061173 - 15 Jun 2025
Viewed by 192
Abstract
As global trade and container transportation continue to grow, port collection and distribution systems face increasing challenges, including congestion, inefficiency, and environmental impact. Traditional ground-based transportation methods often exacerbate these issues, especially under uncertain demand conditions. This study aims to optimize freight flow [...] Read more.
As global trade and container transportation continue to grow, port collection and distribution systems face increasing challenges, including congestion, inefficiency, and environmental impact. Traditional ground-based transportation methods often exacerbate these issues, especially under uncertain demand conditions. This study aims to optimize freight flow allocation in port collection and distribution networks by integrating traditional and innovative transportation modes, including underground container logistics systems, under demand uncertainty. A stochastic optimization model is developed, incorporating transportation, environmental, carbon tax and subsidy, and congestion costs while satisfying various constraints, such as capacity limits, time constraints, and low-carbon transport requirements. The model is solved using a hybrid algorithm combining an improved Genetic Algorithm and Simulated Annealing (GA-SA) with Deep Q-Learning (DQN). Numerical experiments and case studies, particularly focusing on A Port, demonstrate that the proposed approach significantly reduces total operational costs, congestion, and environmental impacts while enhancing system robustness under uncertain demand conditions. The findings highlight the potential of underground logistics systems to improve port logistics efficiency, providing valuable insights for future port management strategies and the integration of sustainable transportation modes. Full article
(This article belongs to the Special Issue Smart and Low Carbon Emission-Oriented Maritime Traffic Management and Controlling)
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18 pages, 3291 KiB  
Article
Monocular Unmanned Boat Ranging System Based on YOLOv11-Pose Critical Point Detection and Camera Geometry
by Yuzhen Wu, Yucheng Suo, Xinqiang Chen, Yongsheng Yang, Han Zhang, Zichuang Wang and Octavian Postolache
J. Mar. Sci. Eng. 2025, 13(6), 1172; https://doi.org/10.3390/jmse13061172 - 14 Jun 2025
Viewed by 240
Abstract
Unmanned boat distance detection is an important foundation for autonomous navigation tasks of unmanned boats. Monocular vision ranging has the advantages of low hardware equipment requirements, simple deployment, and high efficiency of distance detection. Unmanned boats can sense the real-time navigational situation of [...] Read more.
Unmanned boat distance detection is an important foundation for autonomous navigation tasks of unmanned boats. Monocular vision ranging has the advantages of low hardware equipment requirements, simple deployment, and high efficiency of distance detection. Unmanned boats can sense the real-time navigational situation of waters through monocular vision ranging, providing data support for their autonomous navigation. This paper establishes a framework for unmanned boat distance detection. The framework extracts and recognizes the features of an unmanned boat through Yolov11m-pose and selects the key points of the ship for physical distance mapping. Using the camera calibration to obtain the pixel focal length, the main point coordinates and other parameters are obtained. The number of pixel points in the image key point to the image center pixel and the actual distance of the camera from the horizontal plane are combined with the focal length of the camera for triangular similarity conversion. These data are fused with the camera pitch angle and other parameters to obtain the final distance. At the same time, experimental verification of the key point detection model demonstrates that it fully meets the requirements for unmanned boat ranging tasks, as assessed by Precision, Recall, mAP50, mAP50-95 and other indicators. These indicators show that Yolov11m-pose has a better accuracy in the key point detection task with an unmanned boat. The verification experiments also illustrate the accuracy of the key point-based physical distance mapping compared with the traditional detection frame-based physical distance mapping, which was assessed by the mean squared error (MSE), the root mean square error (RMSE), and the mean absolute error (MAE). The metrics show that key point-based unmanned boat distance mapping has greater accuracy in a variety of environmental situations, which verifies the effectiveness of this approach. Full article
(This article belongs to the Section Ocean Engineering)
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15 pages, 3869 KiB  
Article
Sea Ice as a Driver of Fin Whale (Balaenoptera physalus) 20 Hz Acoustic Presence in Eastern Antarctic Waters
by Meghan G. Aulich, Agustin M. De Wysiecki, Brian S. Miller, Flore Samaran, Robert D. McCauley, Benjamin J. Saunders, Cristina D. S. Tollefsen and Christine Erbe
J. Mar. Sci. Eng. 2025, 13(6), 1171; https://doi.org/10.3390/jmse13061171 - 14 Jun 2025
Viewed by 692
Abstract
The environmental drivers of fin whale (Balaenoptera physalus) acoustic presence in Eastern Antarctic waters were investigated based on passive acoustic recordings from four sites, 2013–2019. Fin whale 20 Hz pulses were detected from late austral summer to early winter. Daily values [...] Read more.
The environmental drivers of fin whale (Balaenoptera physalus) acoustic presence in Eastern Antarctic waters were investigated based on passive acoustic recordings from four sites, 2013–2019. Fin whale 20 Hz pulses were detected from late austral summer to early winter. Daily values of sea-ice concentration (SIC) were compared with the number of days with fin whale 20 Hz acoustic presence using a generalized additive model approach. At the Southern Kerguelen Plateau, Casey, and Dumont d’Urville sites, SIC correlated with fin whale calling activity, but less so at the Prydz site. Changes in SIC between sites resulted in variation in acoustic presence: Earlier sea-ice formation at Dumont d’Urville resulted in less acoustic presence in comparison to the Southern Kerguelen Plateau, where sea ice formed later in the season. Interannual variability in SIC impacted yearly acoustic presence, with a later onset of high SIC resulting in greater acoustic presence and later departure (migration timing) of the animals. Identifying the environmental drivers of fin whale presence is key to informing how this migratory species may be affected by environmental variability resulting from climate change. Full article
(This article belongs to the Special Issue Advances in Ecological Modelling of Marine Mammal Habitats of Importance)
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20 pages, 5625 KiB  
Article
Assessing Chlorophyll-a Variability and Its Relationship with Decadal Climate Patterns in the Arabian Sea
by Muhsan Ali Kalhoro, Veeranjaneyulu Chinta, Muhammad Tahir, Chunli Liu, Lixin Zhu, Zhenlin Liang, Aidah Baloch and Jun Song
J. Mar. Sci. Eng. 2025, 13(6), 1170; https://doi.org/10.3390/jmse13061170 - 14 Jun 2025
Viewed by 382
Abstract
The Arabian Sea has undergone significant warming since the mid-20th century, highlighting the importance of assessing how decadal climate patterns influence chlorophyll-a (Chl-a) and broader marine ecosystem dynamics. This study investigates the variability of Chl-a, sea surface temperature (SST), and sea level anomaly [...] Read more.
The Arabian Sea has undergone significant warming since the mid-20th century, highlighting the importance of assessing how decadal climate patterns influence chlorophyll-a (Chl-a) and broader marine ecosystem dynamics. This study investigates the variability of Chl-a, sea surface temperature (SST), and sea level anomaly (SLA) over the past three decades, and their relationships with the Pacific Decadal Oscillation (PDO) and the Atlantic Multidecadal Oscillation (AMO). The mean Chl-a concentration was 1.10 mg/m3, with peak levels exceeding 2 mg/m3 between 2009 and 2013, and the lowest value (0.6 mg/m3) was recorded in 2014. Elevated Chl-a levels were consistently observed in February and March across both coastal and offshore regions. Empirical orthogonal function (EOF) analysis revealed distinct spatial patterns in Chl-a and SST, indicating dynamic regional variability. The SST increased by 0.709 °C over the past four decades, accompanied by a steady rise in the SLA of approximately 1 cm. The monthly mean Chl-a exhibited a strong inverse relationship with both the SST and SLA and a positive correlation with SST gradients (R2 > 0.5). A positive correlation (R2 > 0.5) was found between the PDO and Chl-a, whereas the PDO was negatively correlated with the SST and SLA. In contrast, the AMO was negatively correlated with Chl-a but positively associated with warming and SLA rise. These findings underline the contrasting roles of the PDO and AMO in modulating productivity and ocean dynamics in the Arabian Sea. This study emphasizes the need for continued monitoring to improve predictions of ecosystem responses under future climate change scenarios. Full article
(This article belongs to the Section Physical Oceanography)
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18 pages, 2540 KiB  
Article
A Computational Study on the Excitation Forces of Partially Submerged Propellers for High-Speed Boats
by Fangshuai Wei, Yujun Liu, Ji Wang, Rui Li and Lin Pang
J. Mar. Sci. Eng. 2025, 13(6), 1169; https://doi.org/10.3390/jmse13061169 - 13 Jun 2025
Viewed by 255
Abstract
During high-speed navigation, boat propellers often become partially exposed due to elevated sailing speeds. This condition results in a unique operational scenario where propellers are only partially submerged. Conducting computational studies on the excitation of propellers under such circumstances is essential for optimizing [...] Read more.
During high-speed navigation, boat propellers often become partially exposed due to elevated sailing speeds. This condition results in a unique operational scenario where propellers are only partially submerged. Conducting computational studies on the excitation of propellers under such circumstances is essential for optimizing the dynamic performance of the shafting system. A theoretical calculation method for propeller performance was developed based on the principles of fluid dynamics relevant to water entry, leading to a computational method for determining excitation forces in this specific operational condition. This method was subsequently refined through appropriate adjustments using ANSYS Fluent software to simulate the behavior of partially submerged propellers. The findings highlighted the accuracy of the proposed model in predicting the pulsation of six force components across three distinct directions: along the propeller shaft, vertical, and lateral. Specifically, for a single blade (Blade 1), the pulsation amplitude of the vertical force (Fx) constituted 82.1% of its maximum peak magnitude and equated to 57.5% of the blade’s mean thrust. Analogously, the lateral force (Fz) pulsation amplitude represented 53.3% of its maximum peak magnitude and 40.0% of the mean thrust. These findings indicate the presence of significant unsteady hydrodynamic loads. Furthermore, a visualization approach was presented to analyze blade load phasing, offering insights relevant to the arrangement of blades on partially submerged propellers. Full article
(This article belongs to the Section Ocean Engineering)
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27 pages, 6763 KiB  
Article
Capacity Estimation of Lithium-Ion Battery Systems in Fuel Cell Ships Based on Deep Learning Model
by Xiangguo Yang, Jia Tang, Qijia Song, Yifan Liu, Lin Liu, Xingwei Zhou, Yuelin Chen and Telu Tang
J. Mar. Sci. Eng. 2025, 13(6), 1168; https://doi.org/10.3390/jmse13061168 - 13 Jun 2025
Viewed by 242
Abstract
The capacity estimation of lithium-ion batteries, serving as an auxiliary power source in fuel cell vessels, is crucial for ensuring system stability and enhancing operational efficiency. Accurate capacity estimation technology not only helps extend battery lifespan but also enhances the energy management and [...] Read more.
The capacity estimation of lithium-ion batteries, serving as an auxiliary power source in fuel cell vessels, is crucial for ensuring system stability and enhancing operational efficiency. Accurate capacity estimation technology not only helps extend battery lifespan but also enhances the energy management and scheduling capabilities of the entire vessel. To address the challenge of accurately estimating lithium-ion battery capacity under complex operating conditions, this study extracts universal health factors from battery data under varied charging and discharging scenarios and combines these with a deep learning model to enhance prediction accuracy. First, battery data from three complex conditions are analyzed, extracting partial charge and discharge data. The distance correlation coefficient calculates the correlation between each factor and the capacity sequence, informing the priority of universal health factors. A TCN-BiGRU model is then developed, with hyperparameters determined by the Kepler optimization algorithm (KOA). Cells from a battery pack under consistent conditions are used for training, while other cells in the same pack serve as the test set. Evaluation metrics include mean absolute error (MAE) and root-mean-square error (RMSE). The testing shows that the MAE and RMSE for full-life capacity estimation remain around 1%, with most cells achieving values under 1%. The results indicate that the proposed method effectively aids in accurate capacity estimation for individual cells in complex operating environments. Full article
(This article belongs to the Special Issue Marine Fuel Cell Technology: Latest Advances and Prospects)
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13 pages, 1504 KiB  
Article
Prototype Mobile Vision System for Automatic Length Estimation of Olive Flounder (Paralichthys olivaceus) in Indoor Aquaculture
by Inyeong Kwon, Hang Thi Phuong Nguyen, Paththige Waruni Prasadini Fernando, Hieyong Jeong, Sungju Jung and Taeho Kim
J. Mar. Sci. Eng. 2025, 13(6), 1167; https://doi.org/10.3390/jmse13061167 - 13 Jun 2025
Viewed by 273
Abstract
Real-time estimation of fish growth offers multiple benefits in indoor aquaculture, including reduced labor, lower operational costs, improved feeding efficiency, and optimized harvesting schedules. This study presents a low-cost, vision-based method for estimating the body length and weight of olive flounder (Paralichthys [...] Read more.
Real-time estimation of fish growth offers multiple benefits in indoor aquaculture, including reduced labor, lower operational costs, improved feeding efficiency, and optimized harvesting schedules. This study presents a low-cost, vision-based method for estimating the body length and weight of olive flounder (Paralichthys olivaceus) in tank environments. A 5 × 5 cm reference grid is placed on the tank bottom, and images are captured using two fixed-position RGB smartphone cameras. Pixel measurements from the images are converted into millimeters using a calibrated pixel-to-length relationship. The system calculates fish length by detecting contour extremities and applying Lagrange interpolation. Based on the estimated length, body weight is derived using a power regression model. Accuracy was validated using both manual length measurements and Bland–Altman analysis, which indicated a mean bias of −0.007 cm and 95% limits of agreement from −0.475 to +0.462 cm, confirming consistent agreement between methods. The mean absolute error (MAE) and mean squared error (MSE) were 0.11 cm and 0.025 cm2, respectively. While optimized for benthic species such as olive flounder, this system is not suitable for free-swimming species. Overall, it provides a practical and scalable approach for non-invasive monitoring of fish growth in commercial indoor aquaculture. Full article
(This article belongs to the Special Issue New Challenges in Marine Aquaculture Research—2nd Edition)
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