Journal of Marine Science and Engineering

36 pages, 6078 KB

Open AccessArticle

The Numerical Evaluation of Hydrate Saturation in Marine Sediment During the Injection Process of Self-Heat Generating Fluid

by Kewei Zhang, Kaixiang Shen, Yanjiang Yu, Yingsheng Wang, Jiawei Zhou and Jing Zeng

J. Mar. Sci. Eng. 2025, 13(9), 1772; https://doi.org/10.3390/jmse13091772 (registering DOI) - 13 Sep 2025

Abstract

Marine gas hydrates are recognized as a promising offshore energy resource. Self-heat fluid injection is an innovative thermal-enhanced gas recovery technique for hydrate exploitation engineering. This study numerically investigates hydrate saturation during the self-heating reagent injection process in a sub-sea hydrate reservoir, decoupled [...] Read more.

Marine gas hydrates are recognized as a promising offshore energy resource. Self-heat fluid injection is an innovative thermal-enhanced gas recovery technique for hydrate exploitation engineering. This study numerically investigates hydrate saturation during the self-heating reagent injection process in a sub-sea hydrate reservoir, decoupled from gas production interference. This process employs two consecutive stages: reactive chemical flow stage followed by non-reactive flow stage. The simulation output parameters encompass reservoir temperature, fluid saturation, thermal conductivity, and heat flow rate. The base case demonstrates that fluid injection elevates reservoir temperature from 13.0 °C to 29.3 °C and reduces hydrate saturation from 0.40 to 0.21 through coupled heat–mass transfer mechanisms during the reactive flow stage. In the consequent non-reactive flow stage, hydrate saturation decreases to zero. Sensitivity analysis reveals that initial permeability variation governs the hydrate saturation and temperature during the non-reactive phase. The permeability range of less than 15 mD is the optimal threshold preventing hydrate reformation during fluid injection. 55–70 mD permeability triggers severe secondary hydrate generation, which decreases the fluid application feasibility. Fluid flooding demonstrates superior hydrate dissociation efficacy compared to in situ thermal stimulation. This study develops a novel simulation approach to characterize marine hydrate saturation dynamics. Full article

(This article belongs to the Special Issue Advances in Marine Gas Hydrate Exploration and Discovery—2nd Edition)

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23 pages, 9952 KB

Open AccessArticle

Extremal-Aware Deep Numerical Reinforcement Learning Fusion for Marine Tidal Prediction

by Xiaodao Chen, Gongze Zheng and Yuewei Wang

J. Mar. Sci. Eng. 2025, 13(9), 1771; https://doi.org/10.3390/jmse13091771 (registering DOI) - 13 Sep 2025

Abstract

In the context of global climate change and accelerated urbanization, coastal cities face severe threats from storm surges, and accurately predicting coastal water level changes during storm surges has become a core technological demand for disaster prevention and reduction. Storm surges are caused [...] Read more.

In the context of global climate change and accelerated urbanization, coastal cities face severe threats from storm surges, and accurately predicting coastal water level changes during storm surges has become a core technological demand for disaster prevention and reduction. Storm surges are caused by atmospheric pressure and wind conditions, and their destructive power is closely related to the morphology of the coastline. Traditional tide level prediction models often face difficulties in boundary condition parameterization. Tide level changes result from the combined effect of various complex processes. In past prediction studies, harmonic analysis and numerical simulations have dominated, each with their own limitations. Although machine learning applications in tide prediction have garnered attention, issues such as data inconsistency or missing data still exist. The physical–data fusion approach aims to overcome the limitations of single methods but still faces some challenges. This paper proposes a Deep-Numerical-Reinforcement learning fusion prediction model (DNR), which adopts ensemble learning. First, deep learning models and the numerical model Finite-Volume Coastal Ocean Model (FVCOM) are used to predict tide levels at different tide stations, and then a fusion approach based on the improved reinforcement learning model DDPG_dual is applied for model assimilation. This reinforcement learning fusion model includes a module specifically designed to handle tide extreme points. In the case of the Typhoon Mangkhut storm surge, the DNR model achieved the best results for tide level predictions at six tide stations in the South China Sea. Full article

(This article belongs to the Section Coastal Engineering)

19 pages, 2134 KB

Open AccessArticle

Development of a Gear-Based Fisheries Management Index Incorporating Operational Metrics and Ecosystem Impact Indicators in Korean Fisheries

by Inyeong Kwon, Gun-Ho Lee, Young Il Seo, Heejoong Kang, Jihoon Lee and Bo-Kyu Hwang

J. Mar. Sci. Eng. 2025, 13(9), 1770; https://doi.org/10.3390/jmse13091770 (registering DOI) - 13 Sep 2025

Abstract

Traditional single-species fisheries management has proven inadequate for capturing ecosystem interactions, leading to a shift toward ecosystem-based approaches. In Korea, diverse small- and medium-scale with varying gear types, production volumes, and practices require management tools that address both ecological and industrial needs. This [...] Read more.

Traditional single-species fisheries management has proven inadequate for capturing ecosystem interactions, leading to a shift toward ecosystem-based approaches. In Korea, diverse small- and medium-scale with varying gear types, production volumes, and practices require management tools that address both ecological and industrial needs. This study developed a Gear-based Fisheries Management Index (GFMI) for 24 coastal and offshore fisheries in Korea. The framework, based on the “ideal gear attributes” defined by ICES, is structured around three objectives: gear controllability, environmental sustainability, and operational functionality. Sub-indicators and weights were derived through expert consultation using the Analytic Hierarchy Process and standardized with Z-scores from national statistics, including production volume, license numbers, and accident rates. Results show that in coastal fisheries, coastal gillnets (61.7) and coastal improved stow nets (60.7) recorded the highest scores, largely due to negative impacts such as bycatch, reproductive capacity, and gear loss. Coastal purse seines (40.9) received the lowest score, reflecting species selectivity advantages. In offshore fisheries, large bottom pair trawls (71.8) and Southwestern medium-size bottom pair trawl (69.3) ranked highest, indicating strong habitat impacts. While coastal improved stow nets, large purse seines, and large trawls performed well in operational functionality, high costs and efficiency constraints remain key vulnerabilities. Full article

(This article belongs to the Special Issue Marine Fishing Gear and Aquacultural Engineering)

26 pages, 3513 KB

Open AccessArticle

Coupled Simulation Study on the High-Pressure Air Expulsion from Submarine Ballast Tanks and Emergency Surfacing Dynamics

by Jiabao Chen, Likun Peng, Bangjun Lv, Wei Pan and Yong Wang

J. Mar. Sci. Eng. 2025, 13(9), 1769; https://doi.org/10.3390/jmse13091769 (registering DOI) - 13 Sep 2025

Abstract

Emergency surfacing acts as the final line of defense in preserving the operational viability of submarines, playing a crucial role in their safety. To investigate the dynamic characteristics of submarine emergency surfacing, utilizing whole moving mesh technology, a method for coupled simulation of [...] Read more.

Emergency surfacing acts as the final line of defense in preserving the operational viability of submarines, playing a crucial role in their safety. To investigate the dynamic characteristics of submarine emergency surfacing, utilizing whole moving mesh technology, a method for coupled simulation of high-pressure air blowing out water tanks and emergency surfacing motion of submarines is proposed, enhancing the simulation’s fidelity to real-world dynamics. Based on meeting the requirements for simulation accuracy, utilizing the coupled simulation model, this study explored the effects of varying expulsion pressures on submarine motion parameters including depth, roll, pitch, and yaw angles. The findings indicate that the hull emerges slightly earlier and reaches a marginally higher point when coupling effects are accounted for compared to scenarios where these effects are neglected. At consistent expulsion pressures, as the pitch and roll angles increase and the back pressure decreases, the expulsion rate from the ballast tank accelerates. Higher expulsion pressures result in quicker surfacing of the hull, smaller amplitude of pitch angles, and larger amplitudes of roll angles, while the changes in yaw angle displayed no clear pattern. The methodologies and conclusions of this study offer valuable insights for the design and operational strategies of actual submarines. Full article

(This article belongs to the Special Issue Advanced Studies in Ship Fluid Mechanics)

25 pages, 5005 KB

Open AccessArticle

A Study on the Evolution Law of the Early Nonlinear Plastic Shock Response of a Ship Subjected to Underwater Explosions

by Kun Zhao, Xuan Yao, Renjie Huang, Hao Chen, Xiongliang Yao and Qiang Yin

J. Mar. Sci. Eng. 2025, 13(9), 1768; https://doi.org/10.3390/jmse13091768 (registering DOI) - 13 Sep 2025

Abstract

Early-stage dynamic responses of naval structures under underwater explosion shock loads exhibit high-frequency, intense amplitude fluctuations and short durations, serving as critical factors for the development of plastic deformation and other damage characteristics. These structural dynamics demonstrate prominent nonlinear and non-stationary features. This [...] Read more.

Early-stage dynamic responses of naval structures under underwater explosion shock loads exhibit high-frequency, intense amplitude fluctuations and short durations, serving as critical factors for the development of plastic deformation and other damage characteristics. These structural dynamics demonstrate prominent nonlinear and non-stationary features. This study focuses on the nonlinear evolutionary patterns of early-stage plastic shock responses in underwater explosion-impacted ship structures. Utilizing phase space reconstruction, unimodal mapping, and symbolic dynamics theory, we analyze the nonlinear and non-stationary characteristics along with their evolutionary patterns in experimental data. First, scaled model experiments under varying shock factors were conducted based on a stiffened cylindrical shell prototype, investigating the spatiotemporal evolution of nonlinear and non-stationary dynamic responses under different shock loads while characterizing their uncertainty features. Second, model tests were performed on deck-type cabin structures and plate frameworks derived from a naval vessel’s deck prototype, further analyzing the evolutionary patterns of early-stage plastic dynamic responses and verifying the method’s effectiveness and universality. Research findings indicate that (1) early-stage plastic shock responses of ships under underwater explosions exhibit multiple dynamical behaviors including chaotic motion, periodic motion, and quasi-periodic motion, and (2) during the initial plastic phase, orbital parameters approximate 0.8, providing guidance for test condition setup and initial parameter selection in underwater explosion experiments on naval structures. Full article

(This article belongs to the Section Ocean Engineering)

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18 pages, 2342 KB

Open AccessArticle

Scale-Adaptive Continuous Wavelet Transform for Energy-Envelope Extraction and Instantaneous-Frequency Characterization in High-Resolution Sub-Bottom Profiling

by Doo-Pyo Kim, Sang-Hee Lee and Sung-Bo Kim

J. Mar. Sci. Eng. 2025, 13(9), 1767; https://doi.org/10.3390/jmse13091767 (registering DOI) - 12 Sep 2025

Abstract

In marine seismic surveys, the indistinguishability of subsurface boundaries caused by the superimposition of the acoustic signals reflected from it, particularly at specific frequency ranges characterized by strong spectral interference, reduces the resolution of the seismic record. We processed sub-bottom profiler data, acquired [...] Read more.

In marine seismic surveys, the indistinguishability of subsurface boundaries caused by the superimposition of the acoustic signals reflected from it, particularly at specific frequency ranges characterized by strong spectral interference, reduces the resolution of the seismic record. We processed sub-bottom profiler data, acquired using a Bubble Pulser (nominal central frequency: ~400 Hz; effective bandwidth extending to ~1 kHz), (i) by extracting continuous wavelet transform (CWT) coefficients at the dominant energy scale to form the envelope and (ii) by applying Hilbert-based instantaneous frequency analysis to characterize medium-dependent spectral shifts. Envelope accuracy was benchmarked against four conventional filters using the sum of squared error (SSE) relative to a cubic-spline reference. CWT yielded the lowest SSE, outperforming low-pass 1 kHz and band-pass 400–1000 Hz; band-pass 400–650 Hz and low-pass 650 Hz were the least effective. Instantaneous-frequency trends differentiated rock, sand, and mud layers. Thus, compared to fixed-band filters, the scale-adaptive CWT envelope replicates raw energy more faithfully, while frequency attributes improve sediment classification. Low-pass filtering at 1000 Hz provides a more accurate representation of energy distribution than does bandpass filtering, particularly in the 400–650 Hz range. The integrated workflow—a robust, parameter-light alternative for high-resolution stratigraphic interpretation—enhances offshore engineering safety. Full article

(This article belongs to the Section Geological Oceanography)

21 pages, 1241 KB

Open AccessArticle

Neural Network-Based Ship Power Load Forecasting

by Haozheng Liu, Chengjun Qiu, Wei Qu, Wei He, Yuan Zhuang, Puze Li, Huili Hao, Wenhao Wang, Zizi Zhao and Jiahua Su

J. Mar. Sci. Eng. 2025, 13(9), 1766; https://doi.org/10.3390/jmse13091766 (registering DOI) - 12 Sep 2025

Abstract

This study combines an experimental semi-physical simulation model of an electric propulsion tugboat with four different neural networks to create a real-time simulation model for forecasting total power loads with small samples. The results of repeated experiments demonstrate that the BP neural network [...] Read more.

This study combines an experimental semi-physical simulation model of an electric propulsion tugboat with four different neural networks to create a real-time simulation model for forecasting total power loads with small samples. The results of repeated experiments demonstrate that the BP neural network effectively forecasts the power load. Subsequently, addressing the limitations of traditional BP neural networks, an optimization approach employing an enhanced particle swarm algorithm and attention mechanism was developed, thereby improving the model’s prediction accuracy and robustness. The experiment shows that the improved prediction model achieves an R² value of 97.42%, demonstrating its effectiveness in forecasting changes in the short-term power load of ships as parameters change. In actual operation, ships can allocate power reasonably and in a timely manner according to the load forecast results, thereby improving the efficiency of the power grid. Full article

(This article belongs to the Section Ocean Engineering)

25 pages, 28048 KB

Open AccessArticle

Simulation of Non-Stationary Mobile Underwater Acoustic Communication Channels Based on a Multi-Scale Time-Varying Multipath Model

by Honglu Yan, Songzuo Liu, Chenyu Pan, Biao Kuang, Siyu Wang and Gang Qiao

J. Mar. Sci. Eng. 2025, 13(9), 1765; https://doi.org/10.3390/jmse13091765 - 12 Sep 2025

Abstract

Traditional Underwater Acoustic Communication (UAC) typically assumes static or slowly varying channels over short observation periods and models multipath amplitude fluctuations with single-state statistical distributions. However, field measurements in shallow-water high-speed mobile scenarios reveal that the combined effects of rapid platform motion and [...] Read more.

Traditional Underwater Acoustic Communication (UAC) typically assumes static or slowly varying channels over short observation periods and models multipath amplitude fluctuations with single-state statistical distributions. However, field measurements in shallow-water high-speed mobile scenarios reveal that the combined effects of rapid platform motion and dynamic environments induce multi-scale time-varying amplitude characteristics. These include distance-dependent attenuation, fluctuations in average energy, and rapid random variations. This observation directly challenges traditional single-state models and wide-sense stationary assumptions. To address this, we propose a multi-scale time-varying multipath amplitude model. Using singular spectrum analysis, we decompose amplitude sequences into hierarchical components: large-scale components modeled via acoustic propagation physics; medium-scale components characterized by Hidden Markov Models; and small-scale components described by zero-mean Gaussian distributions. Building on this model, we further develop a time-varying impulse response simulation framework validated with experimental data. The results demonstrate superior performance over conventional single-state distribution and autoregressive models in statistical distribution matching, temporal dynamics representation, and communication performance testing. The model effectively characterizes non-stationary time-varying channels, supporting high-precision modeling and simulation for mobile UAC systems. Full article

(This article belongs to the Special Issue Underwater Acoustics: Advances in Modelling, Measurement, and Technological Applications)

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21 pages, 8239 KB

Open AccessArticle

Energy Minimization for Underwater Multipath Time-Delay Estimation

by Miao Feng, Shiliang Fang, Liang An, Chuanqi Zhu, Shuxia Huang, Qing Fan and Yifan Zhou

J. Mar. Sci. Eng. 2025, 13(9), 1764; https://doi.org/10.3390/jmse13091764 - 12 Sep 2025

Abstract

To address the multipath delay estimation problem in distributed hydrophone passive localization systems, a global energy minimization-based method is proposed in this paper. In this method, correlation pulses are treated as tracking targets, and their trajectories are estimated from correlograms formed by multiple [...] Read more.

To address the multipath delay estimation problem in distributed hydrophone passive localization systems, a global energy minimization-based method is proposed in this paper. In this method, correlation pulses are treated as tracking targets, and their trajectories are estimated from correlograms formed by multiple frames. Specifically, an energy function is designed to jointly encode pulse similarity, motion continuity, trajectory persistence, data fidelity, and regularization, thereby reformulating multipath delay estimation as a global optimization problem. In order to balance the discreteness of observations and the continuity of trajectories, the optimization process is implemented alternating between discrete association (solved via

α

-expansion) and continuous trajectory fitting (using weighted cubic splines). Furthermore, a dynamic hypothesis space expansion strategy based on trajectory merging and splitting is introduced to improve robustness while accelerating convergence. By exploiting both the intrinsic characteristics of correlation pulses in multi-frame processing and the physical properties of motion trajectories, the proposed method achieves higher tracking accuracy without requiring prior knowledge of the number of delay trajectories in a noisy environment. Numerical simulations under various noise conditions and sea trial results validate the superiorities of the proposed multipath delay estimation method. Full article

(This article belongs to the Section Ocean Engineering)

28 pages, 1179 KB

Open AccessArticle

Fully Nonlinear Simulation of the Hydrodynamic Performance of a Submerged Cylindrical Wave Energy Converter in the Presence of Current

by Yihui Xia, Bin Zhang, Changxin Tao and Lixian Wang

J. Mar. Sci. Eng. 2025, 13(9), 1763; https://doi.org/10.3390/jmse13091763 - 12 Sep 2025

Abstract

A potential flow theory-based fully nonlinear 2D NWT is developed in the time domain to investigate the hydrodynamic performance of a submerged circular cylindrical WEC device under combined wave–current conditions. The hydrodynamic force on the submerged cylinder is evaluated using the acceleration potential [...] Read more.

A potential flow theory-based fully nonlinear 2D NWT is developed in the time domain to investigate the hydrodynamic performance of a submerged circular cylindrical WEC device under combined wave–current conditions. The hydrodynamic force on the submerged cylinder is evaluated using the acceleration potential method coupled with the desingularized boundary integral equation method (DBIEM). The impacts of the wave height, current speed, and parameters of the power take-off mechanism on the extracted power capability of the WEC device are investigated. The results show that for the scenario of an opposing current, the dimensionless mean extracted power is reduced by as much as 14.3% with increasing wave height. Except for long waves, the extracted power under a co-flowing current exceeds that of the current-free case and an opposing current yields lower power. In contrast to the current-free scenario, the peak power extraction point shifts to slightly higher values of the spring and damper constants when the current is co-flowing, whereas the opposite trend is observed for the opposing current. Full article

(This article belongs to the Section Marine Energy)

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17 pages, 7446 KB

Open AccessArticle

Structural Response and Volume Change Characteristics of Tuna Cages Equipped with External Egg Collection Nets

by Gun-Ho Lee, Inyeong Kwon and Seung-Cheol Ji

J. Mar. Sci. Eng. 2025, 13(9), 1762; https://doi.org/10.3390/jmse13091762 - 12 Sep 2025

Abstract

The installation of an egg collection net in the upper section of a Pacific bluefin tuna (Thunnus orientalis) cage (diameter 25 m × height 15 m) raises concerns regarding the potential compromise of cage stability due to the fine mesh size. [...] Read more.

The installation of an egg collection net in the upper section of a Pacific bluefin tuna (Thunnus orientalis) cage (diameter 25 m × height 15 m) raises concerns regarding the potential compromise of cage stability due to the fine mesh size. This study addresses two primary questions: (1) How can the egg collection net be deployed effectively without undermining cage stability? (2) What are the effects of the egg collection net on the cage volume and shape under varying current conditions? To investigate these questions, a mass–spring interaction model was developed to simulate the contact behavior between net structures, and numerical simulations were performed under various current speeds and sinker weight conditions. The results indicate that optimal deployment is achieved when a sinker weight of 78.5 N per meter is applied along the lower perimeter of the egg collection net. The additional volume reduction induced by the egg collection net was minimal (0.01–0.54%), falling within the natural range of flow-induced fluctuations. These findings lay the groundwork for the development of more robust and efficient bluefin tuna aquaculture systems. Full article

(This article belongs to the Special Issue Marine Fishing Gear and Aquacultural Engineering)

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20 pages, 3921 KB

Open AccessArticle

Design of an Experimental Teaching Platform for Flow-Around Structures and AI-Driven Modeling in Marine Engineering

by Hongyang Zhao, Bowen Zhao, Xu Liang and Qianbin Lin

J. Mar. Sci. Eng. 2025, 13(9), 1761; https://doi.org/10.3390/jmse13091761 - 11 Sep 2025

Abstract

Flow past bluff bodies (e.g., circular cylinders) forms a canonical context for teaching external flow separation, vortex shedding, and the coupling between surface pressure and hydrodynamic forces in offshore engineering. Conventional laboratory implementations, however, often fragment local and global measurements, delay data feedback, [...] Read more.

Flow past bluff bodies (e.g., circular cylinders) forms a canonical context for teaching external flow separation, vortex shedding, and the coupling between surface pressure and hydrodynamic forces in offshore engineering. Conventional laboratory implementations, however, often fragment local and global measurements, delay data feedback, and omit intelligent modeling components, thereby limiting the development of higher-order cognitive skills and data literacy. We present a low-cost, modular, data-enabled instructional hydrodynamics platform that integrates a transparent recirculating water channel, multi-point synchronous circumferential pressure measurements, global force acquisition, and an artificial neural network (ANN) surrogate. Using feature vectors composed of Reynolds number, angle of attack, and submergence depth, we train a lightweight AI model for rapid prediction of drag and lift coefficients, closing a loop of measurement, prediction, deviation diagnosis, and feature refinement. In the subcritical Reynolds regime, the measured circumferential pressure distribution for a circular cylinder and the drag and lift coefficients for a rectangular cylinder agree with empirical correlations and published benchmarks. The ANN surrogate attains a mean absolute percentage error of approximately 4% for both drag and lift coefficients, indicating stable, physically interpretable performance under limited feature inputs. This platform will facilitate students’ cross-domain transfer spanning flow physics mechanisms, signal processing, feature engineering, and model evaluation, thereby enhancing inquiry-driven and critical analytical competencies. Key contributions include the following: (i) a synchronized local pressure and global force dataset architecture; (ii) embedding a physics-interpretable lightweight ANN surrogate in a foundational hydrodynamics experiment; and (iii) an error-tracking, iteration-oriented instructional workflow. The platform provides a replicable pathway for transitioning offshore hydrodynamics laboratories toward an integrated intelligence-plus-data literacy paradigm and establishes a foundation for future extensions to higher Reynolds numbers, multiple body geometries, and physics-constrained neural networks. Full article

(This article belongs to the Special Issue The Application of Artificial Intelligence and Machine Learning in a Marine Context - Edition II)

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34 pages, 16782 KB

Open AccessArticle

Ultra-Short-Term Prediction of Monopile Offshore Wind Turbine Vibration Based on a Hybrid Model Combining Secondary Decomposition and Frequency-Enhanced Channel Self-Attention Transformer

by Zhenju Chuang, Yijie Zhao, Nan Gao and Zhenze Yang

J. Mar. Sci. Eng. 2025, 13(9), 1760; https://doi.org/10.3390/jmse13091760 - 11 Sep 2025

Abstract

Ice loads continue to pose challenges to the structural safety of offshore wind turbines (OWTs), while the rapid development of offshore wind power in cold regions is enabling the deployment of OWTs in deeper waters. To accurately simulate the dynamic response of an [...] Read more.

Ice loads continue to pose challenges to the structural safety of offshore wind turbines (OWTs), while the rapid development of offshore wind power in cold regions is enabling the deployment of OWTs in deeper waters. To accurately simulate the dynamic response of an OWT under combined ice–wind loading, this paper proposes a Discrete Element Method–Wind Turbine Integrated Analysis (DEM-WTIA) framework. The framework can synchronously simulate discontinuous ice-crushing processes and aeroelastic–structural dynamic responses through a holistic turbine model that incorporates rotor dynamics and control systems. To address the issue of insufficient prediction accuracy for dynamic responses, we introduced a multivariate time series forecasting method that integrates a secondary decomposition strategy with a hybrid prediction model. First, we developed a parallel signal processing mechanism, termed Adaptive Complete Ensemble Empirical Mode Decomposition with Improved Singular Spectrum Analysis (CEEMDAN-ISSA), which achieves adaptive denoising via permutation entropy-driven dynamic window optimization and multi-feature fusion-based anomaly detection, yielding a noise suppression rate of 76.4%. Furthermore, we propose the F-Transformer prediction model, which incorporates a Frequency-Enhanced Channel Attention Mechanism (FECAM). By integrating the Discrete Cosine Transform (DCT) into the Transformer architecture, the F-Transformer mines hidden features in the frequency domain, capturing potential periodicities in discontinuous data. Experimental results demonstrate that signals processed by ISSA exhibit increased signal-to-noise ratios and enhanced fidelity. The F-Transformer achieves a maximum reduction of 31.86% in mean squared error compared to the standard Transformer and maintains a coefficient of determination (R²) above 0.91 under multi-condition coupled testing. By combining adaptive decomposition and frequency-domain enhancement techniques, this framework provides a precise and highly adaptable ultra-short-term response forecasting tool for the safe operation and maintenance of offshore wind power in cold regions. Full article

(This article belongs to the Section Coastal Engineering)

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1 pages, 178 KB

Open AccessCorrection

Correction: Diego-Tortosa et al. Effective Strategies for Automatic Analysis of Acoustic Signals in Long-Term Monitoring. J. Mar. Sci. Eng. 2025, 13, 454

by Dídac Diego-Tortosa, Danilo Bonanno, Manuel Bou-Cabo, Letizia S. Di Mauro, Abdelghani Idrissi, Guillermo Lara, Giorgio Riccobene, Simone Sanfilippo and Salvatore Viola

J. Mar. Sci. Eng. 2025, 13(9), 1759; https://doi.org/10.3390/jmse13091759 - 11 Sep 2025

Abstract

There was an error in the original publication [...] Full article

26 pages, 24511 KB

Open AccessArticle

VTLLM: A Vessel Trajectory Prediction Approach Based on Large Language Models

by Ye Liu, Wei Xiong, Nanyu Chen and Fei Yang

J. Mar. Sci. Eng. 2025, 13(9), 1758; https://doi.org/10.3390/jmse13091758 - 11 Sep 2025

Abstract

In light of the rapid expansion of maritime trade, the maritime transportation industry has experienced burgeoning growth and complexity. The deployment of trajectory prediction technology is paramount in safeguarding navigational safety. Due to limitations in design complexity and the high costs of data [...] Read more.

In light of the rapid expansion of maritime trade, the maritime transportation industry has experienced burgeoning growth and complexity. The deployment of trajectory prediction technology is paramount in safeguarding navigational safety. Due to limitations in design complexity and the high costs of data fusion, current deep learning methods struggle to effectively integrate high-level semantic cues, such as vessel type, geographical identifiers, and navigational states, within predictive frameworks. Yet, these data contain abundant information regarding vessel categories or operational scenarios. Inspired by the robust semantic comprehension exhibited by large language models (LLMs) in natural language processing, this study introduces a trajectory prediction method leveraging LLMs. Initially, Automatic Identification System (AIS) data undergoes processing to eliminate incomplete entries, thereby selecting trajectories of high quality. Distinct from prior research that concentrated solely on vessel position and velocity, this study integrates ship identity, spatiotemporal trajectory, and navigational information through prompt engineering, empowering the LLM to extract multidimensional semantic features of trajectories from comprehensive natural language narratives. Thus, the LLM can amalgamate multi-source semantics with zero marginal cost, significantly enhancing its understanding of complex maritime environments. Subsequently, a supervised fine-tuning approach rooted in Low-Rank Adaptation (LoRA) is applied to train the chosen LLMs. This enables rapid adaptation of the LLM to specific maritime areas or vessel classifications by modifying only a limited subset of parameters, thereby appreciably diminishing both data requirements and computational costs. Finally, representative metrics are utilized to evaluate the efficacy of the model training and to benchmark its performance against prevailing advanced models for ship trajectory prediction. The results indicate that the model demonstrates notable performance in short-term predictions fFor instance, with a prediction step of 1 h, the average distance errors for VTLLM and TrAISformer are 5.26 nmi and 6.12 nmi, respectively, resulting in a performance improvement of approximately 14.05%), having identified certain patterns and features, such as linear movements and turns, from the training data. Full article

(This article belongs to the Section Ocean Engineering)

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34 pages, 4638 KB

Open AccessArticle

Environmental and Economic Assessment of Alternative Marine Fuels for Bulk Carriers: A Comparative Analysis of Handymax, Panamax and Supramax Vessels

by Georgios Charvalos, Athanasios Tzakis, Angelos Arvanitis, Sofia Peppa and Christos Papadopoulos

J. Mar. Sci. Eng. 2025, 13(9), 1757; https://doi.org/10.3390/jmse13091757 - 11 Sep 2025

Abstract

In the present paper, a quantitative assessment of the effect of alternative fuel (LNG, LPG-B, LPG-P and MeOH) implementation in internal combustion engines in bulk carrier vessels on environmental compliance is presented. A fleet comprising 40 vessels across the Handymax, Panamax and Supramax [...] Read more.

In the present paper, a quantitative assessment of the effect of alternative fuel (LNG, LPG-B, LPG-P and MeOH) implementation in internal combustion engines in bulk carrier vessels on environmental compliance is presented. A fleet comprising 40 vessels across the Handymax, Panamax and Supramax classes is examined. By using LNG, the total fleet achieves environmental compliance up to 2030, with 52.5% of the fleet potentially achieving a minor superior energy ranking, while the EU ETS costs can be reduced by up to 24% compared to the case of burning conventional fuels. LPG-B and LPG-P demonstrated moderate improvements in the compliance period, with 50% to 87.5% and 52.5% to 97.5% surviving up to 2030, respectively. Reductions in the EU ETS costs were similar for these two fuels, with the reductions ranging from 3.3% to 12.1% for LPG-B and from 4.1% to 15.2% for LPG-P. Among all fuels, methanol showed the least improvement in extending the compliance period, with 52.5% to 67.5% of the fleet reaching 2030 with inferior to moderate CII ranks. The EU ETS cost reductions were low, ranging from 2.7% to 10%, with substantial fuel cost increases from 29.9% to 107%. The present study aims to assist ship owners/operators by providing a decision-support tool for bulk carrier alternative fuel pathways. Finally, it provides insights into the marine industry and shipping market regarding the future of the bulk carrier fleet in the context of decarbonization. Full article

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

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28 pages, 3589 KB

Open AccessArticle

Broadband Low-Frequency Sound Absorption Enabled by a Rubber-Based Ni₅₀Ti₅₀ Alloy Multilayer Acoustic Coating

by Yizhe Huang, Ziyi Liu, Qiyuan Fan, Huizhen Zhang, Bin Huang, Qibai Huang and Zhifu Zhang

J. Mar. Sci. Eng. 2025, 13(9), 1756; https://doi.org/10.3390/jmse13091756 - 11 Sep 2025

Viewed by 29

Abstract

Acoustic coatings play a vital role in enhancing the acoustic stealth of underwater structures across the full depth range and, especially, in the low-frequency band. However, existing small-scale acoustic coatings struggle to achieve low-frequency broadband sound absorption, which limits further performance improvements. Ni [...] Read more.

Acoustic coatings play a vital role in enhancing the acoustic stealth of underwater structures across the full depth range and, especially, in the low-frequency band. However, existing small-scale acoustic coatings struggle to achieve low-frequency broadband sound absorption, which limits further performance improvements. Ni₅₀Ti₅₀ alloy, with their shape memory effect, hyper elasticity, and high damping properties, offer promising applications in vibration and noise control. In this study, a rubber-based Ni₅₀Ti₅₀ alloy multilayer acoustic coating is proposed, based on the sound absorption mechanism of rubber and the vibration and noise reduction mechanism of Ni₅₀Ti₅₀ alloy. The sound absorption characteristics of the proposed composite coating were obtained through analytical derivations, numerical simulations, and experimental investigations. The objective was to combine the high-frequency absorption capability of rubber and the low-frequency absorption characteristics of Ni₅₀Ti₅₀ alloy without increasing material dimensions, thereby introducing a novel approach for the design of the next generation of underwater acoustic coatings. Full article

(This article belongs to the Section Ocean Engineering)

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17 pages, 22479 KB

Open AccessArticle

Estuary-Tidal Residual Water Level Forecasting Method Based on Variational Mode Decomposition and Back Propagation Neural Network

by Min Zhai, Qihang Cao, Pengfei Huo, Xintong Du and Mingzhen Xin

J. Mar. Sci. Eng. 2025, 13(9), 1755; https://doi.org/10.3390/jmse13091755 - 11 Sep 2025

Viewed by 36

Abstract

The water level changes in the estuarine area are influenced by various factors with different mechanisms and periodicities, including runoff, astronomical tides and storm surges, resulting in relatively low forecasting accuracy of the residual water level. To improve the forecast accuracy of residual [...] Read more.

The water level changes in the estuarine area are influenced by various factors with different mechanisms and periodicities, including runoff, astronomical tides and storm surges, resulting in relatively low forecasting accuracy of the residual water level. To improve the forecast accuracy of residual water levels, an estuary-tidal residual water level forecasting method based on VMD-BPNN (variational mode decomposition and back propagation neural network) is proposed. By conducting tidal harmonic analysis on the long-term water level data of estuarine areas, astronomic water levels and residual water levels can be obtained. The residual water level is subjected to VMD, obtaining multiple intrinsic mode functions of the residual water level in the time series. Then, the BPNN is used to train each intrinsic mode function, and an accurate forecast of residual water levels in the estuary area is achieved through the forecast and superposition of each intrinsic mode function. Water level data from four typical tidal stations in estuarine areas of the United States and France were used for experimental analysis. The method was verified by using Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Nash-Sutcliffe Efficiency (NSE) as evaluation indicators, and the results showed that it had a good comprehensive performance, and high stability and accuracy in the forecasting of the residual water level. This study thereby provides a valuable foundation and insightful reference for future research into the complex mechanisms driving water level changes and the development of high-precision tidal forecasting systems in estuarine environments. Full article

(This article belongs to the Section Coastal Engineering)

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21 pages, 4473 KB

Open AccessArticle

AISStream-MCP: A Real-Time Memory-Augmented Question-Answering System for Maritime Operations

by Sien Chen, Ruoxian Zhao, Jian-Bo Yang and Yinghua Huang

J. Mar. Sci. Eng. 2025, 13(9), 1754; https://doi.org/10.3390/jmse13091754 - 11 Sep 2025

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Abstract

Ports and maritime operations generate massive real-time data streams, particularly from Automatic Identification System (AIS) signals, which are challenging to query effectively using natural language. This study proposes a prototype AISStream-MCP, a memory-augmented real-time maritime question-answering (QA) system that integrates live AIS data [...] Read more.

Ports and maritime operations generate massive real-time data streams, particularly from Automatic Identification System (AIS) signals, which are challenging to query effectively using natural language. This study proposes a prototype AISStream-MCP, a memory-augmented real-time maritime question-answering (QA) system that integrates live AIS data streaming with a Model Context Protocol (MCP) toolchain to support port operations decision-making. The system combines a large language model (LLM) with four MCP-enabled modules: persistent dialogue memory, live AIS data query, knowledge graph lookup, and result evaluation. We hypothesize that augmenting an LLM with domain-specific tools significantly improves QA performance compared to systems without memory or live data access. To test this hypothesis, we developed two prototype systems (with and without MCP framework) and evaluated them on 30 queries across three task categories: ETA prediction, anomaly detection, and multi-turn route queries. Experimental results demonstrate that AISStream-MCP achieves 88% answer accuracy (vs. 75% baseline), 85% multi-turn coherence (vs. 60%), and 38.7% faster response times (4.6 s vs. 7.5 s), with user satisfaction scores of 4.6/5 (vs. 3.5/5). The improvements are statistically significant (p < 0.01), confirming that memory augmentation and real-time tool integration effectively enhance maritime QA capabilities. Specifically, AISStream-MCP improved ETA prediction accuracy from 80% to 90%, anomaly detection from 70% to 85%, and multi-turn query accuracy from 65% to 88%. This approach shows significant potential for improving maritime situational awareness and operational efficiency. Full article

(This article belongs to the Section Ocean Engineering)

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22 pages, 8607 KB

Open AccessArticle

Time Series Changes of Surficial Sediments on Eastern Ship Shoal, Louisiana Shelf

by Adam Gartelman, Kehui Xu, Brian J. Roberts, David Samuel Johnson and Madison Liotta

J. Mar. Sci. Eng. 2025, 13(9), 1753; https://doi.org/10.3390/jmse13091753 - 11 Sep 2025

Viewed by 38

Abstract

Ship Shoal, a large transgressive sand body on the Louisiana continental shelf, is a critical sediment source for coastal restoration. This study evaluates spatial and temporal variability in sediment grain size, percents organic matter (%OM), and carbonate (%CO₃) across the shoal [...] Read more.

Ship Shoal, a large transgressive sand body on the Louisiana continental shelf, is a critical sediment source for coastal restoration. This study evaluates spatial and temporal variability in sediment grain size, percents organic matter (%OM), and carbonate (%CO₃) across the shoal crest (REF), Caminada Dredge Pit (CAM), and Terrebonne Dredge Pit (TER). Sediment samples were collected between 2020 and 2022 using box cores and analyzed for grain size, %OM, and %CO₃, with temporal and spatial patterns assessed through statistical comparisons, correlation analyses, and random forest regression models. Results show that dredged areas act as sinks for fine-grained, organic-rich sediments, with CAM consistently exhibiting the smallest median grain sizes and highest %OM, while REF maintained coarse, well-sorted sands. Carbonate enrichment reflected long-term depositional regimes, with REF exhibiting the highest %CO₃ due to the absence of dredging disturbance. Grain size and %CO₃ were identified as the strongest predictors of %OM, while %CO₃ was only weakly correlated with other sedimentary variables. Collectively, these findings demonstrate that dredge pits function as persistent repositories, with implications for benthic habitat resilience, sediment management, and coastal restoration planning. Future integration of hydrodynamic modeling with sediment transport and biogeochemical processes is needed to enhance predictive capability for managing dredged environments. Full article

(This article belongs to the Special Issue Advances in Coastal Hydrodynamic and Morphodynamic Processes under a Changing Climate)

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16 pages, 2886 KB

Open AccessArticle

Stability Analysis of Coastal Sheet Pile Wall Considering Soil Weakening Induced by Earthquake Loading

by Shuai Ning, Qiang Ma and Yuan Cao

J. Mar. Sci. Eng. 2025, 13(9), 1752; https://doi.org/10.3390/jmse13091752 - 11 Sep 2025

Viewed by 30

Abstract

A sheet pile wall is a widely used retaining structure in coastal and riverbank areas. In liquefiable soils, seismic activity can generate excess pore pressure, which not only increases the load on the sheet pile wall but also reduces the soil strength. Here, [...] Read more.

A sheet pile wall is a widely used retaining structure in coastal and riverbank areas. In liquefiable soils, seismic activity can generate excess pore pressure, which not only increases the load on the sheet pile wall but also reduces the soil strength. Here, a modified stability analysis method is proposed to consider the effect of excess pore pressure on the stability of sheet pile walls. The excess pore pressure ratio was estimated through a pore pressure generation model and an equivalent number of loading cycles. In addition, two sets of dynamic centrifuge model tests were conducted on a liquefiable layer retained by a cantilevered sheet pile wall. The retained backfill experienced significant excess pore pressure, leading to the rotation failure of the sheet pile wall. The bending moments of the sheet pile wall were obtained using strain gauges, validating the effectiveness of the newly proposed stability analysis method. The dynamic water pressure in front of the wall can reduce the wall’s bending moment. When considering dynamic water pressure, the bending moment decreased by approximately 7.7%. For the same earthquake loading, varying the equivalent number of cycles did not affect the wall’s force response or the determination of instability. During the transition of the wall from static to unstable, the passive earth pressure in front of the wall extended deeper, causing a downward shift in the location of the maximum bending moment of the wall. Above all, this study provides a theoretical foundation for the design and construction of sheet pile walls in liquefiable regions. Full article

(This article belongs to the Section Coastal Engineering)

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17 pages, 7213 KB

Open AccessArticle

Deep Learning-Based Wind Speed Retrieval from Sentinel-1 SAR Wave Mode Data

by Ruixuan Sun, Chen Wang, Zhuhui Jiang and Xiaojuan Kong

J. Mar. Sci. Eng. 2025, 13(9), 1751; https://doi.org/10.3390/jmse13091751 - 11 Sep 2025

Viewed by 69

Abstract

Sea surface wind has been listed as an essential climate variable, playing crucial roles in regulating the global and regional weather and climate. Spaceborne synthetic aperture radar (SAR) has demonstrated the advantages in observing the wind field given its all-weather measurement capability. In [...] Read more.

Sea surface wind has been listed as an essential climate variable, playing crucial roles in regulating the global and regional weather and climate. Spaceborne synthetic aperture radar (SAR) has demonstrated the advantages in observing the wind field given its all-weather measurement capability. In this study, we present a convolutional neural network (CNN)-based framework for retrieving 10 m wind speed (

U_{10}

) from Sentinel-1 SAR wave mode (WV) imagery. The model is trained on SAR data acquired in 2017 using collocated ERA5 reanalysis wind vectors as the reference, with final performance evaluated against a temporally independent dataset from 2016 and in situ wind measurements. The CNN approach demonstrates improved retrieval accuracy compared to the conventional CMOD5.N-based result, achieving lower root mean square error (RMSE) and bias across both WV1 and WV2 incidence angle modes. Residual diagnostics show a systematic overestimation at low wind speeds and a slight underestimation at higher wind speeds. Spatial analyses of retrieval bias reveal regional variations, particularly in areas characterized by ocean swell or convective atmospheric activity, highlighting the importance of geophysical features in retrieval accuracy. These results support the viability of deep learning approaches for SAR-based ocean surface wind estimation and suggest a path forward for the development of more accurate, data-driven wind products suitable for both scientific research and operational marine forecasting. Full article

(This article belongs to the Special Issue Application of Remote Sensing Technology in Marine and Water Resources Observation)

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22 pages, 6551 KB

Open AccessArticle

A Coupled SVM-NODE Model for Efficient Prediction of Ship Roll Motion

by Yaxiong Zheng, Fei Peng, Zhanzhi Wang and Siwen Tian

J. Mar. Sci. Eng. 2025, 13(9), 1750; https://doi.org/10.3390/jmse13091750 - 10 Sep 2025

Viewed by 65

Abstract

Traditional analyses of ship roll damping and added moment of inertia rely on free roll decay and forced roll tests, but acquiring linear (small angles) and nonlinear (large angles) relationships demands extensive computational cases and parameter fitting, limiting efficiency. To address this, this [...] Read more.

Traditional analyses of ship roll damping and added moment of inertia rely on free roll decay and forced roll tests, but acquiring linear (small angles) and nonlinear (large angles) relationships demands extensive computational cases and parameter fitting, limiting efficiency. To address this, this study couples Support Vector Machine (SVM) and Neural Ordinary Differential Equation (NODE) networks: SVM solves for added moment of inertia, linear damping, and nonlinear damping, while NODE constructs a complete model for the roll motion equation. Using the DTMB5415 hull form, Computational Fluid Dynamics (CFD) simulations of forced roll build a “time-angle-moment” sample space, and the coupled model learns and predicts free roll decay under different initial angles. The results show that SVM effectively determines roll damping and added moment of inertia from constant-amplitude variable-frequency and constant-frequency variable-amplitude data, reducing required cases significantly. NODE’s simulation of free roll decay validates coefficient accuracy. Within a certain angle range, the SVM-NODE model meets rapid roll motion analysis needs, providing an innovative method for ship roll research and engineering. Full article

(This article belongs to the Section Ocean Engineering)

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26 pages, 101982 KB

Open AccessArticle

Hydrodynamic Optimization and Motion Stability Enhancement of Underwater Glider Combining CFD and MOPSO

by Tian Zhang, Jiaming Wu, Xianyuan Yang and Xiaodong Chen

J. Mar. Sci. Eng. 2025, 13(9), 1749; https://doi.org/10.3390/jmse13091749 - 10 Sep 2025

Viewed by 92

Abstract

This study investigated the motion stability of underwater gliders and optimized their shape to enhance hydrodynamic performance. Given the critical role of stability in underwater operations, a multi-objective optimization framework was developed, focusing on the geometric configuration of hydrofoils. Computational fluid dynamics (CFD) [...] Read more.

This study investigated the motion stability of underwater gliders and optimized their shape to enhance hydrodynamic performance. Given the critical role of stability in underwater operations, a multi-objective optimization framework was developed, focusing on the geometric configuration of hydrofoils. Computational fluid dynamics (CFD) simulations were employed, with stability assessed based on hydrodynamic moments in roll and pitch motions. A surrogate model was constructed using Kriging interpolation, leveraging Latin hypercube sampling (LHS) to generate 60 design points. Sensitivity analysis identified key shape parameters influencing stability, guiding a multi-objective particle swarm optimization (MOPSO) algorithm to explore optimal design configurations. Improvements of up to 68.91% in roll stability and 51.63% in pitch stability are achieved compared to the original model, which demonstrates the effectiveness of the proposed optimization approach. The findings provide valuable insights into the hydrodynamic design of underwater gliders, facilitating enhanced maneuverability and stability in complex marine environments. Full article

(This article belongs to the Special Issue Design and Application of Underwater Vehicles)

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22 pages, 1722 KB

Open AccessReview

From LNG to LH₂ in Maritime Transport: A Review of Technology, Materials, and Safety Challenges

by Matteo Passalacqua and Alberto Traverso

J. Mar. Sci. Eng. 2025, 13(9), 1748; https://doi.org/10.3390/jmse13091748 - 10 Sep 2025

Viewed by 154

Abstract

The adoption of low-carbon fuels in maritime propulsion requires operational autonomy, material suitability, and compliance with safety standards, making liquid fuels like LNG and LH₂ the most viable options. LNG is widely used for reducing GHG, NOx, and SOx emissions, while LH [...] Read more.

The adoption of low-carbon fuels in maritime propulsion requires operational autonomy, material suitability, and compliance with safety standards, making liquid fuels like LNG and LH₂ the most viable options. LNG is widely used for reducing GHG, NOx, and SOx emissions, while LH₂, though new to the maritime sector, leverages aerospace experience. This paper explores the operational requirements and challenges of LH₂ cryogenic handling systems using LNG practices as a reference. Key comparisons are made between LNG and LH₂ supply systems, focusing on cryogenic materials, hydrogen embrittlement, and structural integrity under maritime conditions. Most maritime-approved materials are suitable for cryogenic use, and hydrogen embrittlement is less critical at cryogenic temperatures due to reduced atomic mobility. Risk assessments suggest LH₂’s safety record stems from limited operational data rather than superior inherent safety. The paper also addresses crucial safety and regulatory considerations for both fuels, underscoring the need for strict adherence to standards to ensure the safe and compliant integration of LH₂ in the maritime industry. Full article

(This article belongs to the Topic Sustainable Energy Technology, 2nd Edition)

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31 pages, 3548 KB

Open AccessArticle

Underwater Acoustic Integrated Sensing and Communication: A Spatio-Temporal Freshness for Intelligent Resource Prioritization

by Ananya Hazarika and Mehdi Rahmati

J. Mar. Sci. Eng. 2025, 13(9), 1747; https://doi.org/10.3390/jmse13091747 - 10 Sep 2025

Viewed by 73

Abstract

Underwater acoustic communication faces significant challenges including limited bandwidth, high propagation delays, severe multipath fading, and stringent energy constraints. While integrated sensing and communication (ISAC) has shown promise in radio frequency systems, its adaptation to underwater environments remains challenging due to the unique [...] Read more.

Underwater acoustic communication faces significant challenges including limited bandwidth, high propagation delays, severe multipath fading, and stringent energy constraints. While integrated sensing and communication (ISAC) has shown promise in radio frequency systems, its adaptation to underwater environments remains challenging due to the unique acoustic channel characteristics and the inadequacy of traditional delay-based performance metrics that fail to capture the spatio-temporal value of information in dynamic underwater scenarios. This paper presents a comprehensive underwater ISAC framework centered on a novel Spatio-Temporal Information-Theoretic Freshness metric that fundamentally transforms resource allocation from delay minimization to value maximization. Unlike conventional approaches that treat all data equally, our spatio-temporal framework enables intelligent prioritization by recognizing that obstacle detection data directly ahead of an autonomous underwater vehicle (AUV) require immediate processing. Our framework addresses key underwater ISAC challenges through spatio-temporal-guided power allocation, adaptive beamforming, waveform optimization, and cooperative sensing strategies. Multi-agent reinforcement learning algorithms enable coordinated resource allocation and mission-critical information prioritization across heterogeneous networks comprising surface buoys, AUVs, and static sensors. Extensive simulations in realistic Munk profile acoustic environments demonstrate significant performance improvements. The spatio-temporal framework successfully filters spatially irrelevant data, resulting in substantial energy savings for battery-constrained underwater nodes. Full article

(This article belongs to the Section Ocean Engineering)

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19 pages, 2622 KB

Open AccessArticle

Morphodynamic of Tidal Flat Profiles in an Erosion-to-Accretion Transitional Coastal Segment Under Wave–Current Interaction: A Case Study of Dafeng Port, China

by Tianjun Li, Yifei Zhao, Lizhu Wang, Hong Zhang, Min Xu and Jicheng Cao

J. Mar. Sci. Eng. 2025, 13(9), 1746; https://doi.org/10.3390/jmse13091746 - 10 Sep 2025

Viewed by 73

Abstract

Understanding the morphodynamic evolution of muddy coasts under complex wave–tidal forcing is crucial for effective coastal management, particularly under the unstable hydrodynamic conditions associated with global climate change. This study employs a one-dimensional Delft3D model to investigate a tidal flat north of Dafeng [...] Read more.

Understanding the morphodynamic evolution of muddy coasts under complex wave–tidal forcing is crucial for effective coastal management, particularly under the unstable hydrodynamic conditions associated with global climate change. This study employs a one-dimensional Delft3D model to investigate a tidal flat north of Dafeng Port, Jiangsu Province, China, validated with multi-year profile measurements. Under typical conditions, the profile consistently exhibits upper-flat accretion and lower-flat erosion, with threshold values of Hs ≈ 1.2 m and Tp ≈ 4.5 s triggering nonlinear bed-level changes. During storm tides, the profile displays a distinct upper flood-tide and lower ebb-tide response. Long-term simulations suggest that the profile will likely reach dynamic equilibrium by 2026. Overall, this study demonstrates the capability of one-dimensional modeling to capture nonlinear tidal flat evolution and provides valuable insights into the spatially variable morphodynamics of muddy coasts for adaptive management. Full article

(This article belongs to the Special Issue Advances in Coastal Hydrodynamic and Morphodynamic Processes under a Changing Climate)

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21 pages, 18869 KB

Open AccessArticle

MambaRA-GAN: Underwater Image Enhancement via Mamba and Intra-Domain Reconstruction Autoencoder

by Jiangyan Wu, Guanghui Zhang and Yugang Fan

J. Mar. Sci. Eng. 2025, 13(9), 1745; https://doi.org/10.3390/jmse13091745 - 10 Sep 2025

Viewed by 65

Abstract

Underwater images frequently suffer from severe quality degradation due to light attenuation and scattering effects, manifesting as color distortion, low contrast, and detail blurring. These issues significantly impair the performance of downstream tasks. Therefore, underwater image enhancement (UIE) becomes a key technology to [...] Read more.

Underwater images frequently suffer from severe quality degradation due to light attenuation and scattering effects, manifesting as color distortion, low contrast, and detail blurring. These issues significantly impair the performance of downstream tasks. Therefore, underwater image enhancement (UIE) becomes a key technology to solve underwater image degradation. However, existing data-driven UIE methods typically rely on difficult-to-acquire paired data for training, severely limiting their practical applicability. To overcome this limitation, this study proposes MambaRA-GAN, a novel unpaired UIE framework built upon a CycleGAN architecture, which introduces a novel integration of Mamba and intra-domain reconstruction autoencoders. The key innovations of our work are twofold: (1) We design a generator architecture based on a Triple-Gated Mamba (TG-Mamba) block. This design dynamically allocates feature channels to three parallel branches via learnable weights, achieving optimal fusion of CNN’s local feature extraction capabilities and Mamba’s global modeling capabilities. (2) We construct an intra-domain reconstruction autoencoder, isomorphic to the generator, to quantitatively assess the quality of reconstructed images within the cycle consistency loss. This introduces more effective structural information constraints during training. The experimental results demonstrate that the proposed method achieves significant improvements across five objective performance metrics. Visually, it effectively restores natural colors, enhances contrast, and preserves rich detail information, robustly validating its efficacy for the UIE task. Full article

(This article belongs to the Section Ocean Engineering)

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19 pages, 4516 KB

Open AccessArticle

Accurate Extraction Method for Continental Margin FOS Line Considering Terrain Continuity

by Dong Wang, Jian Dong, Zhiqiang Zhang, Tian Xie, Xiaodong Ma and Tianyue Wang

J. Mar. Sci. Eng. 2025, 13(9), 1744; https://doi.org/10.3390/jmse13091744 - 10 Sep 2025

Viewed by 97

Abstract

This paper addresses the limitations and low efficiency of current methods for precise identification of continental margin break points in the delimitation of the outer continental shelf. From a three-dimensional perspective, it proposes a novel method for extracting the foot-of-slope (FOS) line of [...] Read more.

This paper addresses the limitations and low efficiency of current methods for precise identification of continental margin break points in the delimitation of the outer continental shelf. From a three-dimensional perspective, it proposes a novel method for extracting the foot-of-slope (FOS) line of the continental margin that considers terrain continuity. First, the algorithm uses the rolling ball transform to classify the strength of the attributes of negative topographic feature lines of the seafloor. Then, it conducts experiments on two sets of negative topographic feature lines with strong and weak attributes. By calculating the proportion of the intersection of weak attribute lines with strong ones, it establishes a hierarchical pattern of importance for these lines. Subsequently, the algorithm integrates a multi-factor screening process for the continental margin FOS line. Finally, it achieves accurate and efficient extraction of the FOS line while preserving terrain continuity. The method’s effectiveness is verified through visual interpretation, comparison, and efficiency experiments in a real digital depth model. The results indicate that the algorithm can accurately extract the FOS line, effectively distinguish the continental margin, and maintain high efficiency. Full article

(This article belongs to the Special Issue Data-Driven Methods for Marine Structures, Second Edition)

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31 pages, 5077 KB

Open AccessArticle

The Optimization of Container Relocation in Terminal Yards: A Computational Study Using Strategy-Iterative Deepening Branch-and-Bound Algorithm

by Jiangbei Zhang and Jin Zhu

J. Mar. Sci. Eng. 2025, 13(9), 1743; https://doi.org/10.3390/jmse13091743 - 10 Sep 2025

Viewed by 125

Abstract

Container relocation operations at terminal yards represent a fundamental pillar in the optimization of stowage scheduling during vessel loading, serving as a critical component of port operational efficiency. This paper focuses on the restricted container relocation problem (RCRP), in which the objective is [...] Read more.

Container relocation operations at terminal yards represent a fundamental pillar in the optimization of stowage scheduling during vessel loading, serving as a critical component of port operational efficiency. This paper focuses on the restricted container relocation problem (RCRP), in which the objective is to minimize the number of relocations for retrieving all containers from a bay under a predetermined retrieval sequence. A strategy-oriented algorithm (SOA) was proposed to address this issue, and a strategy-iterative deepening branch-and-bound algorithm (S-IDB&B) was constructed based on this algorithm. Among them, the SOA can quickly find feasible solutions to the problem, while the S-IDB&B algorithm can find the optimal solution to the problem and can also set an early stopping mechanism to obtain high-quality solutions in a shorter period of time. Comparative computational experiments demonstrate that the strategy-iterative deepening branch-and-bound algorithm finds optimal solutions for all small-scale instances within 0.01 s and achieves optimal solutions for over 80% of medium-to-large-scale instances, and it outperforms existing exact algorithms (solve larger scale instances with shorter computation time); moreover, when equipped with the early stopping mechanism, it yields higher solution quality than existing heuristic algorithms (the maximum accuracy deviation is around 20%) while maintaining comparable computation times. Full article

(This article belongs to the Section Ocean Engineering)

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J. Mar. Sci. Eng., Volume 13, Issue 9 (September 2025) – 173 articles

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