Journal of Marine Science and Engineering

36 pages, 3195 KB

Open AccessArticle

Contraction-Based Trajectory Tracking Control for AUVs on SE(3) with Hierarchical Gain Certification

by Jinjun Jia, Kang An, Yuchen Liao, Xun Yan, Tiedong Zhang and Dapeng Jiang

J. Mar. Sci. Eng. 2026, 14(16), 1465; https://doi.org/10.3390/jmse14161465 (registering DOI) - 9 Aug 2026

This paper develops a contraction-certified trajectory-tracking and gain-selection framework for fully actuated autonomous underwater vehicles on

S E (3)

. The vehicle dynamics are represented in port-Hamiltonian form with a Rayleigh-type dissipation potential, and a dual potential shaping controller provides an [...] Read more.

This paper develops a contraction-certified trajectory-tracking and gain-selection framework for fully actuated autonomous underwater vehicles on

S E (3)

. The vehicle dynamics are represented in port-Hamiltonian form with a Rayleigh-type dissipation potential, and a dual potential shaping controller provides an energy-structured rotational–translational cascade. Regional contraction certificates are derived separately for the rotational and translational subsystems. The rotational analysis uses fixed left-trivialised momentum coordinates and retains anisotropic-inertia effects and the complete off-diagonal differential coupling. The translational analysis applies to a general known symmetric positive-definite inertia matrix through an attitude-cover semidefinite programme, with an exact endpoint reduction for isotropic inertia. A scaled composite metric combines the subsystem certificates and guarantees every strict complete-cascade rate below the slower subsystem rate. Large initial attitude errors are handled by an energy-entry phase followed by contraction within a prescribed tube, without controller switching. The four-dimensional gain-selection problem is decomposed into two independent two-dimensional offline searches using bisection and SDP/LMI feasibility tests. Numerical studies on the ODIN AUV quantify the region–gain–rate trade-off and examine small-angle, large-angle, and near-antipodal manoeuvres. The framework certifies complete-cascade rates of

0.042096 s^{- 1}

and

0.008524 s^{- 1}

for the

60^{\circ} / 60^{\circ}

and

150^{\circ} / 80^{\circ}

regions, respectively. Full article

(This article belongs to the Section Ocean Engineering)

21 pages, 14634 KB

Open AccessArticle

Leakage-Free, Cross-Speed, and Cross-Session Evaluation of Vibration-Based Propulsion-Shaft Misalignment Diagnosis in Electric Ships: A Real-Time Detect-Then-Grade Cascade

by Jin-Man Kim, Heon-Hui Kim and Taek-Kun Nam

J. Mar. Sci. Eng. 2026, 14(16), 1464; https://doi.org/10.3390/jmse14161464 (registering DOI) - 8 Aug 2026

Abstract

Vibration-based diagnosis of propulsion-shaft misalignment supports condition monitoring in electric ships, but reported accuracies are often inflated by overlapping-window leakage and by untested cross-recording, cross-session, and cross-speed generalization. We evaluated leakage-free, deployment-oriented protocols (chronological, leave-one-recording-out (LORO), leave-one-session-out, and leave-one-speed-out) on a 50 kW [...] Read more.

Vibration-based diagnosis of propulsion-shaft misalignment supports condition monitoring in electric ships, but reported accuracies are often inflated by overlapping-window leakage and by untested cross-recording, cross-session, and cross-speed generalization. We evaluated leakage-free, deployment-oriented protocols (chronological, leave-one-recording-out (LORO), leave-one-session-out, and leave-one-speed-out) on a 50 kW electric-propulsion land-based test system with six accelerometer channels under 0, 2, and 4 mm offset misalignments, comparing feature-engineered gradient-boosting models, raw-signal deep models, and nominal-speed order-normalized features. Under LORO evaluation, a raw-signal one-dimensional convolutional neural network (1D-CNN) achieved 0.93–1.00 accuracy and outperformed LightGBM in all 15 folds. Under leave-one-speed-out testing, MiniRocket retained 0.775 accuracy, whereas InceptionTime dropped to 0.468. Fixed-Hz features outperformed nominal-speed order-normalized features by 0.079 (p = 0.022), and single-window 1D-CNN inference required 0.50 ms on a CPU. Recording-level and cross-session evaluation are therefore essential for reliable misalignment diagnosis, and raw-signal models provide the strongest deployable performance on the present test rig. Full article

(This article belongs to the Special Issue AI-Driven Optimization of Ship Performance and Navigation Safety)

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30 pages, 5641 KB

Open AccessArticle

A Method for Portal Crane Wire Rope Recognition Based on Improved PointNet⁺⁺

by Xinyuan Li, Yujie Zhang and Yang Shen

J. Mar. Sci. Eng. 2026, 14(16), 1463; https://doi.org/10.3390/jmse14161463 (registering DOI) - 8 Aug 2026

Abstract

In automated dry bulk terminal operations, accurate perception of the spatial pose of portal crane wire ropes is important for grab positioning and can provide geometric information for subsequent anti-sway control research. Vision-based measurements may be affected by metallic reflections, illumination variation, and [...] Read more.

In automated dry bulk terminal operations, accurate perception of the spatial pose of portal crane wire ropes is important for grab positioning and can provide geometric information for subsequent anti-sway control research. Vision-based measurements may be affected by metallic reflections, illumination variation, and dust occlusion, whereas inertial or mechanically coupled measurements may be affected by vibration and dynamic coupling. This study proposes a LiDAR-based method for wire rope point cloud segmentation and pose estimation using an improved PointNet++. Dual-LiDAR point clouds are aligned and filtered using a kinematic constraint-based Region of Interest (ROI) to reduce background redundancy. A Spatial Self-Attention (SSA) module is introduced to combine long-range semantic dependencies with local spatial weighting, improving the representation of sparse and fragmented wire rope points. The segmented wire rope points are separated by t–k-means clustering and fitted with spatial lines for pose estimation. The complete acquisition comprises 11,348 annotated frames: a 9458-frame model development dataset from 1000 complete operating cycles, and a separately retained 1890-frame independent engineering test set from 200 condition-specific operating sequences. The development dataset was divided into mutually exclusive training and validation partitions at the level of complete operating cycles, and checkpoint selection was performed only on the validation set. Three independent training runs with fixed random seeds were conducted. On the independent test set, PointNet++ achieved an F1-score of 87.5 ± 0.2% and an mIoU of 79.0 ± 0.2%, whereas the complete proposed method achieved an F1-score of 92.8 ± 0.2% and an mIoU of 86.6 ± 0.2%. These results characterize performance on independent operating sequences collected from the crane and sensor configurations represented in the dataset. The standalone segmentation stage achieved 111.9 FPS, whereas the complete processing pipeline required slightly more than 2 s per frame because of frame-by-frame KD-ICP fine registration. Full article

(This article belongs to the Special Issue Sustainable Maritime Transport, Ports, Supply Chain Intelligence, and Marine Environmental Engineering)

26 pages, 3120 KB

Open AccessArticle

Separating Sensor-like Anomalies from Regional Oceanographic Events: A Machine-Learning-Assisted, Physics-Guided, Event-Preserving Quality-Control Framework for Coastal Buoy Temperature Records

by Huitae Joo, Byoung-Jun Lim and Hae Kun Jung

J. Mar. Sci. Eng. 2026, 14(16), 1462; https://doi.org/10.3390/jmse14161462 (registering DOI) - 8 Aug 2026

Abstract

Coastal upwelling and typhoon-driven mixing can cool a buoy record by several degrees within hours. Sensor faults do the same. Quality-control schemes that flag anomalies by residual magnitude alone therefore risk discarding real events. We analysed 30 min temperature records from six buoys [...] Read more.

Coastal upwelling and typhoon-driven mixing can cool a buoy record by several degrees within hours. Sensor faults do the same. Quality-control schemes that flag anomalies by residual magnitude alone therefore risk discarding real events. We analysed 30 min temperature records from six buoys and three depths off the east coast of Korea, spanning 2008–2024, and built a machine-learning-assisted, physics-guided, event-preserving quality-control framework that adds new labels without altering any observation or existing flag. Cooling events were catalogued from changes in the observed surface temperature and in the surface-to-bottom temperature difference and classified using three physically interpretable axes: spatial coherence with neighbouring buoys, vertical consistency between layers, and atmospheric forcing from ERA5 and typhoon best-track data. A station-wise ridge prediction model, fitted to the surface layer at five of the six stations, supplied prediction residuals that served only to flag candidates. Residual magnitude separated sensor-like anomalies from regional-event candidates poorly (direction-free AUC 0.52–0.56); upwelling-candidate and typhoon-related events produced residuals as large as those of the sensor-like reference group, or larger. The physical axes showed much stronger internal operational separability, reaching pairwise AUC values up to 1.000 and a multivariate cross-validated mean AUC of 0.987. These values do not represent external validation because the groups were partly defined using the same axes. The framework preserved regional-event candidates while affecting derived monthly means by at most about 0.0005 °C, yet retained event-scale cooling of up to about 8 °C. Prediction residuals are therefore useful for broad anomaly-candidate detection but insufficient for final event classification, which should rely on physically interpretable, multi-station criteria. Full article

(This article belongs to the Special Issue Ocean Observations, Second Edition)

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23 pages, 4109 KB

Open AccessArticle

Arctic Snow Density Retrieval from AMSR-2 Passive Microwave Brightness Temperatures: A Comparative Evaluation of Machine-Learning and Deep-Learning Models

by Jianjun Zhang, Wentao Zhou, Shuhu Yang and Yun Zhang

J. Mar. Sci. Eng. 2026, 14(16), 1461; https://doi.org/10.3390/jmse14161461 - 7 Aug 2026

Abstract

Snow density influences Arctic climate, ecosystems, and surface energy exchange, yet spatially continuous observations remain limited. This study constructed an ERA5-supervised snow-density dataset for 60–90° N by collocating Advanced Microwave Scanning Radiometer 2 (AMSR-2) Level-1R brightness temperatures with ECMWF Reanalysis v5 (ERA5) snow [...] Read more.

Snow density influences Arctic climate, ecosystems, and surface energy exchange, yet spatially continuous observations remain limited. This study constructed an ERA5-supervised snow-density dataset for 60–90° N by collocating Advanced Microwave Scanning Radiometer 2 (AMSR-2) Level-1R brightness temperatures with ECMWF Reanalysis v5 (ERA5) snow density, Soil Moisture Active Passive (SMAP) surface roughness, and auxiliary variables. Ten models were evaluated using 29 observation days spanning September 2022–February 2023 under a chronological training–validation–test split. Extra Trees achieved the best overall performance, with a root mean square error of 18.54 kg m^-3 and an R² of 0.87, while the bidirectional gated recurrent unit (BiGRU) was the strongest deep-learning model. Feature-attribution and ablation analyses showed that microwave brightness temperatures contained predictive information, although geographic and auxiliary variables also contributed substantially. The evaluated models could reproduce ERA5-referenced Arctic snow-density patterns, but their performance partly reflected regional information. Moreover, ERA5 showed limited consistency with station-based Northern Hemisphere Snow Water Equivalent estimates. Consequently, the reported metrics quantify agreement with ERA5 rather than accuracy against independently observed snow density. Temporally coincident and spatially independent field validation remains necessary in the future. Full article

(This article belongs to the Section Ocean and Global Climate)

24 pages, 7746 KB

Open AccessReview

Ferdinandea Island and Graham Bank, Sicily Channel: An Integrated Historical, Geological and Geomorphological Synthesis of a Shallow Submarine Monogenetic Volcanic Field

by Daniele Spatola, Luca Basilone, Fabiano Gamberi, Francesco Latino Chiocci, Gualtiero Basilone and Attilio Sulli

J. Mar. Sci. Eng. 2026, 14(16), 1460; https://doi.org/10.3390/jmse14161460 - 7 Aug 2026

Abstract

Ferdinandea Island, part of a shallow-water submarine volcanic field, emerged in the Sicily Channel between Italy and Tunisia in July 1831 and was eroded below sea level within months; its submerged remnant forms the shallowest water depth region of Graham Bank. Here, we [...] Read more.

Ferdinandea Island, part of a shallow-water submarine volcanic field, emerged in the Sicily Channel between Italy and Tunisia in July 1831 and was eroded below sea level within months; its submerged remnant forms the shallowest water depth region of Graham Bank. Here, we review nearly two centuries of historical accounts, geological interpretations and geomorphological data analysis and reassess them against high-resolution multibeam bathymetry, sub-bottom profiles (CHIRP) and published multichannel seismic data. The field comprises six volcanic edifices (V1–V6), 100–170 m high, located along structural trends characteristic of the Sicily Channel Rift. V3, the shallowest edifice, is the remnant of Ferdinandea Island formed during the 1831 Surtseyan eruption. Its flat summit, wave-reworked terrace and steep flanks record rapid post-eruptive modification. Historical observations and hydrographic surveys document the destruction of the emergent island and a further ~6 m lowering of its shallowest point between 1883 and 2012–2015; the separate contributions of wave erosion, subsidence and gravitational adjustment cannot be resolved from the available data. The same regional structural framework appears to have governed the distribution of the other volcanic centres, pockmarks, erosional escarpments and mass-transport deposits of the study area. Seventeen pockmarks, up to ~540 m wide and 22 m deep, occur as isolated, clustered and locally aligned depressions; they are associated with subsurface concave-upward reflectors and local water-column acoustic anomalies, consistent with focused fluid escape. Failures of volcanic and sedimentary slopes are widespread, with the largest debris-avalanche deposit covering ~2.2 km². Taken together, these observations indicate that tectonics, volcanism, fluid migration, wave- and bottom-current reworking, and gravitational instability have operated over different timescales to shape Graham Bank. Ferdinandea thus offers a rare historical and geological reference for investigating the rapid construction, degradation and long-term evolution of shallow-water volcanic edifices and highlights the still-open questions regarding the evolution and fate of ephemeral volcanic islands. Full article

(This article belongs to the Section Geological Oceanography)

22 pages, 4374 KB

Open AccessArticle

Noise-Robust DEMON Spectrum Extraction for Ship-Radiated Noise via Adaptive Decomposition and Sparse Reconstruction

by Zikai Wang, Juan Hui, Weiyu Tan and Wenwu Wang

J. Mar. Sci. Eng. 2026, 14(16), 1459; https://doi.org/10.3390/jmse14161459 - 7 Aug 2026

Abstract

To improve the quality of detection of envelope modulation on noise (DEMON) spectra extracted from ship-radiated noise under noisy conditions, a noise-robust DEMON spectrum extraction method incorporating adaptive decomposition and sparse reconstruction is proposed. Ensemble empirical mode decomposition (EEMD) is employed to adaptively [...] Read more.

To improve the quality of detection of envelope modulation on noise (DEMON) spectra extracted from ship-radiated noise under noisy conditions, a noise-robust DEMON spectrum extraction method incorporating adaptive decomposition and sparse reconstruction is proposed. Ensemble empirical mode decomposition (EEMD) is employed to adaptively select the effective frequency band, while the diagonal slice of the third-order cumulant is utilized to suppress Gaussian noise and enhance modulation characteristics. Subsequently, sparse Bayesian learning (SBL) is introduced to reconstruct the DEMON spectrum and improve the spectral resolution. The effectiveness of the proposed method is validated through numerical simulations and lake-trial experiments. The results show that the proposed method produces cleaner spectral backgrounds and improves the clarity of the shaft frequency and its harmonics compared with the conventional EMD-based method, especially under low modulation-depth conditions. These results demonstrate that the proposed method provides an effective solution for robust DEMON spectrum extraction from ship-radiated noise in complex underwater acoustic environments. Full article

(This article belongs to the Special Issue Advanced Research in Underwater Acoustic Signal Processing)

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27 pages, 12660 KB

Open AccessArticle

Gain-Scheduled Sliding Mode Control with Time-Delay Estimation for a Cable-Driven Joint of an Underwater Manipulator

by Xiaopeng Lv, Yuqi Qiao, Qifeng Zhang, Yunfei Bai and Qingfeng Yao

J. Mar. Sci. Eng. 2026, 14(16), 1458; https://doi.org/10.3390/jmse14161458 - 7 Aug 2026

Abstract

Using cable transmission in underwater manipulators helps to reduce the mass and rotational inertia of distal moving components, but the control performance of cable-driven joints is affected by flexible cable transmission, equivalent joint-side friction, hydrodynamic effects, and external disturbances. This paper proposes a [...] Read more.

Using cable transmission in underwater manipulators helps to reduce the mass and rotational inertia of distal moving components, but the control performance of cable-driven joints is affected by flexible cable transmission, equivalent joint-side friction, hydrodynamic effects, and external disturbances. This paper proposes a control method combining time-delay estimation (TDE) with gain-scheduled sliding mode control (GSMC) for a cable-driven joint of an underwater manipulator. TDE uses delayed control-input and joint-acceleration data to estimate and compensate for the lumped dynamic term in the equivalent joint model online. GSMC employs a composite sliding surface and an error-dependent gain-scheduling mechanism to suppress trajectory-tracking errors in the presence of the TDE estimation residual. In joint-level MATLAB/Simulink R2024b simulations, smooth-step, sinusoidal-trajectory-tracking, and ablation results under predefined combined-uncertainty conditions, together with the results of 50 paired Monte Carlo runs, show that TDE-GSMC achieves the lowest major tracking-error indices among the four methods for the smooth-step and

0.35 Hz

sinusoidal trajectories and also yields the lowest mean tracking error and 95th percentile of the disturbance peak in the Monte Carlo simulations; the ablation results further characterize the performance differences among the tested controller configurations. Full article

(This article belongs to the Special Issue Dynamics and Control of Marine Mechatronics)

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22 pages, 21482 KB

Open AccessArticle

Global Sensitivity Analysis of Platform-Mooring Responses for a 15 MW Semi-Submersible Floating Wind Turbine Based on PCE-Sobol and Spearman Methods

by Qiang Liu, Qunyi Wang, Xu Han, Xin Li, Chana Sinsabvarodom and Wei Shi

J. Mar. Sci. Eng. 2026, 14(16), 1457; https://doi.org/10.3390/jmse14161457 - 7 Aug 2026

Abstract

For large-scale floating offshore wind turbines, existing sensitivity studies have not fully addressed the combined effects of multiple uncertain input parameters on multiple output responses. Meanwhile, conventional Sobol indices quantify contribution magnitude but do not indicate effect direction. Based on the IEA 15 [...] Read more.

For large-scale floating offshore wind turbines, existing sensitivity studies have not fully addressed the combined effects of multiple uncertain input parameters on multiple output responses. Meanwhile, conventional Sobol indices quantify contribution magnitude but do not indicate effect direction. Based on the IEA 15 MW semi-submersible benchmark model, this study investigates the sensitivity of mooring tension and platform motion dynamic responses at a normal operating condition under power production. Integrated dynamic simulations were performed to generate response data. Eight uncertain parameters were considered, including the key mechanical and hydrodynamic coefficients of mooring lines as well as mass distribution and hydrodynamics-related key parameters for the platform. A polynomial chaos expansion surrogate model was used for the global sensitivity analysis, based on the Sobol index, Spearman coefficient, and a newly proposed modified Sobol index. The results indicate weak parameter interactions, with first-order Sobol indices dominating. The platform mass makes the largest contribution, with first-order Sobol indices approaching 1.0 for the mean tensions of all three mooring lines and 0.995 and 0.999 for the mean surge and heave displacements, respectively. The mooring line normal drag coefficient reaches a first-order Sobol index of 0.805 for the standard deviation of the upwind mooring line tension. The pitch response is influenced by multiple parameters. The Spearman coefficients confirmed the dominant parameters and identified their effect directions. By integrating variance contribution with effect direction, the modified Sobol index provides a more interpretable assessment of parameter effects. These findings can support parameter prioritization, mooring system design, and digital-twin model updating for floating offshore wind turbines. Full article

(This article belongs to the Special Issue Resilient Offshore Structures: Design, Analysis and Optimization)

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40 pages, 2485 KB

Open AccessArticle

CFD Analysis of Drag and Internal Volume Tradeoffs in a Compact AUV with a Myring Forebody and Flat Stern

by Zhenchao Fu, Jingxing Feng, Zhengyang Zhu, Zhihao Wang, Xiaodong Liu, Yude Shao and Hokeun Kang

J. Mar. Sci. Eng. 2026, 14(16), 1456; https://doi.org/10.3390/jmse14161456 - 7 Aug 2026

Abstract

Low-slenderness-ratio, flat-ended autonomous underwater vehicles must balance hydrodynamic resistance against internal volume retention, yet classical slender-body criteria do not fully represent their coupled forebody wake response. A generalized Myring forebody was assessed for an AUV with L = 0.8 m, D = 0.2 [...] Read more.

Low-slenderness-ratio, flat-ended autonomous underwater vehicles must balance hydrodynamic resistance against internal volume retention, yet classical slender-body criteria do not fully represent their coupled forebody wake response. A generalized Myring forebody was assessed for an AUV with L = 0.8 m, D = 0.2 m, and L/D = 4.0 using 53 steady three-dimensional Reynolds averaged Navier-Stokes simulations with the shear stress transport k-ω model. Gaussian process regression and the non-dominated sorting genetic algorithm II (NSGA-II) were used only for candidate-region screening; production grid direct CFD samples were used to determine nondominance. Strict fold-wise leave-one-out cross-validation gave Q² = 0.173 globally and RMSE = 0.001399 and Q² = 0.683 in the predefined 18-sample decision region, indicating local screening utility rather than global surrogate validation. The direct CFD audit identified 13 globally and seven locally nondominated samples; both previously selected test configurations were dominated after CFD back-substitution. Their three grid drag sequences were monotonic but non-asymptotic. Pressure drag comprised 75.88–79.20% of total drag. However, the reduction in the low-drag test configuration relative to the baseline arose mainly from a lower viscous contribution; axial pressure fields therefore indicate redistribution rather than exclusive drag-reduction causation. Paired CFD samples showed that the sign of the drag responded to N reversal between the two sampled L_nose values, whereas analytical volume increased with N in both pairs. The results reveal a discrete, configuration-dependent drag volume trade-off and local N-L_nose coupling. The rectangular regions are sampling envelopes rather than validated optimum windows, and the conclusions are restricted to steady, deeply submerged, smooth-wall bare-hull conditions. Full article

(This article belongs to the Special Issue Advances in Marine Engineering Hydrodynamics, 2nd Edition)

31 pages, 2635 KB

Open AccessArticle

A Modified Constrained Groove Pressing Process (MCGP) for Enhanced Strength and Microstructural Refinement of Deoxidized High-Phosphorus (DHP) Copper Sheets: Potential Implications for Marine Component Reliability

by Mohsen Forouzanmehr, Mohammad Reza Dashtbayazi, Kazem Reza Kashyzadeh and Mahmoud Chizari

J. Mar. Sci. Eng. 2026, 14(16), 1455; https://doi.org/10.3390/jmse14161455 - 7 Aug 2026

Abstract

Deoxidized high-phosphorus (DHP) copper is widely used in marine heat-exchangers and seawater piping, where long-term structural reliability demands both high strength and a deformation-tolerant microstructure to resist damage initiation. Constrained groove pressing (CGP) is a scalable severe plastic deformation route for metallic sheets; [...] Read more.

Deoxidized high-phosphorus (DHP) copper is widely used in marine heat-exchangers and seawater piping, where long-term structural reliability demands both high strength and a deformation-tolerant microstructure to resist damage initiation. Constrained groove pressing (CGP) is a scalable severe plastic deformation route for metallic sheets; however, the sharp trapezoidal junctions of the conventional die impose parasitic bending strains that produce sinusoidal in-plane hardness variations and anisotropic properties. This study introduces a modified CGP (MCGP) process in which the sharp crest and valley of each 45° tooth are replaced by tangent circular arcs (R₁ = 1.6 mm at the crest, R₂ = 4.8 mm at the valley), removing geometric discontinuities while exactly preserving the groove angle, pitch, and die envelope for drop-in compatibility with existing equipment. DHP copper sheets processed by conventional CGP and MCGP were systematically compared using optical microscopy, SEM, XRD, microhardness, tensile testing, and finite-element analysis. MCGP delivered exceptional mechanical performance: yield strength of 281.19 MPa, ultimate tensile strength of 451.94 MPa (96.4% above the as-received state and 23.8% above conventional CGP), mean hardness of 131.38 HV, and the finest apparent (instrument-uncalibrated) coherent diffraction-domain size of 22.75 nm. Finite-element modelling revealed a lower peak equivalent plastic strain with a more continuously distributed through-thickness deformation path, despite an unchanged nominal grooving strain (≈0.56). Notably, while the modified die redistributes deformation rather than amplifying the nominal strain, the measured through-thickness hardness inhomogeneity factor increased from 7.14% to 21.97% due to strain concentration in the mid-thickness region, indicating that full homogenisation requires further arc-radius optimisation. Nevertheless, the substantial gains in strength and microstructural refinement demonstrate that MCGP offers a promising processing route for producing DHP copper components with enhanced strength and refined microstructures, which may contribute to improved damage tolerance. However, it is acknowledged that direct tests on seawater corrosion, corrosion fatigue, and thermal cycling were not performed in this study; the implications for marine service life are inferred from the established literature on the benefits of grain refinement for corrosion and fatigue resistance. Future work incorporating marine environmental performance tests is recommended to validate these implications. Full article

(This article belongs to the Special Issue Marine Equipment Intelligent Fault Diagnosis)

16 pages, 3629 KB

Open AccessArticle

An Interpolation-Free Near-Field Frequency-Domain Beamforming Algorithm for Uniform Linear Array Sonar

by Zhibin Yue, Jinsong Tang, Heping Zhong, Haoran Wu, Han Li and Meng Zhao

J. Mar. Sci. Eng. 2026, 14(16), 1454; https://doi.org/10.3390/jmse14161454 - 7 Aug 2026

Abstract

To address the issue of phase mismatch, mainlobe broadening, image defocusing, and high computational complexity caused by spherical wavefront propagation in near-field imaging of uniform linear array (ULA) imaging sonar, this paper proposes an interpolation-free near-field frequency-domain beamforming algorithm for uniform linear arrays. [...] Read more.

To address the issue of phase mismatch, mainlobe broadening, image defocusing, and high computational complexity caused by spherical wavefront propagation in near-field imaging of uniform linear array (ULA) imaging sonar, this paper proposes an interpolation-free near-field frequency-domain beamforming algorithm for uniform linear arrays. Under the considered operating conditions, the exact range history is approximated as the sum of a range-dependent quadratic term and a range-independent linear term with respect to the element position. The quadratic term produces negligible envelope migration; therefore, only its phase needs to be compensated. By contrast, the linear term produces more significant range migration, which is corrected together with its associated phase through range-domain and azimuth-domain FFT processing. Under the adopted range-history approximation and discrete sampling conditions, this implementation avoids the explicit point-by-point fractional-delay interpolation required by the backprojection (BP) algorithm. The proposed method substantially reduces the computational cost while maintaining near-field focusing performance comparable to that of the BP algorithm. For the simulated data, the runtime is reduced from 58.23 s to 0.41 s, with only a 0.1% broadening of the azimuth impulse-response width and a 0.11 dB degradation in the peak sidelobe ratio. For the measured lake-trial data, the runtime is reduced from 50.18 s to 0.29 s. Full article

(This article belongs to the Section Ocean Engineering)

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21 pages, 3681 KB

Open AccessArticle

A Wind Data Quality Control Algorithm Utilizing Deep Learning-Based Association Test Rules

by Ruidi Ma, Song Gao, Fan Jiang, Bo Yu, Haoqiang Tian, Yanchen Song, Yong Ge, Dianjun Ren and Chenxu Wang

J. Mar. Sci. Eng. 2026, 14(16), 1453; https://doi.org/10.3390/jmse14161453 - 7 Aug 2026

Abstract

Harnessing the powerful learning and modeling capabilities of artificial intelligence, this study introduces a deep learning-driven wind data quality control algorithm that employs correlation verification rules. By constructing a Dual-Track Information Fusion Network (DTF-Net), it captures local temporal variations in wind speed via [...] Read more.

Harnessing the powerful learning and modeling capabilities of artificial intelligence, this study introduces a deep learning-driven wind data quality control algorithm that employs correlation verification rules. By constructing a Dual-Track Information Fusion Network (DTF-Net), it captures local temporal variations in wind speed via the temporal track and uncovers physical coupling relationships among temperature, pressure, wind direction, and other variables through the global track. Integrating dynamic three-standard-deviation spike detection with 3δ-RMSE spatial validation based on deep learning predictions, the algorithm enables multi-dimensional collaborative anomaly detection in the absence of neighboring stations. Experimental findings demonstrate that the proposed method achieves Mean Absolute Errors (MAE) of 0.217, 0.398, and 0.462 for 1 h, 12 h, and 24 h wind speed forecasts, respectively, representing a 3.8–61.3% reduction compared to general-purpose models like AutoFormer, ITransformer, and FiLM. The anomaly detection rate for quality control ranges from 0.33% to 9.20%, effectively identifying data aberrations during buoy maintenance, equipment failures, and abrupt changes in short-term weather patterns. This study leverages the powerful learning and modeling capabilities of artificial intelligence to establish a novel and easily understandable intelligent quality-control paradigm for sparse ocean observation networks, providing direct practical value for improving the quality of marine meteorological data assimilation. Full article

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31 pages, 19938 KB

Open AccessArticle

Dynamic Analysis of Jacket-Type Offshore Wind Turbine Considering Equivalent Scour Effect and Wind-Wave Directionality

by Bin Wang, Jiawei Yu, Chao Luo, Yujia Tang, Yongqing Lai and Jingxian Fan

J. Mar. Sci. Eng. 2026, 14(16), 1452; https://doi.org/10.3390/jmse14161452 - 7 Aug 2026

Abstract

Jacket foundations, with their excellent adaptability and economic efficiency, have been increasingly widely applied in medium-deep water areas. However, the scouring and erosion effects in the marine environment, coupled with complex wind-wave loads, have severely restricted the long-term safe service of jacket foundations. [...] Read more.

Jacket foundations, with their excellent adaptability and economic efficiency, have been increasingly widely applied in medium-deep water areas. However, the scouring and erosion effects in the marine environment, coupled with complex wind-wave loads, have severely restricted the long-term safe service of jacket foundations. In this study, a structure-pile-soil coupled dynamic response model considering the effects of scour depth and changes in wind and wave directions for the jacket-type offshore wind turbine is developed by integrating the wind and wave load generation capability of OpenFAST and the nonlinear pile-soil interaction analysis function of OpenSees. By quantitatively analyzing key response parameters such as tower top displacement, nacelle acceleration, and internal forces of the foundation tower and pile shaft, this study reveals the significant influence of soil stiffness degradation induced by scour on structural dynamic characteristics, and verifies the effective suppression mechanism of the feathering shutdown strategy on structural responses under extreme loads. The research results indicate that scour has a negligible impact on the fundamental frequency of the jacket-type offshore wind turbine structure, while it significantly reduces the high-order frequencies and leads to a substantial increase in pile shaft internal forces; the effect of wind-wave angle intensifies the spatially coupled vibration response of the structure. The study provides important theoretical and technical support for the anti-scour design, multi-directional load assessment, and formulation of safety control strategies for jacket foundations in complex deep-sea environments. Full article

(This article belongs to the Special Issue Offshore Renewable Energy: Waves, Tides, and Wind)

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30 pages, 20163 KB

Open AccessArticle

Vacuum Preloading for Enhanced Uplift Performance of Suction Buckets in Soft Clay: Model Tests and Hydro-Mechanical Finite-Element Analysis

by Zhen Huang, Chenyang He, Lei Fan, Linkai Wang, Yongjin Zhang and Li Shi

J. Mar. Sci. Eng. 2026, 14(16), 1451; https://doi.org/10.3390/jmse14161451 - 7 Aug 2026

Abstract

Suction bucket foundations provide an efficient foundation solution for offshore wind turbines, but improving their uplift resistance in soft clay remains a practical challenge. Vacuum preloading has been an effective means to enhance soft clay engineering properties, yet its offshore application is constrained [...] Read more.

Suction bucket foundations provide an efficient foundation solution for offshore wind turbines, but improving their uplift resistance in soft clay remains a practical challenge. Vacuum preloading has been an effective means to enhance soft clay engineering properties, yet its offshore application is constrained by the difficulty of maintaining a reliable underwater seal. This study proposes soil-plug vacuum preloading, in which the impermeable skirt and lid of an installed suction bucket serve as a natural sealed boundary for post-installation soil improvement for enhanced uplift performance. Model tests on a bucket equipped with a central prefabricated vertical drain (PVD) were conducted to examine vacuum transmission, soil consolidation, and uplift behaviour. Two vacuum-preloaded cases with prefabricated vertical drain lengths H_PVD = L and H_PVD = 2L, where H_PVD denotes the PVD length and L denotes the bucket skirt length, were compared with an untreated case. Coupled hydro-mechanical finite element analyses were performed to interpret the observed responses. The tests showed that ultimate pullout capacity increased by 197% for H_PVD = L and 288% for H_PVD = 2L. The maximum negative pore pressure beneath the lid increased by 89% and 154%, respectively, while the remaining non-suction resistance also increased markedly due to vacuum-induced consolidation. Prototype-scale numerical analyses of a double-walled bucket were further conducted to investigate the effects of the bucket length-to-diameter ratio (L/D = 0.6, 1.0, and 1.5) and the applied vacuum pressure (0 to −70 kPa) on the uplift response. Among the analysed cases, the largest increase occurred for L/D = 1.5 under an applied vacuum pressure of −70 kPa, where the predicted uplift load at a displacement of 0.20 m increased by up to 42% compared with the no-vacuum condition. These results provide proof-of-concept evidence that soil-plug vacuum preloading may offer a potentially feasible post-installation approach for improving the uplift response of suction buckets in soft clay, although further experimental and field-scale validation is required. Full article

(This article belongs to the Special Issue Marine Geohazards and Offshore Geotechnics)

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19 pages, 9298 KB

Open AccessArticle

Method for Calculating a Generic Oil

by Rintaro Moriyasu, Dalina Thrift-Viveros and Christopher H. Barker

J. Mar. Sci. Eng. 2026, 14(16), 1450; https://doi.org/10.3390/jmse14161450 - 7 Aug 2026

Abstract

Accidental oil spills are an all-too-common occurrence. In order to properly plan for and respond to oil spills, responders and planners need to understand how the oil will behave in the environment: how it will weather and how it will affect ecosystems and [...] Read more.

Accidental oil spills are an all-too-common occurrence. In order to properly plan for and respond to oil spills, responders and planners need to understand how the oil will behave in the environment: how it will weather and how it will affect ecosystems and biota. To address this need, databases of oil properties have been developed, such as NOAA’s Automated Data Inquiry for Oil Spills (ADIOS^®) Oil Database, a publicly available database of oil properties useful for oil spill modelers, responders, and planners, currently containing over 1400 oil records. However, despite its size, when a spill occurs, the actual oil spilled is unlikely to be in the database, even if the oil’s identity is known. The challenge is even greater for planners, who cannot possibly plan for the spilling of thousands of individual different oils. When the exact product is not available, the responder must choose an oil record from the database that closely resembles the product at hand. This process can slow the responder down and may require them to have years of experience to choose the most appropriate oil record. If not done with care, a user can inadvertently select an atypical oil with a similar name, or a record with poor data quality, which can yield inappropriate results. In this work, we generated a set of “generic” oil records for the ADIOS^® Oil Database that have been developed to be a good representation of typical products of a certain type, e.g., “medium crude” or “diesel fuel”. These oil records can then be used in the early stages of a response when details about the spilled product are sparse. These generic oil records can also be very helpful for drills, training, and planning when the user does not need to work with a specific product. These generic records were developed by examining the extensive dataset available in the ADIOS Oil Database and determining which records matched a given type of oil and were of sufficient quality. Then all the records for each oil type were combined to create a “typical” or “average” oil that is representative of that oil type. In the course of this project, statistical methods were chosen that were most appropriate to the property at hand. The oil types chosen were: Light, Medium, and Heavy Crude, Condensate, Jet Fuel, Diesel, Gasoline, Intermediate Fuel Oil (IFO), and Heavy Fuel Oil (HFO). These are all oil types that are likely to be spilled, and for which sufficient data existed in the ADIOS Oil Database to compute an “average” oil. Other potential product types could be added in the future should more data become available. Full article

(This article belongs to the Special Issue Oil Transport Models and Marine Pollution Impacts)

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32 pages, 4370 KB

Open AccessReview

Research Progress of Archimedes Spiral Hydrokinetic Turbines in Free-Flow Conditions: A Comprehensive Review

by Ke Song, Ji Yao, Huiting Huan, Liuchuang Wei and Qingxue Liu

J. Mar. Sci. Eng. 2026, 14(15), 1449; https://doi.org/10.3390/jmse14151449 - 6 Aug 2026

Abstract

Ocean current energy is abundant, yet its exploitation is severely constrained by the low-velocity conditions typical of most marine environments, where conventional lift-type turbines exhibit poor self-starting capability and low efficiency. This review provides the first comprehensive synthesis of research on free-stream Archimedes [...] Read more.

Ocean current energy is abundant, yet its exploitation is severely constrained by the low-velocity conditions typical of most marine environments, where conventional lift-type turbines exhibit poor self-starting capability and low efficiency. This review provides the first comprehensive synthesis of research on free-stream Archimedes spiral hydrokinetic turbines (ASHTs), a class of drag-dominated rotors developed specifically for low-velocity kinetic energy harvesting. A unified classification is introduced, dividing ASHTs into single-blade long-axis (SL-ASHT) and three-blade short-axis (TS-ASHT) configurations. The energy conversion mechanisms, governed by pressure difference and hydrodynamic force synergy within helical passages, are elucidated, and the influence of critical geometric parameters is assessed. For SL-ASHTs, the analysis highlights exceptional self-starting capability (cut-in velocity: 0.1 m/s), a starting torque coefficient of 0.52, a maximum power coefficient of 0.51, and passive yaw adaptability that limits efficiency variation to below 2% over yaw angles of 0–40°. TS-ASHTs feature a compact architecture and higher rotational speed, facilitating direct generator coupling. With variable blade-angle distributions, thin airfoils, and non-uniform gap ratios, the power coefficient reaches 0.312. Performance-enhancement measures, including multi-parameter optimization, ducts, and winglets, deliver power gains of up to 35%, 122%, and 12%, respectively. This review further identifies critical barriers to engineering deployment: sediment erosion, cyclic fatigue, performance degradation under large yaw angles, and wake interactions. Future priorities include multi-objective optimization, advanced materials and flow control, full-scale sea trials, multiphysics coupling, array layout optimization, and hybrid energy system integration. By establishing a coherent classification and performance-evaluation framework, this work demonstrates that ASHTs offer strong potential as core devices for large-scale utilization of low-velocity ocean current and river hydrokinetic energy. Full article

(This article belongs to the Topic Marine Energy)

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16 pages, 4369 KB

Open AccessArticle

Design and Evaluation of Sequential Near-Bottom Mobile Organism Sampling Equipment Intended for Deep-Sea Mining Plume-Affected Zones

by Kaichuang Wang, Haiyang You, Xinghui Tan, Jiale Wu, Bo Han, Junyi Yang, Congchi Huang, Jiawang Chen and Jin Guo

J. Mar. Sci. Eng. 2026, 14(15), 1448; https://doi.org/10.3390/jmse14151448 - 6 Aug 2026

Abstract

Deep-sea mining generates plumes containing heavy metals harmful to marine ecosystems. Assessing their impact on benthic organisms is essential. Traditional monitoring methods commonly employ acoustic, optical, and electrical approaches to investigate collective behaviors of organisms, while direct acquisition and in situ preservation of [...] Read more.

Deep-sea mining generates plumes containing heavy metals harmful to marine ecosystems. Assessing their impact on benthic organisms is essential. Traditional monitoring methods commonly employ acoustic, optical, and electrical approaches to investigate collective behaviors of organisms, while direct acquisition and in situ preservation of biological specimens during deployment remain challenging. To address this limitation, a novel sequential near-bottom mobile organisms sampling equipment equipped with multiple chambers was developed. Sequential sampling of near-bottom mobile organisms was conducted to obtain samples at different time points for individual-level biological assessment. This study investigates the alcohol diffusion dynamics at various injection volumes. The results show that during injection, alcohol is concentrated in the upper section of the chamber. When 5.0 L of alcohol is introduced, the alcohol mass fraction within the sampling container rapidly reaches and stabilizes at 87%. The sampling equipment’s performance was evaluated through dock experiments and deep-sea trials at a depth of 1571 m in the South China Sea. Field tests successfully captured five jellyfish, with alcohol mass fractions of 85%, 87%, and 87% achieved in the respective chambers. These results demonstrate the feasibility of the equipment for sequential biological sampling and in situ preservation in deep-sea environments. Full article

(This article belongs to the Section Ocean Engineering)

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31 pages, 2170 KB

Open AccessReview

Methodologies for Underwater Geomagnetic Navigation: Progress and Prospects

by Wenjun Zhang, Jiaqing Chen, Menghang Wu, Ye Li, Zhe Dong, Li Wang and Teng Ma

J. Mar. Sci. Eng. 2026, 14(15), 1447; https://doi.org/10.3390/jmse14151447 - 6 Aug 2026

Abstract

High-precision, long-endurance navigation remains a central bottleneck for autonomous underwater vehicles (AUVs) operating in GNSS-denied, acoustically constrained, and dynamically disturbed marine environments. This manuscript examines the complete sensing-mapping-estimation chain for underwater geomagnetic navigation. It distinguishes scalar and vector measurements; compares shipborne, towed, and [...] Read more.

High-precision, long-endurance navigation remains a central bottleneck for autonomous underwater vehicles (AUVs) operating in GNSS-denied, acoustically constrained, and dynamically disturbed marine environments. This manuscript examines the complete sensing-mapping-estimation chain for underwater geomagnetic navigation. It distinguishes scalar and vector measurements; compares shipborne, towed, and AUV-mounted survey configurations, calibration requirements, platform-interference mitigation, and uncertainty sources; reviews global, regional, and local magnetic models; and evaluates nonlinear map-aided positioning. Existing approaches are organized into map-based matching, filter-aided navigation, geomagnetic simultaneous localization and mapping (SLAM), and matching-area adaptability assessment. Their assumptions, data requirements, uncertainty treatment, accuracy evidence, and computational burden are critically compared. Persistent gaps include magnetic cleanliness, three-dimensional mapping, weak-feature-area observability, benchmark datasets, uncertainty quantification, and reproducible long-duration sea trials. Full article

(This article belongs to the Section Ocean Engineering)

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