Remote sensing and AIS-based navigation risk assessment under sea ice conditions in the Bohai Sea

Sea state reconstruction based on the Spearman rank correlation using full-scale data from a containership

This paper presents a data-driven framework for quantifying attainable ship speed uncertainty considering weather forecast uncertainty. The methodology integrates two parallel workflows: weather forecast processing and ship performance simulation. Weather forecast data from NOAA and GFS sources are collected at multiple lead times (0-24h, 24-72h, 72-120h, 120-168h). The data undergo spatial discretisation over a North Atlantic rectangular grid, extracting the main meteorological variables, including significant wave height, peak period, wave direction, wind speed, and wind direction. Ship performance simulations were done using Wärtsilä NaviTrainer NTPRO 5000 and HydroComp NavCad to generate attainable ship speed lookup tables under varying conditions: intended speeds (14.5, 13.5, 12.0 kn), wave heights (0-14 m according to WMO Sea State Codes 0-8), and wave encounter angles (0°-180°). Multiple metrics were used for uncertainty quantification, including RMSE, MAE, Bias, UGR, CRPS, IoA, and FSS for meteorological variables, alongside CMAE for directional parameters. These metrics are subsequently applied to estimated attainable ship speeds, establishing response variable uncertainties. Correlation analysis was conducted between the uncertainty of meteorological variables and the uncertainty in attainable ship speed, providing important insights for estimated time of arrival (ETA) calculations and voyage planning under weather uncertainty.

Automated Real-Time Localized Sea State Estimation During Navigation Based on the Beaufort Scale

Innovative integrated damping mooring technology for floating wind turbines under extreme sea conditions

To address the challenge that unmanned surface vehicles (USVs) struggle to effectively balance tracking accuracy, control smoothness, and system energy efficiency under external disturbances, this paper proposes an anti-disturbance path tracking control method integrating quantum-inspired optimization (QIO) and dynamic game theory (GT). The proposed control method consists of a two-layer optimization architecture: the upper layer employs dynamic game theory to optimize the guidance process, modeling the optimization of the look-ahead distance (Ld) and switching radius (R) in the LOS guidance algorithm as a non-cooperative game, and achieves adaptive adjustment to path variations and environmental disturbances by solving for the Nash equilibrium. The lower layer, based on a quantum-inspired optimization algorithm, enhances the control process by employing quantum bit probability amplitude encoding for the PID parameter space and utilizing a quantum rotation gate mechanism for efficient global search, thereby achieving online self-tuning of PID parameters under environmental disturbances. Simulation results indicate that, under sea conditions with external disturbances, the proposed method achieves a superior balance among tracking accuracy, control smoothness, and system energy efficiency compared to the traditional fixed-parameter PID-LOS approach, enhancing the comprehensive anti-disturbance robustness of the USV.

This paper presents a methodology for determining the wave surface profile and the velocity components of water particles from the wave spectrum of a random sea state to calculate the loads acting on the buoy. For the calculation of mooring lines with attached weights under random loads, the finite element method (FEM) is applied. The parameters of the governing dynamic equations are identified, the Newmark method is used for time integration, and a computational program is developed. The program is verified by comparing the results of the static problem with the dynamic problem under constant loading, demonstrating the correctness of the algorithm and computation. The program is then applied to calculate mooring lines with attached weights for an actual navigational buoy subjected to wind, current, and random wave loads, demonstrating the practical applicability of the proposed approach.

Offshore support operations must balance safety and sustainability under highly variable sea conditions. Deterministic motion analyses can underestimate extreme vessel responses, leading to insufficient operational limits and increased environmental impact. We develop a fuzzy‐enhanced multi‐body dynamics framework in which key inputs significant wave height, peak period, added mass, and radiation damping are represented as fuzzy numbers. An α-cut decomposition yields interval bounds at each confidence level, and a fourth-order Runge-Kutta scheme integrates the six-degree-of-freedom equations of motion for both lower and upper “vertex” systems. A case study off the Karnataka coast applies both full 6-DoF and single-DOF heave approximations to demonstrate methodology. The heave response envelopes under calm (nominal α = 1: 0.73 m; full range at α = 0: 0.64–1.64 m) and severe (nominal 1.58 m; range 1.32–2.36 m) sea states reveal potential underestimations of 124 % and 49 %, respectively, when using only nominal values. By selecting an operational α-level (e.g., α* = 0.35 to cap heave ≤ 1.8 m), decision-makers can balance risk tolerance and conservatism. Sensitivity analysis identifies significant wave height as the dominant uncertainty driver. Computational trade-offs and adaptive α-sampling strategies are discussed. This work provides a self-contained, uncertainty-aware tool for deriving operational envelopes that improve risk-informed planning and enable fuel-efficiency optimization. By embedding fuzzy uncertainty quantification into vessel dynamics, the methodology supports safer, more sustainable marine operations and can be extended to real-time sensor fusion, multi-vessel interactions, and frequency-dependent hydrodynamics.

Floating photovoltaic (FPV) systems are increasingly deployed in offshore environments. Among various FPV concepts, membrane-type platforms offer distinct advantages, including reduced weight, lower material consumption, and cost-effectiveness. This study investigates the hydrodynamic response of a membrane-type offshore FPV system through a 1:40 scale physical model test based on the Ocean Sun prototype. Static-water free-decay tests were first conducted to determine the natural periods and damping characteristics in heave, surge, and pitch motions. Subsequently, irregular-wave tests were performed under seven sea states representative of an offshore demonstration site. Free-decay results show model-scale natural periods of approximately 1.0 s for heave, 0.8 s for pitch, and 15 s for surge. The long surge natural period avoids resonance with short-period waves, while the high damping in heave and pitch effectively limit dynamic amplification. Under irregular waves, heave and pitch motions remain small, whereas surge motion exhibits pronounced long-frequency excursions. Spectral analysis reveals a dominant low-frequency surge peak at f ≈ 0.067 Hz (corresponding to the natural period of 15 s), superimposed with higher-frequency components associated with wave-induced motions. A strong correlation is observed between low-frequency surge and mooring tensions. Across Sea States 1–6, the motion responses increase gradually, while a marked rise in the exceedance probability of mooring forces occurs only in the most severe sea state. Weibull extreme-value fits show good linearity, indicating that the measured extremes are statistically consistent. The results provide experimental data and design insights for membrane-type FPV systems, establishing a foundation for future hydroelastic studies.

In early 2025, the Jason-3 satellite’s orbit shifted from an “interleaved” to a tandem configuration with Sentinel-6A, and its Geophysical Data Records (GDR) were upgraded from Version F to G. This study evaluated GDR-G via eight processing approaches, using Jason-3’s last six GDR-F cycles (#394–#399) and first six GDR-G cycles (#501–#506), integrating histogram/geographical distribution analyses of Sea Surface Height Anomaly (SSHA), Significant Wave Height (SWH), Wind Speed (WS), and multi-method validation (e.g., self-cross-calibration). Key findings include the following: GDR-G had significantly lower SSHA noise than GDR-F, with up to ~4 cm SSHA bias from different retrackers/corrections; Adaptive retracker + 3D Sea State Bias (SSB) correction achieved optimal accuracy. Adaptive retracker’s SWH/WS anomalies linked to invalid MLE4 results and non-Brownian waveforms (coastal/sea ice). A detrending method was proposed, and the 41-point Lanczos window was optimal for smoothing. The results from the “detrending method” were consistent with the results based on the SSHA spectrum and classic self-cross-calibration methods. A ~5 mm drop was observed in Jason-3 GDR-G MLE4 baseline SSHA, probably caused by GDR upgrade or geographic sampling mismatch, while Sentinel-6A’s GDR-G upgrade might induce ~1 cm jump. The jumps along with GDR version upgrade highlighted the value of timely in situ absolute calibration.

Wave–wind energy conversion systems integrated have become an interesting alternative for enhancing the reliability of renewable power generation, mainly at maritime/coastal sites with severe resourcevariation. However, the interconnection of two very fluctuating energy sources impose great power quality challenges, dynamic stability and coordinated control. In this study, a new hybrid wave–wind system aimed at real time energy management and MPPT has been developed based on Fuzzy Logic –MPC driven MOPSO optimization algorithm is proposed. The proposed structure combines: (i) a Fuzzy Logic Controller (FLC) for adaptive MPPT control in non-linear and fast changing sea states; (ii) a Model Predictive Controller (MPC) for short-horizon optimisation of active/reactive power flows and converter dynamics; and, (iii), a MOPSO supervisory layer that continuouslymonitors the efficiency, power smoothness and mechanical stress system-wide.Numerical results indicate that the proposed FLC–MPC control is truly responsive to fast variations of wave height and wind turbulence, up to 22% improvement in MPPT tracking accuracy with respect to conventional approaches being reported. By allowing the system to adjust the number of optimal control parameters off grid the MOPSO algorithm achieves a 17% decrease in power oscillations, 28% improved energy capture ratio as well as significant decreases on turbine and point absorber mechanical loading. Moreover, the hierarchical mode guarantees that both the storage use and grid-connected operation are implemented in a coordinated manner, such that with complex ocean–wind disturbances, the voltage and frequency can be stably regulated.In conclusion, it can be noted that the multi-layer intelligent control approach satisfied energy capture maximum effort and system reliability, as well as improved power quality for in integrated wave–wind energy systems. This work provides a scalable platform for the next-generation marine renewable hybridization and enables the transition to sustainable offshore energy infrastructure and secure coastal microgrids.

Abstract. Wave-driven fragmentation is a key mechanism shaping the Marginal Ice Zone (MIZ). Capturing this process is therefore essential for improving sea ice models, which currently do not fully capture the complex interactions between the forcing imposed by waves and the nonlinear dynamics of the resulting sea ice breakup and deformation. To investigate these interactions, we introduce a comprehensive multi-instrument dataset from a field campaign in the MIZ of the St. Lawrence Estuary, Canada, designed to characterize wave propagation and mechanical properties of sea ice under natural forcing conditions. The dataset integrates synchronized measurements from geophone arrays, wave buoys, smartphones configured as motion sensors, and Unmanned Aerial Vehicles (UAVs), all collected during coordinated deployments across diverse ice types and sea states. Seismic data, recorded with geophone arrays, enable estimation of the ice thickness and elastic properties via active and passive wavefield analyzes. Concurrently, wave buoys and smartphones capture ocean wave characteristics including amplitude, wavelength, and attenuation near ice edges. UAV imagery is processed with advanced methods to detect vertical ice displacements with sub-centimetre sensitivity, allowing extraction of wave dispersion relations in different ice conditions. Preliminary analyzes demonstrate strong agreement between independent measurement methods, validating the dataset's quality. This multi-sensor approach offers unique opportunities to improve our understanding of wave-ice interactions, wave attenuation, and fracture dynamics in situ, thus offering a valuable resource for the sea ice and oceanographic research community to gain insight in wave-induced ice break-up mechanisms under natural conditions.

This study proposes a novel artificial neural network-based methodology for classifying the incident wave direction during ship navigation using the heave–roll–pitch motion response spectra as input. The proposed model demonstrated a balanced performance with an overall accuracy of approximately 0.888, effectively classifying the wave direction into three major categories: head-sea, beam-sea, and following-sea. The methodology utilizes Response Amplitude Operators derived from linear potential flow theory to generate motion response spectra, which are then used to classify the incident wave direction. The model effectively learns the frequency-distribution characteristics of the response spectrum, enabling wave direction classification without the need for complex inverse analysis procedures. This approach is significant in that it allows wave direction recognition solely based on measurable ship motion responses, without the need for additional external sensors or mathematical modeling. This data-driven approach has strong potential for integration into autonomous ship situational awareness modules and real-time wave monitoring technologies. However, the study simplified the directional domain into three representative groups, and the model was validated primarily using a numerically generated dataset, indicating the need for future improvements. Future research will expand the dataset to include a broader range of sea states, improve directional resolution, and explore continuous wave direction prediction. Additionally, further validation using field-measured data will be conducted to assess the real-time applicability of the proposed model.

The paper examines key factors controlling gas hydrate formation on Arctic shelves, including subsea permafrost and ice sheet dynamics, which influence environmental and geotechnical conditions in shallow Arctic seas. It presents results of numerical modelling of the evolution and present-day distribution of relic subsea permafrost and the gas hydrate stability zone (GHSZ) across the Eurasian Arctic shelf over the past 26 000 years. The modelling framework incorporates glacial isostatic adjustment and the impact of the Eurasian Ice Sheet on pressure and temperature conditions affecting hydrate stability. Four main GHSZ types are identified: permafrost-associated; subglacial, formed under high pressures of the ice sheet; post-glacial, preserved due to elevated hydrostatic pressure from sea level rise; and post-permafrost, persisting after permafrost degradation due to the high thermal inertia of marine sediments. The model shows that an extensive subglacial GHSZ developed in the central Barents Sea during the Last Glacial Maximum, with remnants potentially still persisting today. Modelling also reveals recent GHSZ growth near the outer shelf edge, likely linked to sea level rise. These findings highlight geotechnical risks such as seabed weakening, subsidence, slope failure, and methane release critical for Arctic offshore infrastructure and resource development.

The suspension system is a critical component of a small catamaran rescue boat. Traditional spring suspension systems have weak damping capabilities and offer poor ride comfort. In this study, a novel three-degree-of-freedom hydraulically interconnected vibration-damping suspension system was developed for small rescue vessels. A simulation model of the suspension system was built using the AMESIM simulation platform, and its characteristics were analyzed under various operating conditions. To validate the proposed suspension system, its stability and vibration-damping performance were thoroughly examined. The results demonstrated that the new suspension system successfully achieved three-degree-of-freedom motion (including roll, pitch, and heave motions) compensation of the upper platform displacement under three levels of sea conditions. Additionally, it effectively absorbed wave impacts and reduced deck platform vibrations. The stability of the small rescue vessel was found to be most affected by crest wave impacts. The damping capacity of the suspension system was closely related to the sea state level but was less influenced by wave direction. Moreover, within the frequency range of 4–8 Hz, the acceleration amplitude of the main hull was significantly reduced, leading to a notable improvement in ride comfort.

ABSTRACT The centennial transformation in maritime engineering, from the era of the Titanic to today’s AI-driven systems, underscores the importance of interdisciplinary collaboration, international cooperation, and especially interaction between industry and academia. This article highlights how such partnerships foster mutual education and innovation, with a particular focus on asset management for ships and offshore structures. A case study is briefly presented that demonstrates the effectiveness of combining academic expertise, industry experience, and defense-driven objectives to enhance the prediction and assessment of vessel responses under extreme sea states. The outcomes have significant implications for extending the operational life of assets, ensuring crew safety, and addressing economic and environmental challenges. Looking forward, sustained progress in this field will increasingly depend on advanced numerical simulation, real-time data integration, and closer engagement between researchers, industry practitioners, and technology developers.

Accurate prediction of ship roll motion is essential for safe and autonomous navigation. This study presents a deep learning framework that estimates both roll motion and epistemic uncertainty using Monte Carlo (MC) Dropout. Two architectures, a Long Short-Term Memory (LSTM) network and a Transformer encoder, were trained on HydroD–Wasim simulations covering various sea states, speeds, and damage conditions, and validated with real voyage data from two ferries, showing complementary performance, where LSTM achieved higher accuracy and Transformer provided more reliable confidence intervals. Model performance was evaluated by mean squared error (MSE), prediction interval coverage probability (PICP), and prediction interval normalized average width (PINAW). The LSTM achieved lower MSE, showing superior deterministic accuracy, while the Transformer produced higher PICP and wider PINAW, indicating more reliable uncertainty estimation. Results confirm that MC Dropout effectively quantifies epistemic uncertainty, improving the reliability of deep learning–based ship motion forecasting for intelligent maritime operations.

It is recognized that a ship’s performance, speed, fuel consumption, and resistance are impacted by the marine environment. The magnitude of this effect, which can be altered by ship design and operational conditions, necessitates added resistance calculations for optimizing these phases. Ship designers can generate efficient hull forms and operators can make sound navigational decisions to reduce emissions within the service zone. For this research, air and wave resistances were calculated using the KCS hull form with a superstructure during a simulated voyage in the North Pacific Ocean. To verify the results, data from towing tank tests available in the literature were used, along with calm water resistance calculations obtained from a computational fluid dynamics (CFD) analysis conducted for this study. When transporting 3600 loaded containers, sea conditions at model-scale impact the ship’s power requirements, leading to air resistance from the superstructure (aerodynamic) and hull resistance from head waves. This research compares the increased wave and air resistance with calm water resistance to provide important insights into the main engine power requirements when traveling in this region. Cruising between 14 and 18 knots generates 8–11% added resistance when encountering head waves at Sea State 5.

This paper is dedicated to solving the environmental perception system problem of unmanned surface vehicles (USVs) experiencing adverse sea conditions and complex mission scenarios. First, the functionalities and characteristics of each subsystem in the USV environmental perception system under different mission scenarios are analyzed, and an efficient and stable environmental perception system is designed. Second, the static and dynamic characteristics of the sea–sky line are investigated, along with the impacts on each subsystem of the environmental perception system when the USV experiences six-degree-of-freedom motion on the sea surface. Based on the above analysis, a sea–sky line detection method based on the radar–electro-optical system is designed. This method utilizes the features of the radar and electro-optical subsystems to redefine the region of interest, effectively suppressing interference from non-sea–sky line edges, thereby improving detection efficiency and accuracy. Furthermore, a sea–sky line-based target detection algorithm is proposed, which confines the search area to the vicinity of the detected sea–sky line, significantly reducing false detections caused by sea clutter and noise. Sea trials demonstrate that the proposed method enhances the accuracy and real-time performance of USV environmental perception. The proposed systematic approach offers a practical solution for improving the robustness of USV environmental perception in complex marine environments. Sea trials have shown that the method improves the effectiveness of target information by 3.61%.

Accurate prediction of significant wave height (SWH) is central to coastal ocean dynamics, wave–climate assessment, and operational marine forecasting, yet many high-performing machine-learning (ML) models remain opaque and weakly connected to underlying wave physics. We propose an explainable, feature engineering-guided ML framework for coastal SWH prediction that combines extremal wave statistics, temporal descriptors, and SHAP-based interpretation. Using 30 min buoy observations from a high-energy, wave-dominated coastal site off Australia’s Gold Coast, we benchmarked seven regression models (Linear Regression, Decision Tree, Random Forest, Gradient Boosting, Support Vector Regression, K-Nearest Neighbors, and Neural Networks) across four feature sets: (i) Base (Hmax, Tz, Tp, SST, peak direction), (ii) Base + Temporal (lags, rolling statistics, cyclical hour/month encodings), (iii) Base + a physics-informed Wave Height Ratio, WHR = Hmax/Hs, and (iv) Full (Base + Temporal + WHR). Model skill is evaluated for full-year, 1-month, and 10-day prediction windows resulting in 84 distinct configurations. Performance was assessed using R2, RMSE, MAE, and bias metrics, with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) employed for multi-criteria ranking. Inclusion of WHR systematically improves performance, raising test R2 from a baseline range of ~0.85–0.95 to values exceeding 0.97 and reducing RMSE by up to 86%, with a Random Forest|Base + WHR configuration achieving the top TOPSIS score (1.000). SHAP analysis identifies WHR and lagged SWH as dominant predictors, linking model behavior to extremal sea states and short-term memory in the wave field. The proposed framework demonstrates how embedding simple, physically motivated features and explainable AI tools can transform black-box coastal wave predictors into transparent models suitable for geophysical fluid dynamics, coastal hazard assessment, and wave-energy applications.