Search Results (1,833)

Marine energy technologies offer renewable alternatives to conventional energy sources by harnessing ocean-based resources such as wave motion, tides, temperature, and salinity gradients. They are particularly promising for coastal and island regions. This paper presents a literature-based assessment of the technical potential and limitations of these resources, with a focus on the Adriatic Sea as a model for low-energy, semi-enclosed basins. Resource availability and technological maturity are systematically reviewed. Results indicate that wave energy offers the highest regional potential, with peak annual mean wave power reachig up to 2.784 kW/m near the southern offshore regions of the Adriatic. However, current resource levels limit feasibility to down-scaled, modular installations. Tidal and thermal energy are constrained by the Adriatic’s microtidal regime and limited temperature gradients. Although still in early development, salinity gradient systems may become viable near major river mouths such as those of the Po and Neretva. In addition to technical analysis, broad environmental and socio-economic considerations are reviewed to inform responsible marine energy development. These findings help define strategic development and research priorities for marine renewables in enclosed seas and other resource-constrained marine environments. Full article

In this paper, machine learning techniques and risk factor analyses are applied to a marine hazard potential map of the Taiwan Strait. The waters surrounding Taiwan are characterized by dense maritime traffic, including commercial cargo transportation and fishing operations. Marine accidents caused by severe weather conditions are frequently reported, leading to irreversible loss of life and property. To mitigate these risks, this study utilizes the XGBoost machine learning model in conjunction with oceanic parameters and historical accident statistics to map the risk potential distribution of maritime accidents across the Taiwan Strait on a monthly basis. To address the challenge of limited historical accident data, this research employs a TVAE (Tabular Variational Autoencoder) to generate synthetic maritime accident data. The quality of such synthetic data is evaluated by comparing the similarity of probability distributions between the original and synthetic datasets. The resulting risk potential maps indicate that risk levels are significantly higher during the winter and lower during the summer. Furthermore, the SHAP (SHapley Additive exPlanations) model is applied to analyze key risk factors, identifying wave height as the primary driver, followed by meridional (north–south) wind speed and the primary spatial modes of wave height. These findings are validated using the National Ocean Database and Sharing System (NODASS) data, providing a comprehensive explanation of the underlying physical mechanisms. This study has successfully utilized the XGBoost machine learning model together with the TVAE generative technique to develop monthly marine hazard potential distribution maps for the Taiwan Strait. The novel research flowchart employed in this study can be applied to many other marine problems. Full article

Predicting the motion of ships and floating structures is essential for ensuring economical and environmentally friendly operations in the ocean. In this study, we propose a hybrid encoder–decoder Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) architecture to predict motions of a moored Floating Production Storage and Offloading (FPSO) vessel under varying sea conditions. The model integrates a CNN for spatial wave-field feature extraction and an LSTM encoder–decoder to capture temporal dependencies in vessel motion. Synthetic datasets were generated using mid-fidelity dynamics simulations of a coupled FPSO–mooring–riser system subjected to wave excitations. Five sea states ranging from calm to severe were considered to evaluate the model’s robustness. A key preprocessing step involved determining the optimal spatial domain for wave field input, and a wave field size of 600 m × 600 m was identified as the most cost-effective configuration while maintaining accuracy. The model was validated using the Root Mean Square Error (RMSE) or relative RMSE (RRMSE). Despite low RRMSE values in low sea states, predictions were noisier due to high-frequency, low-amplitude responses. In contrast, higher sea states yielded more stable predictions despite higher RRMSE values. The proposed method offers high-resolution motion forecasting capability, which can enhance operational safety and energy efficiency of offshore platforms, particularly when integrated with stereo camera-based wave monitoring systems. Full article

This study investigates the temporal and spatial variations in sea level anomaly (SLA) and sea surface wind in the East China Sea (ECS) from 1993 to 2021 using AVISO altimetry data and ERA5 reanalysis wind data. Empirical Orthogonal Function (EOF) and trend analyses were applied to identify dominant modes and long-term changes. Results reveal pronounced seasonal SLA variability, with lower levels in winter/spring and higher levels in summer/autumn, strongly modulated by monsoon winds. The first EOF mode of SLA accounted for 52.73% of variance, showing basin-coherent seasonal fluctuations, while the second mode (7.79%) reflected contrasts between coastal and Kuroshio-influenced regions. The ECS experienced an average sea level rise of 3.77 mm/year, exceeding 6 mm/year along the Jiangsu and Zhejiang–Fujian coasts. Sea surface wind stress variability was greatest in the northern Taiwan Strait and southwest of the Ryukyu Islands, but decreased along the Zhejiang coast. Sea level anomalies (SLAs) in the East China Sea exhibit clear multi-scale coupling with the wind field. The seasonal SLA variability in the East China Sea is jointly modulated by local Ekman forcing due to wind stress, while also being potentially linked to the Kuroshio and open-ocean Rossby waves. These findings underscore the role of wind forcing in regional sea level changes and provide insight for coastal management under climate change. Full article

This study examines a nonlinear partial differential equation, namely the (3+1)-dimensional Boussinesq-type equation. To explore this model, three versatile analytical approaches are applied: the Exp-function method, the Kudryashov method, and the Riccati equation method. Using these techniques, a range of exact analytical solutions is derived, exhibiting diverse structural forms such as periodic, kink-type, rational, and trigonometric solutions. The analysis reveals the rich dynamical behavior of the equation and demonstrates its effectiveness in modeling a variety of nonlinear wave phenomena across different physical contexts. Several of the obtained solutions are illustrated through graphical representations for better interpretation. The results include hyperbolic, trigonometric, and rational function solutions, along with a sensitivity analysis. To highlight the physical relevance of the findings, suitable parameter values are selected, and the corresponding wave behaviors are visualized using three-dimensional and contour plots generated with Maple 2024. Overall, the study provides valuable insights into the mechanisms underlying the generation and propagation of complex nonlinear phenomena in fields such as fluid dynamics, optical fiber systems, plasma physics, and ocean wave transmission. Full article

Wind-driven shear and vertical mixing in the upper meter of the ocean strongly regulate near-surface circulation and buoyant tracer transport, yet direct field observations immediately beneath the air–sea interface remain scarce. We present Lagrangian observations, equipped with an upward-looking Acoustic Doppler Current Profiler (ADCP), collected during 5–7 April 2022 in the Jeju Strait under wind stresses of 0.0006–0.19 Pa. Near-surface shear and turbulence metrics were resolved within the top surface layer (TSL), and a response-time analysis showed that upper-layer shear responded most promptly to wind variability, whereas deeper-layer shear and sea-state metrics adjusted more slowly. Wave-period variability exhibited the weakest coupling, indicating additional nonlocal influences. Reynolds-stress estimates showed that the along-wind momentum flux was predominantly negative, indicating net downward transfer of downwind momentum, while cross-direction fluxes were smaller on average and frequently reversed sign, consistent with intermittent lateral transfers associated with evolving wave–current interactions. Using an eddy-viscosity framework, we derived stress-based exponential-saturation parameterizations for depth-averaged shear and vertical diffusivity, with the diffusivity magnitude treated as sensitive to the assumed turbulent Prandtl number. The relationships are intended for event-scale conditions within the observed forcing range and provide field-constrained, implementation-ready formulations for near-surface transport and mixing models. Full article

Typhoon avoidance is critical for ship maneuvering safety under extreme meteo-ocean conditions. This study proposes a data-driven framework that converts AIS trajectories into interpretable course deviation and speed change responses for navigational decision support. After AIS cleaning, temporal resampling, and matching with gridded wind, wave, and current fields, rule-based sliding-window and regression procedures, informed by experienced captains and company staff, automatically generate proxy labels for deviation and speed reduction. Samples are stratified by vessel size to reflect differences in inertia and maneuverability, and XGBoost classifiers are trained with simple resampling to mitigate class imbalance. The framework is demonstrated on a single-event case study of Typhoon Yagi in the South China Sea, covering 8609 vessels and reconstructed sailing fragments. On the test set, the deviation model achieves 89.8% accuracy and high recall for deviation cases, while the speed change model reaches 82% balanced accuracy under the proxy-label setting. Results suggest a scale-dependent response: smaller vessels exhibit more frequent course deviation, whereas larger vessels more often reduce speed under severe wind-wave loading. The framework offers a proof-of-concept approach to derive behavior-based indicators from AIS and environmental data and may support situational assessment under adverse weather. Full article

Reanalysis wave datasets are essential for understanding wave conditions along the West African coast, a region with over 350 million people and diverse economic activities. This study evaluates the effectiveness of various datasets, including ERA5, WAVERYS, satellite (HY-2B/HY-2C), and buoy measurements, focusing on significant wave height (Hs). WAVERYS was found to better match in situ conditions compared to ERA5, making it the preferred dataset for climate estimation. This study found that wave heights (Hs) of WAVERYS in the region range from 0.5 m to 3.2 m, with waves primarily coming from the south and southwest, having periods between 3.8 s and 25 s. Swell, originating from the South Atlantic Ocean, dominates the wave climate, while local wind waves contribute only about 5% to the overall sea state energy. Seasonal analysis showed that the highest waves occur between June and September, coinciding with the South Atlantic winter and stronger winds. The validation performed in this study confirms that the WAVERYS reanalysis can reliably be used as a source of wave data in the Gulf of Guinea. This recommendation is based on its consistently better agreement with the available in situ observations and its improved representation of wave dynamics in the region. At locations where buoy measurements exist, in situ data should remain the primary reference for site-specific applications; however, such measurements are spatially sparse and temporally limited across West Africa. Consequently, WAVERYS provides a practical and robust alternative for regional-scale analyses, long-term assessments, and operational applications in areas lacking direct observations, making it particularly valuable for coastal risk assessment, engineering design, and marine operations in the region. Full article

The Licantén coastal area in central Chile was severely impacted by the 2010 Mw 8.8 Cobquecura earthquake and subsequent tsunami, exposing the high vulnerability of coastal communities. Over the past decade, urban expansion has advanced toward the shoreline, increasing exposure to coastal hazards. This study aims to quantify shoreline dynamics and urban growth in Licantén between 2010 and 2025. We integrated satellite-derived shorelines (SDSs) from Landsat and Sentinel-2 imagery, ERA5 ocean reanalysis to characterize extreme wave events, and an open-source building footprint dataset with high-resolution imagery for urban mapping. Results indicate a post-earthquake acceleration in shoreline erosion up to 5 m per year and a rise in extreme wave events linked to climate variability. Urbanized areas expanded by an average of 46.3%, intensifying risk in hazard-prone zones. These findings highlight the urgent need for evidence-based coastal planning, including zoning and land-use restrictions, to reduce exposure and enhance resilience. This research contributes to climate adaptation strategies and sustainable coastal management in Chile. Full article

Floating offshore wind turbines (FOWTs) are subjected to complex oceanic environmental loads, which can result in non-collinear wind and wave directions that may not align with the rotor axis, potentially leading to complex variations in aerodynamic characteristics. In this study, the aerodynamic performance and wake of the NREL 5 MW wind turbine under different inflow angles and platform surge motions in various directions were investigated using the actuator line model (ALM) implemented in OpenFOAM. The results demonstrate that an increase in surge amplitude primarily amplifies the cyclic fluctuations in rotor thrust and torque, while the direction of surge motion has a negligible influence. In contrast, yawed inflow leads to a substantial reduction in both the mean and peak values of thrust and torque. Wake analysis further reveals that the mean wake recovery is predominantly governed by the yaw angle. Under aligned inflow conditions, the wake remains nearly symmetric and shows limited sensitivity to platform surge motion. Conversely, yawed inflow induces significant wake deflection with an asymmetric distribution of turbulent kinetic energy and enhanced mixing in the downstream region. Full article

In this work, we examine the prospects of matching the Kadomtsev–Petviashvili (pKP) equation with the B-type Kadomtsev–Petviashvili (BKP) equation, which we will call the pKP-BKP equation. The resulting model gives a rigorous mathematical framework for describing long wave phenomena in oceans, impoundments and estuaries and for forecasting tsunamis; river, tide and irrigation flows; and wave patterns in the atmosphere. Using a consolidated method of analysis based on symmetry reductions and rational function transformations, we obtain several classes of exact solutions composed of rational, periodic, breather and kink-wave structures. These methods shed light on the interplay between symmetries that control the formation of soliton solutions, hence allowing the construction of new families of analytical soliton solutions. The solutions obtained are linked together through spectral degeneracies and reductions in symmetry. These methodologies are presented in a systematic way, emphasizing their applicability to a general class of nonlinear evolution equations. The results of the analysis are substantiated through direct substitution, and the structural characteristics of the solutions are discussed in detail. As a result, these results expand the solution space of the pKP–BKP equation and provide better analytical insights into Kadomtsev–Petviashvili-type nonlinear evolution equations. Full article

Marine heatwaves (MHWs) are prolonged extreme warming events that pose severe threats to marine ecosystems and coastal communities, necessitating reliable prediction capabilities for climate adaptation and marine resource management. Traditional numerical models, while physically grounded, are constrained by computational costs and error accumulation, whereas purely data-driven approaches often lack physical consistency and generalize poorly to extreme events. To address these challenges, this study proposes a Heat Wave-Optimized Physics-Informed Neural Network (HW-OPINN) that synergistically integrates ocean mixed-layer heat budget dynamics with adaptive deep learning techniques. The proposed framework introduces three methodological innovations. First, an adaptive sampling strategy grounded in Boltzmann distribution theory dynamically reallocates physical collocation points toward high-gradient regions based on historical loss patterns. Second, a residual-based adaptive weight update mechanism automatically modulates physical constraint contributions across spatially heterogeneous regions during training. Third, a Bayesian optimization framework employing Gaussian process surrogates systematically balances physical constraints against data fitting objectives. The framework is validated through comprehensive experiments in the Mediterranean Sea using multi-source reanalysis data spanning over two decades. Results demonstrate that HW-OPINN achieves superior performance in sea surface temperature (SST) prediction, with a test MSE of 0.009138 and RMSE of 0.095595, representing improvements of 43.9% and 25.1%, respectively, compared to the ConvLSTM baseline (MSE: 0.016275, RMSE: 0.127575), and 44.8% and 25.7% improvements over standard PINN (MSE: 0.016550, RMSE: 0.128661). Based on the predicted SST fields, the model successfully reproduces the spatial heterogeneity of key MHW characteristics, including event frequency, duration, and intensity distributions, demonstrating its effectiveness for downstream MHW detection and analysis. Full article

The residual current is the ocean current after the tidal component has been removed. Understanding the spatiotemporal distribution characteristics of sea surface residual currents is key to revealing the local current field evolution and typical physical oceanographic processes. The Taiwan Strait is in the East Asian monsoon region, where residual currents are significantly influenced by monsoons during periods of high wind speeds. However, the characteristics and dynamic mechanisms of residual currents under low wind speed conditions (≤5 m/s) remain unclear. Based on high-frequency surface wave radar current data and wind field reanalysis data, this study analyzed the characteristics of residual currents in the southwestern Taiwan Strait under low wind speed conditions, focusing on two orthogonal directions: cross-shore and along-shore. During these periods, residual currents exhibit counter-wind current characteristics. These currents cross the Taiwan Bank and generate wave signals with wavelengths ranging from 35.6 km to 65.8 km and durations of 6 to 12 h in the Xiapeng Depression area. These fluctuations are triggered by the combined timing of low winds and nonlinear current–topography interactions. In terms of dynamic mechanisms, the Coriolis force term and the acceleration term dominate the momentum equations in both two orthogonal directions, indicating that the current field is in a non-steady inertial adjustment phase during this period. Furthermore, this study constructs a two-layer ocean model of rotationally modified gravity waves to analyze the influences of topography, oceanic stratification, and steady current velocity on the characteristics of residual current fluctuations under low wind speed conditions. The theoretical model yields spatial scales that closely match the observed wavelength characteristics. Full article

Marine renewable energy could support Costa Rica’s decarbonization pathway, but its offshore resource base and enabling conditions remain poorly characterized in the body of knowledge. This study provides the first integrated assessment of marine energy resources, grid integration opportunities, and governance challenges in Costa Rica. A meta-analysis of 76 technical, legal, and policy sources is combined with qualitative doctrinal analysis, GIS-based multi-criteria evaluation for Ocean Thermal Energy Conversion (OTEC), and satellite and reanalysis data for winds, waves, currents, and sea surface temperature to estimate power densities and extractable energy. Results show a contrast between the Pacific and Caribbean coasts. For instance, on the Northern Pacific coast, there are strong Papagayo winds, and persistent swells yield high offshore wind and wave energy potentials, with technical offshore wind resources of around 14.4 GW and Pacific wave power frequently exceeding 20–25 kW/m with relatively low seasonal variability. Furthermore, twelve OTEC-suitable zones are identified with two priority areas in the southern Pacific that combine steep bathymetry and strong thermal gradients with limited environmental conflicts, but they overlap with sensitive conservation and Indigenous territories. Current energy potential is more localized and modest in the Caribbean coast. The analysis highlights major infrastructural, legal, and social barriers but concludes that marine energy can play a pivotal role in diversifying Costa Rica’s renewable-dominated electricity market. Full article

Accurate identification of hydrodynamic parameters is essential for high-fidelity modeling and control of unmanned underwater vehicles (UUVs). Compared with towing tank experiments and computational fluid dynamics simulations, system identification based on free-running trial data offers a cost-effective and scalable alternative. However, in real ocean environments, unmodeled lumped disturbances—such as shear currents, stratification-induced buoyancy variations, and wave-induced drift forces—strongly couple with the vehicle’s intrinsic dynamics. Conventional least-squares estimators and physics-informed neural networks tend to absorb environmental effects into the physical parameters, leading to physically inconsistent estimates. To address this challenge, this paper proposes a physics-guided hybrid network (PG-HyNet) with input-domain structural decoupling. The architecture explicitly separates the intrinsic rigid-body dynamics from spatially varying environmental disturbances by assigning dynamics-related states to a physics-constrained branch and position-dependent variables to a residual disturbance branch. A staged training strategy is introduced to stabilize identification and suppress parameter drift during optimization. The framework is validated using high-fidelity simulations incorporating shear currents, density stratification, and wave drift effects, as well as real-world lake trial data. The results demonstrate that PG-HyNet significantly improves robustness against disturbance-induced parameter compensation, enabling physically consistent hydrodynamic parameter recovery while accurately capturing spatially varying environmental disturbance effects. Full article