Search Results (2,676)

Small-scale vertical-axis wind turbines (VAWTs) are increasingly essential for the “blue economy,” providing autonomous power to remote coastal communities, offshore platforms, and marine industries. However, the design of efficient Darrieus-type rotors is complicated by complex unsteady aerodynamics, particularly the phenomenon of dynamic stall. This study aims to establish and validate a cost-effective yet accurate mathematical modeling approach for simulating unsteady turbulent flow around a Darrieus H-rotor to support practical engineering applications. The research methodology integrates computational fluid dynamics (CFD) with physical experiments in a hydrodynamic channel. The numerical model utilizes the unsteady Reynolds-averaged Navier–Stokes (URANS) equations closed with the Strain-Adaptive Linear Spalart–Allmaras (SALSA) turbulence model, chosen for its efficiency in capturing flow separation. The system of initial equations was being devised relatively to an arbitrary curvilinear coordinate system. The pressure and velocity fields have been coordinated using the artificial compressibility method adapted to calculate non-stationary problems. Experimental verification was conducted in the GT-400 hydrodynamic tube using a three-bladed H-rotor model, where flow structures were visualized via the colored jet method at tip speed ratios λ ranging from 2 to 5 and Reynolds number 1470. The findings reveal that dynamic stall occurs over a significant portion of the blade trajectory, characterized by vortex generation at the leading edge and subsequent advection along the chord. Qualitative comparison demonstrates a high degree of correlation between the calculated vortex dynamics and physical flow spectra. These results confirm that the URANS-SALSA approach provides a rational compromise between computational cost and physical accuracy. Full article

This study presents a framework for developing, emulating, and validating offshore wind turbine models when proprietary blade designs are unavailable. The methodology addresses a critical industry challenge by demonstrating that aero-servo-hydro-elastic models reproducing the measured operational behavior can be constructed using only publicly available reference designs and operational measurements. An inverse design approach based on differential evolution optimization reconstructs blade aerodynamic characteristics from field data, enabling the creation of models that replicate operational behavior without requiring access to proprietary geometries. The framework incorporates statistical error characterization through machine learning techniques to predict simulation errors based on environmental and operational conditions. Validation against extensive field measurements from an operational offshore wind turbine demonstrates the effectiveness of the methodology. The machine-learning models predict the simulation-error distributions (bias and variability). The prediction fidelity is highest for the fore–aft response, which is thrust driven, and lower for the side–side response, for which several influencing factors remain unmodeled. This approach offers a practical pathway for model calibration and error prediction for offshore wind turbines, particularly when complete design documentation is unavailable. Full article

Ireland was an early offshore wind pioneer, with Arklow Bank Phase 1 commissioned in 2004 as one of the world’s first commercial offshore wind farms (OWFs). Despite this early start, offshore wind development (OWD) in Ireland remained limited for almost two decades. In recent years, however, the Government of Ireland has declared ambitious offshore renewable energy (ORE) targets, aiming to deliver up to 37 GW of capacity by 2050. One of the key constraints during this period has been the absence of a coherent and integrated marine planning and consenting framework capable of supporting large-scale ORE. This paper examines the evolution of Ireland’s ORE planning and consenting regime, tracing the transition from fragmented, largely “developer-led” arrangements toward a more coordinated and “state-led” framework. It reviews key legislative and policy developments, including the National Marine Planning Framework, the Maritime Area Planning (MAP) Act 2021, the establishment of the Maritime Area Regulatory Authority (MARA), and the introduction of Designated Maritime Area Plans (DMAPs), particularly the South Coast DMAP. The paper also situates Ireland’s recent reforms within selected leading European jurisdictions, highlighting persistent challenges related to governance coordination, permitting complexity, and regulatory sequencing in offshore wind deployment in Ireland. Full article

Offshore wind farms are increasingly and rapidly expanding due to their ability to harness strong and consistent wind energy resources. Large offshore wind farms are connected to mainland grids through High-Voltage Direct Current (HVDC) technology. However, offshore wind farms can often experience disturbances related to sudden wind changes, voltage drops/dips, faults related to converter switching, and unbalanced grid conditions which affect both the HVDC operation and wind turbine output. As a result, there is a growing need for more advanced and reliable modeling and monitoring tools. Moreover, traditional proportional-integral (PI) controllers are widely applied in wind turbines and HVDC systems due to their simple structure, easy implementation, and reliability. However, PI controllers perform poorly under non-linear and abnormal/fast-changing conditions, especially during sudden drops in wind power and grid faults. With this background, this paper first develops a digital twin model of an offshore wind farm that enables remote operation and monitoring of individual wind turbines. Also, an artificial intelligence (AI)-based controller, namely a fuzzy logic controller (FLC), is proposed to maintain transient stability of a full digital twin-based offshore wind farm connected to the HVDC grid under fault conditions. The effectiveness of the proposed FLC is demonstrated by considering a digital twin-enabled 700 MW offshore wind farm. The performance of the proposed FLC has been compared with that of the PI controller. Simulations performed by the MATLAB/Simulink software show that during the moderate voltage dip at 15 s, the PI controller experienced a 29.8% power reduction with a recovery time of approximately 9 s, whereas the FLC reduced the power drop to 23.1% and recovered within 6 s. During the severe converter disturbance at 15 s, the PI controller recorded a 36.9% power reduction compared to 23.4% for the FLC. Similarly, during the short-duration turbulence at 15 s, the PI controller exhibited a 36.73% power drop and recovered in approximately 7 s, while the FLC limited the power reduction to 19.17% and recovered within 5s. Overall, the FLC provided improved voltage stability, faster recovery, reduced oscillations, and superior fault ride-through capability compared with the conventional PI controller, demonstrating its effectiveness for digital twin-enabled offshore wind farm application. Full article

Aeolian sand bodies unrelated either to coastal barrier systems of Holocene or earlier age or to modern beaches have been identified along the central New South Wales coast of southeast Australia. Some of these deposits cap headlands or are located above high sea-cliffs. Others lie below modern sea-levels, whilst one substantial dune field extends 12 km inland. Many of these have previously been interpreted as Early Holocene cliff-top dunes, the product of the migration of beach sands up aeolian sand ramps at the foot of the sea-cliffs of the region and onto the cliff tops. The rising sea-levels of the Middle Holocene eroded the ramps and cut off the supply of sand to the dunes, allowing them to stabilise. But re-investigation shows that these dune fields accumulated at times of low Quaternary sea-levels, with a particle-size distribution suggestive of an inland rather than a coastal origin. We therefore propose an alternative model for the accumulation of these features. At times of low sea-level, sediments exposed on the inner shelf were reworked onto the adjacent coast by onshore winds, where they accumulated in locations unconnected to the modern or the earlier Holocene coastal aeolian sedimentary regime. This model challenges the conventional story that the dominant glacial maximum winds across southeastern Australia were from the west (and thus offshore). This pattern of sediment accumulation and its associated wind regime may have been more stable (continuing for over 30 000 years) and more long-lived (repeated through at least the last two glacial cycles) than has previously been believed. Although the cliff-top dune model has been widely applied, we question its suitability in its type location and suggest a more cautious approach to its application elsewhere. We argue that the products of the landward aeolian reworking of sediment exposed on the continental shelf have been overlooked, despite their potential for the preservation of long-term environmental records. Full article

To ensure the operational safety of the OCTABUOY platform used for offshore wind turbine installation in shallow waters, an eight-point symmetric mooring system was designed based on its octagonal structural configuration. The system provides high horizontal stiffness and balanced load distribution, enhancing stability under complex environmental conditions. Physical model tests were conducted under combined wind, wave, and current loading, considering multiple wave directions, environmental cases, and five draft conditions. The mooring tensions and six-degree-of-freedom motions were systematically analyzed to evaluate system performance and safety. Results show that the proposed mooring system effectively limits platform motions and maintains stable load-sharing characteristics. The minimum safety factor under the most unfavorable condition exceeds the design requirement. In addition, the system demonstrates good redundancy: after single-line failure, remaining mooring lines redistribute loads without progressive collapse. Draft and wave incident angle significantly influence peak tensions and motion responses, with smaller drafts and oblique wave directions producing relatively higher loads. The experimental results confirm the reliability and safety margin of the eight-point mooring system and provide practical guidance for the engineering application and operational assessment of the OCTABUOY platform in shallow-water wind installation projects. Full article

Accurate forecasting of offshore wind power is essential for the stability of power systems, yet it remains challenging due to the strong non-stationarity and complex multivariate coupling of meteorological data. To address the tendency of error accumulation in medium- and long-term predictions, this paper proposes a novel framework, termed ISSAVMD-TCN-SOFTS, which integrates adaptive signal decomposition with lightweight deep temporal modeling. Specifically, an improved sparrow search algorithm, enhanced by Lévy flight and sine–cosine modulation mechanisms, is introduced to adaptively optimize the parameters of variational mode decomposition (VMD). This optimization ensures the robust decomposition of highly non-stationary power series. Furthermore, the framework combines the capability of temporal convolutional networks (TCN) to extract multiscale local temporal features with the efficiency of the STAR module in SOFTS for modeling global channel dependencies. Experiments on multi-site, multi-horizon SCADA data from real offshore wind farms show that the proposed model reduces MAE and RMSE by 10–45% compared with mainstream linear models, recurrent neural networks, and Transformer-based models, and maintains high stability over extended forecasting horizons. The results confirm that the integration of adaptive decomposition and collaborative temporal-channel modeling provides an effective solution for the accurate and stable forecasting of offshore wind power. Full article

Wind turbines are increasingly deployed at larger scales and in harsher operating environments, leading to greater structural complexity, stronger load variability, and higher maintenance demands across both drivetrain and structural components. Reported field data indicate that gearboxes and bearings account for approximately 30–40% of total turbine downtime, while blade-related failures contribute roughly 20–25% of reported failure events, primarily through fatigue, delamination, leading-edge erosion, and lightning-induced defects. In parallel, large-scale and offshore turbines show increasing susceptibility to tower fatigue cracking, corrosion-assisted degradation, and flange joint bolt-preload loss under cyclic and environmental loading. This review provides a comprehensive applied-engineering synthesis of failure mechanisms, reliability challenges, and monitoring strategies for major wind turbine components, including gearboxes, bearings, blades, towers, and flange joints. A wide range of condition monitoring, structural health monitoring (SHM), and prognostics and health management (PHM) approaches is critically examined, including vibration analysis, acoustic emission, ultrasonic inspection, infrared thermography, impedance-based sensing, electromagnetic methods, machine vision, SCADA-based diagnostics, and artificial-intelligence-assisted fault classification. The review compares these techniques in terms of detectable damage types, spatial coverage, sensitivity, deployment practicality, and limitations under real operating conditions. In addition, statistical reliability methods and data-driven models are discussed to interpret failure trends and uncertainty. Recent AI-based studies have reported fault classification accuracies exceeding 90% under controlled or semi-controlled conditions; however, their field reliability remains constrained by data imbalance, domain shift, limited labeled failure datasets, model interpretability, and insufficient validation under realistic turbine operating environments. The main contribution of this review is an integrated applied synthesis that connects drivetrain and structural failure mechanisms with measurable monitoring indicators, diagnostic technologies, AI-enabled PHM limitations, and predictive-maintenance decision needs. The paper provides practical guidance for monitoring design, early fault detection, predictive maintenance, and long-term reliability improvement in next-generation wind turbine systems. Full article

Offshore wind energy offers abundant resources and significant potential for large-scale development. Efficient design of collection systems is critical to the economic viability of offshore wind farms (OWFs). This study proposes an upper–lower level topology optimization framework based on the Artificial Lemming Algorithm (ALA) to address the complexity arising from large numbers of wind turbines (WTs). At the upper level, wind turbines can be partitioned into different numbers of regions according to practical engineering requirements using the Radial Fuzzy C-Means (RFCM) clustering algorithm. At the lower level, the ALA is applied to optimize the collection system topology within each region, aiming to minimize total construction cost while satisfying operational constraints. A case study involving a 75-WT offshore wind farm is conducted. Comparative simulations against various heuristic algorithms including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Differential Evolution (DE) show that the proposed method achieves faster convergence, lower total costs and greater robustness. Specifically, the ALA reduces the best cost by 9.9% and improves average runtime by 28.5%, indicating its advantages in best-cost search and computational efficiency in the tested case. In addition, based on 10 independent runs, the ALA achieves the lowest median cost of $6684\times {10}^4$ CNY, with an interquartile range of 6593–$6813\times {10}^4$ CNY and a cost range of 6362–$7087\times {10}^4$ CNY. Overall, the proposed framework provides a practical optimization approach for obtaining low-cost feasible collection-system layouts in the studied offshore wind farm case. Full article

Offshore wind turbine blades in cold marine environments are exposed to low-temperature, high-humidity, and saline-droplet conditions, under which the melting behavior, interfacial sliding, and de-icing energy demand of saline ice differ from those of freshwater ice. Existing studies on combined phase-change coating–electrothermal de-icing have mainly focused on freshwater icing. Here, a glass-fiber-reinforced polymer (GFRP) NACA0018 airfoil was tested in a recirculating low-temperature icing wind tunnel to evaluate an n-tetradecane phase-change microcapsule/polyurethane (PCMS-PUR) coating combined with electrothermal heating at a salinity of 3%. Operating parameters, including heat flux density (8, 10, and 12 kW/m²), ambient temperature (−5, −10, and −15 °C), and incoming wind speed (3, 6, and 9 m/s), were systematically varied under a constant water flow rate (60 mL/min) and spray pressure (0.3 MPa) to characterize the evolution of ice morphology, temperature response, and de-icing energy consumption. During electrothermal de-icing, saline ice was more prone to interfacial softening and lubricating meltwater-layer formation, resulting in a dominant whole-block sliding detachment mode rather than gradual local melting. The PCMS-PUR coating further promoted interfacial melting and advanced ice destabilization through latent-heat release and thermal buffering. When the heat flux density increased from 8 to 12 kW/m², the de-icing energy consumption of the uncoated and coated blades decreased by 45.08% and 42.53%, respectively. The maximum energy-saving efficiency of the combined system reached 16.27% at 9 m/s. These findings clarify the de-icing behavior and energy-saving potential of combined phase-change coating–electrothermal systems under saline icing and provide guidance for the design of low-energy de-icing systems for offshore wind turbine blades. Full article

The deployment of offshore wind farms (OWFs) has increased impressively over the last decade. While a group of frontrunner countries has led early deployment, the offshore wind sector is expanding to new regions; the Thracian Sea represents a promising area for OWFs deployment due to its favorable wind and wave climate. The successful implementation of OWFs projects depends on a comprehensive understanding of local environmental conditions, with particular emphasis on complex wind–wave interactions quantification, as well as on robust and representative power performance evaluation. In the present paper, hourly environmental data spanning 29 years (1993–2021), including wind and wave parameters, are utilized to quantify joint probability distributions at selected four locations in the Thracian Sea. Corresponding environmental contours are derived and presented using a probabilistic model for given return period. The joint probability distributions of wind and wave conditions are estimated and the environmental contour surfaces for 50- and 100-year return periods are calculated and presented for generic use. Furthermore, the power production of an OWF comprising nine IEA 15 MW turbine units arranged in an orthogonal grid layout is assessed through a numerical model developed in an open access computational tool. The model accounts for key physical processes influencing OWF capacity performance, including wake interactions, atmospheric conditions, turbine control strategies, and layout effects. The results indicate a substantial value of annual energy production and capacity factor for different zones within Thracian Sea achieving a value of 526 GWh and 44%, respectively. The presented results provide practical guidance for OWFs development in the Thracian Sea and contributes to reducing uncertainty in early-stage project planning and future engineering studies. Full article

An empirical model is developed to predict significant wave height and significant wave period using only wind speed at 10 m height, fetch, and water depth. The model distinguishes between fetch-limited and duration-limited sea states within a conditional empirical framework that incorporates modified empirical exponents and corrections into classical wave formulations. Validation was performed using wind and wave data from the Global Forecast System at 26 coastal and offshore stations distributed across eleven different pilot seas and oceans worldwide, encompassing a broad spectrum of marine environments and climatic conditions. The proposed model was benchmarked against established empirical approaches. Results indicate a mean prediction error of 6.6% for the significant wave height and 9.6% for the significant wave period, substantially outperforming conventional formulations whose errors exceed 50% under comparable conditions. Unlike existing empirical models that are restricted to specific regions or sea-state conditions, the proposed model demonstrated strong predictive performance across diverse seas, oceans, and climatic conditions, enabling more reliable wave predictions in data-scarce and dynamically complex marine environments. The developed model was further applied to the Red Sea, where it successfully reproduced the spatial variability of significant wave height and wave period. From the results, it has been found that the developed model provides a practical and transferable tool for wave forecasting, coastal engineering, and offshore renewable energy applications. Full article

Mass–balance discrepancies exist between estimated land-based inputs and observed marine plastic inventories. While current global mass–balance models predominantly treat the open ocean as a passive terminal sink, they overlook the rapid expansion of offshore and deep-sea industrial frontiers. This review identifies offshore and deep-sea activities as active, in situ emission nodes of microplastics (MPs). Through a bibliometric analysis and numerical descriptions of studies, we document that direct offshore emissions are underrepresented in the current literature. By synthesizing these limited quantitative data, preliminary metrics indicate localized MP enrichment signals and elevated biological exposure near specific offshore infrastructures. Furthermore, plastics released directly into the marine environment bypass terrestrial weathering, undergoing distinct multiscale aging pathways governed by the complex interplay of wave-induced physical fragmentation bounded by critical size thresholds, UV-driven chemical photo-oxidation, and biological interactions. We conclude that refining global plastic budgets supports moving toward an integrated ocean-industrial framework. However, the synthesis remains constrained by data scarcity and high methodological heterogeneity across different environmental matrices. Future strategies must prioritize standardized in situ flux quantification and the incorporation of MP emission risks into offshore Environmental Impact Assessments. Full article

Offshore wind turbine blades are chronically exposed to complex marine environments with high humidity, salt spray, strong wind, waves, and intense radiation. Under such conditions, blade defects often exhibit small sizes, weak visual features, and heterogeneous visible infrared manifestations. Conventional single-sensor monitoring and empirically weighted fusion methods are insufficient for reliable defect diagnosis and risk grading. To address this problem, this paper proposes a cross-sensor consistency-guided dual-spectrum fusion framework, termed CG-DSF, for offshore wind turbine blade defect diagnosis and risk assessment. First, visible-light images and infrared thermal images are acquired by UAV-mounted imaging sensors, and sensor-specific branches are constructed to extract surface structural features and thermal anomaly responses. Second, visible and infrared features are aligned at the feature token level, and cross-sensor evidence is evaluated for spatial consistency, diagnostic semantic consistency, and anomaly consistency. A reliability-aware fusion strategy is then used to suppress low-quality or conflicting observations and construct a unified defect representation. Finally, a series of representative simulation case studies are carried out to comprehensively assess the overall performance and practical applicability of the constructed model. Experimental results reveal that the proposed framework possesses evident advantages in blade defect identification for offshore wind turbines, offering a feasible solution for advancing proactive and intelligent condition-based operation and maintenance of offshore wind assets in complex marine environments. Full article

Offshore rocket launches offer advantages such as unrestricted launch locations, convenient recovery, large effective payload capacity, and long satellite service life. However, due to the complex marine environment, variations in wind, waves, and currents significantly impact the dynamic performance of the launch platform. The study of rocket plume impact on platforms and the influence of wind, waves, and currents on their dynamic performance, as well as corresponding optimization measures, is of critical importance. This paper provides a comprehensive review of the dynamic issues concerning offshore launch platforms, covering theoretical analysis, numerical simulations, and experimental research. Firstly, the research on rocket plume impact loads is introduced. Subsequently, the research achievements in the hydrodynamic performance, structural dynamics, and motion prediction of launch platforms are summarized. On this basis, recent performance optimization methods for launch platforms are presented. Finally, unresolved issues and future research directions are highlighted. The aim is to offer valuable insights for further advancements in offshore rocket launches. Full article