Search Results (1,262)

This study introduces a novel methodology for estimating loading and fatigue damage in the blades of wind turbines, emphasizing non-Gaussian wind conditions’ impact. By calculating blade loading and fatigue using higher statistical moments of the irregular winds, the study demonstrates the significance of non-Gaussian effects on loading and fatigue predictions. A two-step methodology is developed to synthesize non-Gaussian wind processes, integrating the TurbSim (version 1.5) and Hermite moment model transformation methods. These wind time histories are then utilized in a fully coupled simulation of a floating wind turbine, integrating with a blade beam model. Preliminary analysis of wind thrust and the blade root bending moment indicates non-Gaussian effects on aerodynamic loading. Further analysis of fatigue reveals that fatigue hot spots vary along the blade surface, depending on short-term wind conditions and long-term wind distribution, with total fatigue life estimated by summing the fatigue damage at each potential hot spot. The probability density function of long-term wind process is estimated by fitting the Weibull distribution to measured buoy data. The results show that variations in long-term wind speed distributions lead to an average fatigue life difference of about 1.3 years (16%). The Gaussian wind model overestimates fatigue life by roughly 1.5 years (18%) compared to the non-Gaussian model. This highlights the importance of considering both long-term wind distributions and short-term wind characteristics for accurate fatigue assessment. The findings provide valuable insights for the design and operation of floating offshore wind turbines. Full article

A three-dimensional Computational Fluid Dynamics (CFD) model is developed to simulate an Ocean Brick System (OBS) placed in a wave tank. When stacked, ocean bricks are designed to withstand wave forces and ocean currents, enhancing the stability of offshore support structures, such as base supports of offshore wind turbines. In this study, the commercial software Ansys Fluent 2022 R1 is used for the simulations. A user-defined function (UDF) is developed to generate numerical waves that closely replicate those observed in experimental conditions. The numerical wave model is first validated against theoretical wave data, showing good agreement. The CFD model is then validated using experimental data from OBS tests conducted in the wave tank. Subsequently, the study investigates how OBS structures influence tidal waves—specifically, how they reduce the wave amplitude, and the pressure exerted on the bricks. Specifically, the wave amplitude reduction is more effective for waves with shorter wavelengths than for those with longer wavelengths, achieving up to a 70% reduction for waves with an amplitude of 0.785 m, a period of 5 s. Finally, a modification to the original brick geometry is proposed to further reduce wave amplitude and improve the stability of OBS platforms. For the same wave input, the modified brick geometry reduces wave energy effectively, achieving an 89.2% decrease in wave amplitude. Full article

As wind energy development continues to expand toward nearshore and deep-sea regions, enhancing the aerodynamic efficiency of vertical axis wind turbines (VAWTs) in complex marine environments has become a critical challenge. To address this, a composite flow control strategy combining leading-edge suction and trailing-edge gurney flap is proposed. A two-dimensional unsteady numerical simulation framework is established based on CFD and the four-equation Transition SST (TSST) transition model. The key control parameters, including the suction slot position and width as well as the gurney flap height and width, are systematically optimized through orthogonal experimental design. The aerodynamic performance under single (suction or gurney flap) and composite control schemes is comprehensively evaluated. Results show that leading-edge suction effectively delays flow separation, while the gurney flap improves aerodynamic characteristics in the downwind region. Their synergistic effect significantly suppresses blade load fluctuations and enhances the wake structure, thereby improving wind energy capture. Compared to all other configurations, including suction-only and gurney flap-only blades, the composite control blade achieves the most significant increase in power coefficient across the entire tip speed ratio range, with an average improvement of 67.24%, demonstrating superior aerodynamic stability and strong potential for offshore applications. Full article

In this study, an innovative passive stability-enhancing barge platform geometry is presented to improve the operational efficiency of floating offshore wind turbines (FOWTs) by mitigating platform motion caused by wave action. Barge-type FOWTs, which primarily rely on surface support, have received less attention in terms of geometric optimization. The proposed design incorporates skirts and a trapezoidal cross-sectional shape for the barge platforms.To achieve effective stability given cost-effect considerations, geometrical optimization was performed while maintaining the same mass as the original design. Positioning the skirt with a height-to-diameter ratio of 0.8 reduces platform movements considerably, decreasing the heave by approximately 20% and the pitch by up to 70% relative to the original design. In addition, the analysis demonstrated that increasing the moonpool area to approximately 400 m² (approximately 10% of the platform’s surface area) led to an additional reduction in the heave and pitch responses. A specific moonpool diameter saturation point value was identified to increase the stability of the floater. Finally, the platform configuration yielded consistently lower peak motions across different wave angles, demonstrating improved stability. Full article

Pile foundations are widely used to support offshore wind turbines. While the p-y curve method is adopted for analysis of pile–soil interactions in popular design specifications, including the American Petroleum Institute (API), its accuracy remains unassessed systematically and quantitatively. This study established a database by collecting 491 sets of pile p-y curves from multiple offshore wind turbine projects. The database was used to statistically evaluate the accuracy of the API p-y curve method for cohesionless soils. The model accuracy is represented by a model factor defined as the ratio of measured to predicted values of soil resistance around the pile. The results showed that accuracy assessment using the field data is significantly different from that using the laboratory model test data. On average, the API p-y curve method overestimates the true soil resistance in the field by about 30%, but underestimates that in the laboratory by about 8%. The dispersions in prediction accuracy of both cases are high. Correction terms are introduced to calibrate the current API p-y curves. The calibrated API methods were shown to be accurate in general and medium dispersive in prediction accuracy. Last, the model factors for the current and calibrated API methods were demonstrated to be lognormal random variables. Full article

Achieving the full potential of wind energy in the global renewable transition depends critically on enhancing the performance and reliability of polymer composite components. This review synthesizes recent advances from 2022 to 2025, including the development of next-generation hybrid composites and the application of high-fidelity computational methods—finite element analysis (FEA), computational fluid dynamics (CFD), and fluid–structure interaction (FSI)—to optimize structural integrity and aerodynamic performance. It also explores the transformative role of artificial intelligence (AI) in structural health monitoring (SHM) and the integration of Internet of Things (IoT) systems, which are becoming essential for predictive maintenance and lifecycle management. Special focus is given to harsh offshore environments, where polymer composites must withstand extreme wind and wave conditions. This review further addresses the growing importance of circular economy strategies for managing end-of-life composite blades. While innovations such as the geometric redesign of floating platforms and the aerodynamic refinement of blade components have yielded substantial gains—achieving up to a 30% mass reduction in PLA prototypes—more conservative optimizations of internal geometry configurations in GFRP blades provide only around 7% mass reduction. Nevertheless, persistent challenges related to polymer composite degradation and fatigue under severe weather conditions are driving the adoption of real-time hybrid predictive models. A bibliometric analysis of over 1000 publications confirms more than 25 percent annual growth in research across these interconnected areas. This review serves as a comprehensive reference for engineers and researchers, identifying three strategic frontiers that will shape the future of wind turbine blade technology: advanced composite materials, integrated computational modeling, and scalable recycling solutions. Full article

This work proposes a mid-fidelity load-mapping method for the structural analysis of semisubmersible floating offshore wind turbine substructures. Building on a hybrid linear potential flow and strip-theory dynamic analysis, the method maps hydrodynamic, current, hydrostatic, gravitational, inertial, mooring, and turbine loads onto a shell-based finite element (FE) model. The functionality of the proposed method is demonstrated through two case studies involving ultimate limit state analysis of a structurally reinforced OC4 DeepCwind semisubmersible platform. The analyses were conducted for two design load cases (DLCs) formulated to represent the metocean conditions at the Utsira Nord site, located off the coast of Norway. The accuracy of the mapped hydrostatic and potential flow loads is validated against dynamic simulation data, while a mesh convergence study is used to ensure reliable FE model performance. Results show that the highest von Mises stresses occur at unsupported heave-plate regions, internal stiffeners, and welded joints, with peak stresses safely below the steel’s yield strength. The more severe conditions of DLC 6.1 lead to a broader distribution of high-stress locations compared to DLC 1.6 but only a modest increase in peak stress. Full article

Ensuring the structural integrity of wind turbines is crucial for the sustainability of wind energy. A significant challenge remains in transitioning from mere defect detection to objective, scalable criticality assessment for prioritizing maintenance. In this work, we propose a novel comprehensive framework that leverages multispectral unmanned aerial vehicle (UAV) imagery and a novel standards-aligned Fuzzy Inference System to automate this task. Our contribution is validated on two open research-oriented datasets representing small on- and offshore machines: the public AQUADA-GO and Thermal WTB Inspection datasets. An ensemble of YOLOv8n models trained on fused RGB-thermal data achieves a mean Average Precision (mAP@.5) of 92.8% for detecting cracks, erosion, and thermal anomalies. The core novelty, a 27-rule Fuzzy Inference System derived from the IEC 61400-5 standard, translates quantitative defect parameters into a five-level criticality score. The system’s output demonstrates exceptional fidelity to expert assessments, achieving a mean absolute error of 0.14 and a Pearson correlation of 0.97. This work provides a transparent, repeatable, and engineering-grounded proof of concept, demonstrating a promising pathway toward predictive, condition-based maintenance strategies and supporting the economic viability of wind energy. Full article

Clarifying the changes in the flow field, trajectory, and range of particulate matter such as input detritus and feces of marine aquaculture in offshore wind farms is of great importance for optimizing the layout of cage culture, preventing water pollution, and promoting the integrated development of wind power and aquaculture. This study designed multiple scenarios based on the basic data of the Nanpeng Island wind farm. The flow field changes were simulated through a k-epsilon model based on the porous medium model, and the particle diffusion range and trajectory were simulated via the discrete phase model (DPM) and the MIKE 21 model. The results showed that flow velocities in the whole area, except in the region near the wind turbine, were unaffected by the monopile or jacket foundation. The center velocities of the cages decreased by 14.58% and 21.45%, respectively, when culture density increased from 12.5 to 20 kg/m³. In the case of one-way inflow, placing rafts upstream of the aquaculture area can effectively slow down the flow velocity, which is reduced by 45.2% and 32.3% at the inlet and center of the cage, respectively. In the case of the occurrence of unidirectional water flow, downstream raft frames, arranged in a triangular pattern, could align with the cage center axis. Under actual sea conditions, the raft frame could be arranged in an elliptical shape around the cage. The ratio of the length of its major axis to that of its minor axis is approximately 3:1. Full article

Shallow foundations are widely used in both terrestrial and marine environments, supporting critical structures such as buildings, offshore wind turbines, subsea platforms, and infrastructure in coastal zones, including piers, seawalls, and coastal defense systems. Accurately determining the soil bearing capacity for shallow foundations presents a significant challenge, as it necessitates considerable resources in terms of materials and testing equipment, as well as a substantial amount of time to perform the necessary evaluations. Consequently, our research was designed to approximate the forecasting of soil bearing capacity for shallow foundations using machine learning algorithms. In our research, four ensemble machine learning algorithms were employed for the prediction process, benefiting from previous experimental tests. Those four models were AdaBoost, Extreme Gradient Boosting (XGBoost), Gradient Boosting Regression Trees (GBRTs), and Light Gradient Boosting Machine (LightGBM). To enhance the model’s efficacy and identify the optimal hyperparameters, grid search was conducted in conjunction with k-fold cross-validation for each model. The models were evaluated using the R² value, MAE, and RMSE. After evaluation, the R² values were between 0.817 and 0.849, where the GBRT model predicted more accurately than other models in training, testing, and combined datasets. Moreover, variable importance was analyzed to check which parameter is more important. Foundation width was the most important parameter affecting the shallow foundation bearing capacity. The findings obtained from the refined machine learning approach were compared with the well-known empirical and modern machine learning equations. In the end, the study designed a web application that helps geotechnical engineers from all over the world determine the ultimate bearing capacity of shallow foundations. Full article

This study introduces an innovative deflection-controlled design method (DCM) for evaluating the bearing capacity of offshore mono-bucket foundations (MBFs) in clay, integrating advanced numerical simulations using FLAC3D with the modified cam clay (MCC) soil model. Departing from conventional ultimate bearing capacity approaches, the proposed method prioritizes serviceability limits by constraining foundation deflections to ensure optimal structural performance and turbine efficiency. A systematic investigation revealed that the MBF performance is predominantly governed by eccentricity ratios and soil–structure interaction, with vertical loads exhibiting a minimal impact in a serviceability limit state. Key findings include the following: (1) the rotation center (RC) stabilizes at approximately 0.8 times the skirt length (L) under loading; (2) thin, deep MBFs (aspect ratio > 1.0) exhibit up to a 30% higher bearing capacity compared to wide, shallow configurations; (3) increasing eccentricity ratios (ε = 0.31–1.54) enhance the moment capacity but reduce the allowable horizontal force by 15–20%; (4) compressive vertical loads (υ = −0.30) slightly reduce the normalized bending moments (ω) by 5–10% at low eccentricities (ε < 0.5). The numerical framework was rigorously validated against centrifuge test data, demonstrating high accuracy (error < 3%) in predicting foundation behavior. By bridging geotechnical mechanics with practical engineering requirements, this study provides a robust and efficient design framework for MBFs, offering significant improvements in reliability and cost-effectiveness for offshore wind turbine applications. The proposed DCM successfully guided the design of an MBF in southeastern China, demonstrating its efficacy for use with homogeneous clay. Full article

To meet the requirements of geometric nonlinear modeling and bending–torsion coupling analysis of long, flexible offshore blades, this paper develops a high-precision engineering simplified model based on the Absolute Nodal Coordinate Formulation (ANCF). The model considers nonlinear variations in linear density, stiffness, and aerodynamic center along the blade span and enables efficient computation of 3D nonlinear deformation using 1D beam elements. Material and structural function equations are established based on actual 2D airfoil sections, and the chord vector is obtained from leading and trailing edge coordinates to calculate the angle of attack and aerodynamic loads. Torsional stiffness data defined at the shear center is corrected to the mass center using the axis shift theorem, ensuring a unified principal axis model. The proposed model is employed to simulate the dynamic behavior of wind turbine blades under both shutdown and operating conditions, and the results are compared to those obtained from the commercial software Bladed. Under shutdown conditions, the blade tip deformation error in the y-direction remains within 5% when subjected only to gravity, and within 8% when wind loads are applied perpendicular to the rotor plane. Under operating conditions, although simplified aerodynamic calculations, structural nonlinearity, and material property deviations introduce greater discrepancies, the x-direction deformation error remains within 15% across different wind speeds. These results confirm that the model maintains reasonable accuracy in capturing blade deformation characteristics and can provide useful support for early-stage dynamic analysis. Full article

This paper presents a preliminary feasibility study for integrating hydro-pneumatic energy storage (HPES) with off-grid offshore wind turbines and green hydrogen production facilities—a concept termed HydroGenEration (HGE). This study compares the performance of this innovative concept system with an off-grid direct wind-to-hydrogen plant concept without energy storage, both under central Mediterranean wind conditions. Numerical simulations were conducted at high temporal resolution, capturing 10-min fluctuations of open field measured wind speeds at an equivalent offshore wind turbine (WT) hub height over a full 1-year, seasonal cycle. Key findings demonstrate that the HPES system of choice, namely the Floating Liquid Piston Accumulator with Sea Water under Compression (FLASC) system, significantly reduces Proton Exchange Membrane (PEM) electrolyser (PEMEL) On/Off cycling (with a 66% reduction in On/Off events), while maintaining hydrogen production levels, despite the integration of the energy storage system, which has a projected round-trip efficiency of 75%. The FLASC-integrated HGE solution also marginally reduces renewable energy curtailment by approximately 0.3% during the 12-month timeframe. Economic analysis reveals that while the FLASC HPES system does introduce an additional capital cost into the energy chain, it still yields substantial operational savings exceeding EUR 3 million annually through extended PEM electrolyser lifetime and improved operational efficiency. The Levelized Cost of Hydrogen (LCOH) for the FLASC-integrated HGE system, which is estimated to be EUR 18.83/kg, proves more economical than a direct wind-to-hydrogen approach with a levelized cost of EUR 21.09/kg of H₂ produced. This result was achieved through more efficient utilisation of wind energy interfaced with energy storage as it mitigated the natural intermittency of the wind and increased the lifecycle of the equipment, especially that of the PEM electrolysers. Three scenario models were created to project future costs. As electrolyser technologies advance, cost reductions would be expected, and this was one of the scenarios envisaged for the future. These scenarios reinforce the technical and economic viability of the HGE concept for offshore green hydrogen production, particularly in the Mediterranean, and in regions having similar moderate wind resources and deeper seas for offshore hybrid sustainable energy systems. Full article

Drag embedment anchors (DEAs) play a vital role in maintaining the stability and safety of offshore structures, including floating wind turbines, oil rigs, and marine renewable energy systems. Accurate prediction of anchor performance is essential for optimizing mooring system designs, reducing costs, and minimizing risks in challenging marine environments. By leveraging advanced machine learning techniques, this research provides innovative solutions to longstanding challenges in geotechnical engineering, paving the way for more efficient and reliable offshore operations. The findings contribute significantly to developing sustainable marine infrastructure while addressing the growing global demand for renewable energy solutions in coastal and deep-water environments. This current study evaluated tree-based machine learning algorithms, e.g., decision tree regression (DTR) and random forest regression (RFR), to predict the holding capacity and efficiency of DEAs in sand seabed. To train and validate the results of machine learning models, the K-fold cross-validation method, with K = 5, was utilized. Eleven geotechnical and geometric parameters, including sand friction angle (φ), fluke-shank angle (α), and anchor dimensions, were analyzed using 23 model configurations. Results demonstrated that RFR outperformed DTR, achieving the highest accuracy for capacity prediction (R = 0.985, RMSE = 344.577 KN) and for efficiency (R = 0.977, RMSE = 0.821 KN). Key findings revealed that soil strength dominated capacity, while fluke-shank angle critically influenced efficiency. Single-parameter models failed to capture complex soil-anchor interactions, underscoring the necessity of multivariate analysis. The ensemble approach of RFR provided superior generalization across diverse seabed conditions, maintaining errors within ±10% for capacity and ±5% for efficiency. Full article