This study covers the structural optimization of vertical axis wind turbines (VAWTs) that can operate reliably for long periods of time in marine environments, as well as simulation analysis to evaluate their fatigue and strain resistance. Due to the nature of the marine environment, strong wind speeds and constant wave loads are applied, and VAWTs are likely to suffer from fatigue build-up and deformation problems in the long term. In this study, detailed numerical simulations were performed using ANSYS software (2024 R2) to analyze the effects of different airfoil shapes, material choices, tip speed ratios (TSRs), and foundation types on the turbine’s stress distribution and fatigue resistance. The results showed that NACA 0030 airfoil, composite steel, and single-pile foundation performed best under TSR 1.8 conditions, with the potential to reduce strain by approximately 30% and fatigue damage by approximately 25% compared to conventional structures. With this optimized combination, it was found that maintenance costs could be significantly reduced while maintaining structural stability at sea. These results could make an important contribution to the economical and durable design of VAWTs in the future.

The global transition to renewables in response to climate change has largely been supported by the expansion of wind power capacity and improvements in turbine technology. This is being made possible mainly due to improvements in the design of highly efficient turbines exceeding a 10 MW rated power. Apart from power efficiency, wind turbines must withstand the mechanical stress caused by wind–hydro conditions. Such comprehensive structural analysis has rarely been performed previously, especially for large-scale wind turbines under real environmental conditions. The present work analyzes the energy production and structural performance of an NREL-IEA 15 MW wind turbine using measured wind and hydro data. First of all, an optimum operating range is determined in terms of the wind speed and blade pitch angle to maximize the power coefficient. Then, at this optimum range, a detailed breakdown of the forces and moments acting on different components of the wind turbine is presented. It was found that wind speeds of 9 to 12 m/s are best suited for this wind turbine, as the power coefficient is at its maximum and the mechanical loads on all components are at a minimum. The loads are at a minimum due to the optimized blade pitch angle. The bending force on a monopile foundation (fixed on the seabed) is found to be at a maximum and corresponds to nearly 2000 kN. The maximum blade force is nearly 700 kN, whereas on the tower it is almost 250 kN. The maximum force on the tower occurs at a point which is found to be undersea, whereas above-sea, the maximum force on the tower is nearly 20% less than the undersea maximum force. Finally, seasonal and annual energy production is also estimated using locally measured wind conditions.

Offshore wind energy plays a crucial role in achieving renewable energy targets, with OWFs facing unique environmental challenges that impact turbine performance and structural demands. This study develops an advanced optimization methodology to identify the most effective layout configurations for offshore wind farms (OWFs) with monopile foundations, focusing on enhancing structural integrity and energy generation efficiency. Using a multi-criteria optimization approach, the effects of wind turbine spacing, angular orientation, and height on energy yield and monopile loading were evaluated. Based on a seven-year dataset from the Ouido site in South Korea, where the mean wind speed is 6.95 m/s at a 150 m hub height, optimized configurations were determined. For average wind conditions, a turbine spacing of 250 m, a hub height of 148 m, and an orientation angle of 36.87° minimized wake losses and distributed structural loads effectively. Under rated wind speeds of 10.59 m/s, a spacing of 282 m, a hub height of 155 m, and an orientation angle of 45° further enhanced performance. These designs reduced wake interference by 25%, decreased monopile fatigue loads by 18%, and lowered the levelized cost of electricity (LCOE) by up to 15%. This study’s findings provide a robust framework for optimizing OWFs to increase energy yield, improve operational efficiency, and ensure economic viability.

To meet growing energy demands, offshore wind turbines (OWTs) with higher energy outputs are being developed, presenting increased challenges for their foundation design. Over the past decade, extensive research on the design optimization of OWT support structures has significantly reduced the anticipated costs of offshore wind farm development. Various design methods have been developed and applied in practice, each with its own advantages and limitations. In this study, 3D finite element (FE) modeling, validated against the measured response of a large-scale test monopile, is used to investigate the lateral load response of monopiles with different geometries and slenderness ratios in smaall and large displacements. The results are compared to the standard p–y method, and specific behavioral and design aspects of large-diameter monopiles, such as the moment contribution ratio from different resisting components and the minimum embedment length criteria, are evaluated and discussed. The results showed that the maximum and minimum differences between the 3D FE modeling and one-dimensional (1D) DNV p–y method are 41% and 11% for large displacements, and 32.5% and 13.3% for small displacements, respectively. As the slenderness ratio increases, the discrepancy between the finite element (FE) modeling results and the 1D DNV p–y method decreases, with an average difference of about 13% across all monopile diameters at an L/D ratio of 10, in both small and large displacements, indicating the reasonable accuracy of the 1D method for slenderness ratios of 10 and above. Among the three minimum embedment length criteria examined, the DNV recommended and vertical-tangent criteria offered shorter embedment lengths. The primary resisting moment across all slenderness ratios comes from the distributed lateral load along the monopile shaft (${\mathrm{M}\mathrm{C}\mathrm{R}}_{\mathrm{p}-\mathrm{y}}$), which increases as the L/D ratio increases.

A comprehensive investigation of the directional bending performance of a 4-leg jacket substructure, supporting a 3 MW offshore wind turbine, has been carried out in this study. The jacket substructure with a Pratt bracing system which is already installed in the southwest offshore wind farm in South Korea has been chosen as a reference support structure. A numerical model of the 3MW support structure (i.e., tower, transition piece, and jacket structure) is configured, and its structural performances are evaluated under the conditions of (1) extreme environmental loads (Env), (2) critical Design Load Cases (DLCs), and (3) a total of 288 combined load cases (CBs). For the case of Env (i.e., wind, wave, and current loads), loading directions varying from 0° to 360° at intervals of 15° are considered. The DLCs are provided from the 3 MW wind turbine manufacturer, in a 6 × 12 matrix format. The selected 4-leg jacket substructure in this study showed the smallest bending stiffness at the loading angles of 135° and 315° under the condition of Env, and at the loading angles between 105° and 150° under the CBs. From these results, critical bending directionality of the 4-leg jacket substructure is identified. This study also found that the effects of Env loads are not small compared to the total structural responses of the 4-leg jacket substructure which is supporting a 3 MW offshore wind turbine.

The Weibull algorithm is one of the most accurate tools for forecasting and estimating wind energy potential. Two main parameters of the Weibull algorithm are the ‘Weibull shape’ and ‘Weibull scale’ factors. There are six different numerical methods to estimate the two Weibull parameters. These six methods are the empirical method of Justus (method 1), the empirical method of Lysen (method 2), the maximum likelihood method (method 3), the modified maximum likelihood method (method 4), the energy pattern factor method (method 5) and the graphical method (method 6). Many commercial wind energy software programs use the Weibull algorithm, and these six methods are used to calculate the potential wind energy at a given site. However, their accuracy is rarely discussed, particularly regarding wind data height. For this purpose, wind data measured for a long period (six years) at real sites are introduced. The wind data sites are categorized into three levels, i.e., low, medium, and high, based on wind data measurement height. The analysis shows that methods 1 and 2 are the most accurate methods among all six methods at low and medium heights. The number of errors increases with the height of these two methods. Methods 3 and 4 are the most suitable options for larger heights, as these scenarios have minimal error. The present study’s findings can be used in various fields, e.g., wind energy forecasting and wind farm planning.