ABSTRACT This study examines how wave‐induced modulation affects the operational performance and structural dynamics of fixed‐bottom offshore wind turbines operating under a neutral atmospheric boundary layer profile. To capture these effects accurately while maintaining computational efficiency, unsteady RANS modelling techniques are employed. An actuator line method (ALM) is used to simulate the rotating blades, while a volume of fluid is used to generate the air–water interface and waves and to capture wake development. A 10‐MW wind turbine case study has been selected to study the impact of wave‐induced modulation on loads and wakes. Wake analysis reveals that wave modulation affects its development, resulting in a higher wake deficit and slower recovery. Specifically, four diameters downstream, the interaction leads to a 1.2% increase in wake deficit, with local increases up to 3.6% near the blade tips. These effects correspond to an average difference in wake deficit of 2.48% when wave modulation is taken into account and a consequent reduction in power of approximately 1%. In addition to wake effects, wave modulation leads to increased standard deviations across key aerodynamic variables, including blade loads and angle of attack, with the most pronounced changes occurring in the lower rotor area. The damage equivalent load (DEL) increases by 4% or up to 8% in the absence of the tower‐shadow effect. The ALM proves to be capable of capturing these interactions, suggesting its effectiveness in modelling realistic offshore conditions. These findings emphasise the importance of accounting for wave‐induced modulation in offshore wind turbine design and operation and suggest directions for future research, including wind‐wave misalignment and floating turbine configurations.

ABSTRACT Time series analysis of icing‐induced power loss in wind turbines pose several challenges: the response is bounded, serially dependent, intermittently missing, highly dispersed, and often inflated at a single value. We address these challenges with discrete‐time hidden Markov models for a discrete‐continuous process assumed to follow a mixture of state‐dependent zero‐inflated beta distributions. The framework allows covariates to influence either the transition probabilities or the distribution parameters. In a case study, we evaluate 12‐h‐ahead forecasts of icing‐related power loss. Compared with autoregressive and regression baselines, the proposed model achieves the highest predictive accuracy.

ABSTRACT There is an increased need for accurate validation of turbine models used by original equipment manufacturers to fine‐tune prototypes and predict power performance and maintenance needs while avoiding costly warranty payouts. Perhaps the most substantial source of uncertainty in typical wind turbine model validation procedures is the use of stochastically generated turbulent inflows using only 10‐min mean data from field measurements as inputs. As part of the Rotor Aerodynamics, Aeroelastics, and Wake (RAAW) campaign, we hub‐mounted a SpinnerLidar on a 2.8‐MW turbine for unobstructed and high‐fidelity measurement of the turbine's inflow. These data allowed us to create real‐time, measurement‐derived inflows that are compatible with OpenFAST. Herein, we compare the results of using these SpinnerLidar‐derived inflows to a standard approach using TurbSim‐generated inflows that use only 10‐min mean data from meteorological (met) tower anemometers as inputs. Results from multiple quantities of interest across 1645 10‐min bins of data are compared. Both inflow methods perform similarly on control related statistics, though results from Spinner inflows demonstrate higher correlation coefficients to the real turbine. Because Spinner inflows include veer and improved spatial matching of yaw misalignment, we see the largest differences in loads affected by these, such as the tower side–side moments. The Spinner inflows also allow us to identify that the tower side–side and tower top torque loads likely have model errors. Overall, we demonstrate that this method, or similar methods of turbine model validation through measurement‐derived inflows, shows considerable promise for identifying sources of error within the turbine model.

ABSTRACT Yaw engineering models are commonly used as add‐ons to the industrial blade element momentum (BEM) framework to improve load and power predictions by accounting for the skewed wake effect. However, existing yaw engineering models show noticeable limitations in accurately predicting the induced velocity distribution across the blade span. In this study, we employ a genetic symbolic regression （SR） approach to develop a new set of yaw engineering models for both the normal and tangential induced velocities of a static yawed wind turbine. The model regression is performed using simulation data from Reynolds‐averaged Navier–Stokes (RANS) simulations with an actuator line model (ALM) of the NREL 5‐MW wind turbine, covering a range of yaw angles () and thrust coefficients () over which the skewed wake effect is dominant. The regressed models are selected based on an optimal trade‐off between accuracy and complexity, with complexity constrained to remain comparable with Branlard's yaw engineering model. The selected models are subsequently verified using three unseen cases that span different operating conditions and wind turbine models. Verification is performed through a series of evaluations, including generalization performance tests, implementation within the BEM framework to assess their aerodynamic performances, and quantitative errors and loading analyses. The results demonstrate that the proposed models improve both the amplitude accuracy and azimuthal phase of induced velocities compared with the existing models of Coleman and Branlard, enabling it to accurately capture the phase of the peak aerodynamic forces across each annulus and to predict the nonrestoring yaw moment occurring in the inboard region of the turbine, which other models fail to reproduce.

ABSTRACT Cross‐flow turbines show promise for renewable energy generation from wind and tidal sources. The rotating reference frame of cross‐flow turbine blades results in virtual camber and incidence due to streamline curvature, altering the lift, drag, and pitching moment of the blades. Adding geometric camber is therefore likely to alter performance and loading; however, there is little consensus regarding the direction of camber that might be most favorable. This study compares 2% concave‐in and concave‐out cambered blades (NACA 2418) with symmetrical NACA 0018 foils for a turbine with a 0.49 chord‐to‐radius ratio. Experimental performance measurements are compared across a range of tip‐speeds, and particle image velocimetry is used to explore the in‐rotor flow evolution through the cycle. Concave‐out blades, which enhance virtual camber and lift in the power stroke, are found to exhibit sub‐optimal performance. In contrast, concave‐in cambered blades slightly improved symmetrical blade performance by enhancing downstream flow reattachment, more than compensating for reduced peak power generation. The difference between each cambered foil is seen to grow with increasing tip‐speed ratio. Moreover, these concave‐in blades reduce peak loading by 13%, which may prove critical in future designs, especially at high tip‐speed ratios. Exploration of the near‐blade flow fields suggests that the influence of geometric camber is nonlinear and that use of a simplistic summation of both geometric and virtual camber to account for camber effects may be overly simplistic. Despite this, corresponding validated simulations suggest that a small but positive net camber (geometric plus virtual) is optimal for this turbine.

ABSTRACT Wind energy has become one of the most promising new energy resources. In the construction of offshore wind farms, the cable accounts for about 10% of the total investment, and the optimization of the cable layout is necessary for reducing the total cost of the wind farm. In this paper, we proposed a novel algorithm for the optimization of the cable layout of wind farms, which can consider the relationship between the cost of cable and the amount of its connected wind turbines. Mathematically, it is a dynamic minimum spanning tree (DMST) algorithm that can consider the dynamic changes of the edge weights during the optimization process. This feature makes it suitable for optimizing the cable layout and cable type of wind farms simultaneously, and the wind turbines can be reasonably clustered in consideration of the current‐carrying limit of the cables. In this paper, this algorithm was used to optimize the cable layout of two offshore wind farms, respectively. In the first case of the wind farms, the proposed method saved 5.08%, 2.07%, and 8.61% of the cable investment compared to the Prim algorithm, the traditional DMST algorithm, and the ant colony algorithms, respectively. In the second case of the wind farms, the proposed method saved correspondingly 8.12%, 8.12%, and 7.98% of the cable investment, respectively.

ABSTRACT An experimental study of NACA0021 airfoil equipped with vortex generators (VGs) and Gurney flaps (GFs) was conducted to assess the effects of these devices on the aerodynamic airfoil performance, the information that is necessary for studying the self‐starting characteristics of vertical axis wind turbines (VAWTs). This is particularly important given current efforts to enhance the efficiency and lifetime of VAWTs. The pressure distribution on the airfoil was assessed for the entire angle of attack ( AoA ) range (i.e., 0° ≤ AoA < 360°). Four airfoil configurations were studied, i.e., (a) the baseline airfoil, (b) the airfoil with VGs, (c) the airfoil with a GF with a height of 0.014 chord ( c ) lengths, and (d) the airfoil with a GF of height 0.025 c . VGs yield a 33.3% increase in the AoA at which the flow reattaches and 16.3% increase in the maximum lift force coefficient c L . Configurations equipped with GFs exhibit 27.9% increase in the maximum c L and a concurrent increase in the lift‐to‐drag ratio.

ABSTRACT Wind turbine blades are highly engineered structures designed to face temperature extremes and high winds. However, erosion of the blade's leading edge and subsequent repair remains a significant and costly challenge for the wind energy industry. Repair of these leading edges can lead to large amounts of downtime for the turbine and significant operational inefficiencies. In this work, the strength of adhesion and healing ability of a commercially available vitrimer (Mallinda's VITRIMAX) was compared to that of a thermoplastic resin, which has previously been demonstrated in wind energy applications (Arkema's Elium) to evaluate their efficacy as surface coatings for wind turbine blades, particularly their leading edges. Vitrimers are a class of inherently reprocessable thermosets, and it was theorized that vitrimer‐based leading edge coatings could enable more robust and efficient wind turbine blades with decreased operational downtime and safer maintenance practices. It was found that the VITRIMAX adhered better to the wind blades' surfaces than both the manufacturer's paint and Elium, with increases in pull‐off strength of adhesion ranging from 24% to 83% above that of the original paint. Furthermore, the VITRIMAX adhered strongly to the underlying composite of each blade with strength of adhesion values increasing in ranges from 42% to 97% above that of the original paint. Finally, the vitrimer coating showed an 88% decrease in surface roughness compared to end‐of‐life blade materials, and initial healing demonstrations in which coatings were manually scratched and subsequently healed exhibited an ~84.5% decrease in scratch depths.