Wind turbine blade leading edge erosion reduces the lift and increases the drag of the blade airfoils. This occurrence, in turn, reduces turbine power and energy yield. This study focuses on the aerodynamic analysis of large and sparse erosion cavities, observed in intermediate to advanced erosion stages, whose size and surface pattern do not lend themselves to experimental and numerical analysis by means of distributed roughness models alone. Making use of three‐dimensional Navier‐Stokes computational fluid dynamics enhanced by laminar‐to‐turbulent transition modeling, and geometrically resolving individual erosion cavities, the study validates this simulation‐based approach for predicting the aerodynamics and performance loss of blade sections featuring the aforementioned erosion cavities against available experimental data. It is found that the considered cavities can trigger transition, indicating the necessity of both resolving their geometry in the simulations and also modeling distributed surface roughness, of typically lower level, as this latter affects the properties of boundary layers and, if sufficiently high, may trigger transition over the entire spanwise length affected. The energy yield loss of a utility‐scale turbine due to the considered erosion pattern is found to be between 2.1% and 2.6% using measured and computed force data of the nominal and eroded outboard blade airfoil. A parametric analysis of the cavity geometry suggests that the geometry of the cavity edge has a much larger impact on aerodynamic performance than the cavity depth.

No abstract

Wind turbine leading edge erosion is a complex installation site-dependent process that spoils the aerodynamic performance of wind turbine rotors. This gradual damage process often starts with the formation of pits and gouges leading ultimately to skin delamination. This study demonstrates the application of open source parametric CAD functionalities for the generation of blade geometries with leading edge erosion damage consisting of pits and gouges. This capability is key to the development of high-fidelity computational aerodynamics frameworks for both advancing knowledge on eroded blade aerodynamics, and quantifying energy losses due to erosion. The considered test case is an offshore 5 MW turbine featuring leading edge pit and gouge damage in the outboard part of its blades. The power and loads of the nominal and damaged turbines are determined by means of a blade element momentum theory code using airfoil force data obtained with 3D Navier-Stokes computational fluid dynamics. An annual energy loss between about 1 and 2.5 percent of the nominal annual energy yield is encountered for the considered leading edge damages. The benefits of adaptive power control strategies for mitigating erosion-induced energy losses are also highlighted.

Wind turbine blade deterioration issues have come to the attention of researchers and manufacturers due to the relevant impact they can have on the actual annual energy production (AEP). Research has shown how after prolonged exposure to hail, rain, insects or other abrasive particles, the outer surface of wind turbine blades deteriorates. This leads to increased surface roughness and material loss. The trailing edge (TE) of the blade is also often damaged during assembly and transportation according to industry veterans. This study aims at investigating the loss of AEP and efficiency of modern multi-MW wind turbines due to such issues using uncertainty quantification. Such an approach is justified by the stochastic and widely different environmental conditions in which wind turbines are installed. These cause uncertainties regarding the blade’s conditions. To this end, the test case selected for the study is the DTU 10 MW reference wind turbine (RWT), a modern reference turbine with a rated power of 10 MW. Blade damage is modelled through shape modification of the turbine’s airfoils. This is done with a purposely developed numerical tool. Lift and drag coefficients for the damaged airfoils are calculated using computational fluid dynamics. The resulting lift and drag coefficients are used in an aero-servo-elastic model of the wind turbine using NREL’s code OpenFAST. An arbitrary polynomial chaos expansion method is used to estimate the probability distributions of AEP and power output of the model when blade damage is present. Average AEP losses of around 1% are predicted mainly due to leading-edge blade damage. Results show that the proposed method is able to account for the uncertainties and to give more meaningful information with respect to the simulation of a single test case.

Wind turbines operate in challenging environmental conditions. In hot and dusty climates, blades are constantly exposed to abrasive particles that, according to many field reports, cause significant damages to the leading edge. On the other hand, in cold climates similar effects can be caused by prolonged exposure to hail and rain. Quantifying the effects of airfoil deterioration on modern multi-MW wind turbines is crucial to correctly schedule maintenance and to forecast the potential impact on productivity. Analyzing the impact of damage on fatigue and extreme loading is also important to improve the reliability and longevity of wind turbines. In this work, a blade erosion model is developed and calibrated using Computational Fluid Dynamics (CFD). The DTU 10MW Reference Wind Turbine is selected as the case study, as it is representative of the future generation wind turbines. Lift and Drag polars are generated using the developed model and a CFD numerical set-up. Power and torque coefficients are compared in idealized conditions at two wind speeds, i.e. the rated speed and one below it. Full aero-servo-elastic simulations of the turbine are conducted with the eroded polars using NREL's BEM-based code OpenFAST. Sixty-six ten-minute simulations are performed for each stage of airfoil damage, reproducing operating conditions specified by the IEC 61400-1 power production DLC-group, including wind shear, yaw misalignment and turbulence. Aeroelastic simulations are analyzed, showing maximum decreases in CP of about 12% as well as reductions in fatigue and extreme loading.