A Damage Assessment for Wind Turbine Blades from Heavy Atmospheric Particles

Fiore, Giovanni; Fujiwara, Gustavo E. C.; Selig, Michael S.

doi:10.2514/6.2015-1495

Cited by 12 publications

(16 citation statements)

References 40 publications

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“…22,25 BugFoil integrates a pre-existing particle trajectory code 33 and a customized version of XFOIL. 34 The local flowfield velocity components are obtained by querying the built-in potential flow routine of XFOIL from which the particle trajectory and impact location on the airfoil are computed.…”

Section: A Particle Equations Of Motionmentioning

confidence: 99%

“…However, the two particles that are most sensitive to the blade flowfield are sand grains and rain drops. 22,25 Both particles are associated with an erosion rate, hence allowing for an estimate of surface material loss. From an optimization perspective, such a parameter represents a direct feedback of the airfoil fitness with respect to particle erosion.…”

Section: Introductionmentioning

confidence: 99%

“…Other heavy particles such as hailstones are characterized by a trajectory that is fairly independent of the blade flowfield. 23,25 Little may be done from a shape optimization standpoint to minimize the damage to the panel due to hailstone strikes. However, the two particles that are most sensitive to the blade flowfield are sand grains and rain drops.…”

Section: Introductionmentioning

confidence: 99%

“…[22][23][24][25]32 From an aerodynamic standpoint it must be noted that there exists a correlation between the blade flowfield and the particle interaction with the blade surface. In particular, small and lightweight particles are more susceptible to the aerodynamic flowfield than bigger and heavier particles.…”

Section: Introductionmentioning

confidence: 99%

“…Throughout the world, another common damage scenario is represented by water-based particles, namely raindrops and hailstones. [23][24][25] When intense precipitation occurs, large raindrops may impact with the wind turbine blades and potentially promote internal panel delamination. 23,26 Even smaller raindrops increase the mechanical fatigue in the coating plastic materials 27,28 which translates into surface micro-cracking and a final erosive effect.…”

Section: Introductionmentioning

confidence: 99%

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Optimization of Wind Turbine Airfoils Subject to Particle Erosion

Fiore

Selig

2015

33rd AIAA Applied Aerodynamics Conference

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An optimization of wind turbine airfoils subject to particle erosion is investigated. The erosive damage to the surface was represented by sand grains colliding with the blade leading edge. The optimization was performed by using a genetic algorithm approach (GA). A newly-written GA code was coupled with a two-dimensional inviscid flowfield solver (XFOIL) and with a particle dynamics code (BugFoil). The chosen scheme of the GA was based on random tournament selection. Each geometry was evaluated through a figure of merit that represented the airfoil fitness to damage and to aerodynamic performance when the airfoil is damaged. Multiple approaches to the optimization were taken. The initial step of this study involved optimization through geometry perturbations of the leading edge by means of Bezier curves. It was found that the leading edge shape modifications of existing airfoils increase the fitness of the airfoils subject to erosion. The second approach involved a crossover of existing airfoil geometries along with leading edge shape mutations, in order to expand the design space. The fitness of such airfoils was improved with respect to the first approach. However, for both these approaches the airfoil aerodynamic performance was disregarded, as they were intended to give insight on the optimal airfoil shapes. The final step of the study made use of an inverse airfoil design method (PROFOIL) to widen the design space even further. In this approach the aerodynamic performance of the airfoil was evaluated, and the GA became a two-objective optimization process (maximum lift-to-drag ratio, and erosive figure of merit). It was observed that bulbous upper leading edges, and slanted, flat lower leading edges allowed for best erosion performance. Moreover, it was seen that the airfoils with a positive camber required a smaller angle of attack to operate at the same lift coefficient, hence reducing the erosive damage experienced for sand grain impacts. The airfoils obtained from the inverse design GA algorithm were characterized by a lift-to-drag ratio greater than 310 for a Reynolds number of 5.84×10 6 . Nomenclature AK= particle nondimensional mass c = airfoil chord length COE = Cost of Energy C d = airfoil drag coefficient C D = particle drag coefficient C l = airfoil lift coefficient d = particle diameter D = particle drag force E = erosion rate FOM = figure of merit GA = genetic algorithm AIAA Aviation LE = leading edge m = particle mass n = erosion rate velocity exponent r/R = blade section radial location Re = freestream Reynolds number t = time t/c = airfoil thickness-to-chord ratio U = chordwise flowfield velocity component V = chord-normal flowfield velocity component V ∞ = freestream velocity V imp = particle impact velocity V s = particle slip velocity x = chordwise coordinate y = chordwise-normal coordinate α = angle of attack α r = relative angle between flowfield and particle velocity θ = impact angle ω = weight used to compute FOM Subscripts and superscripts 0 = initial state l = lower limit P = partic...

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Section: A Particle Equations Of Motionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Optimization of Wind Turbine Airfoils Subject to Particle Erosion

Fiore

Selig

2015

33rd AIAA Applied Aerodynamics Conference

Self Cite

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Machine learnt prediction method for rain erosion damage on wind turbine blades

et al. 2021

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This paper proposes a paradigm shift in the numerical simulation approach to predict rain erosion damage on wind turbine blades, given the blade geometry, its coating material, and the atmospheric conditions (wind and rain) expected at the installation site. Contrary to what has been done so far, numerical simulations (flow field and particle tracking) are used not to study a specific (wind and rain) operating condition but to build a large database of possible operating conditions of the blade section. A machine learning algorithm, trained on this database, defines a prediction module that gives the feature of the impact pattern over the 2-D section, given the wind and rain flow. The advantage of this approach is that the prediction becomes much faster than using the standard simulations; thus, the study of a large set of variable operating conditions becomes possible. The module, coupled with an erosion model, is used to compute the erosion damage of the blade working on specific installation site. In this way, the variations of the flow conditions due to dynamic effects such as variable wind, wind turbulence, and turbine control can be also considered in the erosion computation. Here, we describe the method, the database creation, and the development of the prediction tool. Then, the method is applied to predict the erosion damage on a blade section of a reference wind turbine, after one year of operation in a rainy onshore site. Results are in good agreement with on field observations, showing the potential of the approach.

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WRF Modeling of Deep Convection and Hail for Wind Power Applications

Letson

Shepherd

Barthelmie

et al. 2020

Journal of Applied Meteorology and Climatology

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Deep convection and the related occurrence of hail, intense precipitation and wind gusts represent a hazard to a range of energy infrastructure including wind turbine blades. Wind turbine blade leading edge erosion (LEE) is caused by the impact of falling hydrometeors onto rotating wind turbine blades. It is a major source of wind turbine maintenance costs and energy losses from wind farms. In the United States Southern Great Plains (SGP), where there is widespread wind energy development, deep convection and hail events are common, increasing the potential for precipitation-driven LEE. A 25-day Weather Research and Forecasting (WRF) model simulation conducted at convection-permitting resolution and using a detailed microphysics scheme is carried out for the SGP to evaluate the effectiveness in modelling the wind and precipitation conditions relevant to LEE potential. WRF output for these properties is evaluated using radar observations of precipitation (including hail) and reflectivity, in situ wind speed measurements and wind power generation. This research demonstrates some skill for the primary drivers of LEE. Wind speeds, rainfall rates and precipitation totals show good agreement with observations. The occurrence of precipitation during power producing wind speeds is also shown to exhibit fidelity. Hail events frequently occur during periods when wind turbines are rotating and are especially important to LEE in the SGP. The presence of hail is modelled with a mean proportion correct of 0.77 and an odds ratio 4.55. Further research is needed to demonstrate sufficient model performance to be actionable for the wind energy industry and there is evidence for positive model bias in cloud reflectivity.

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A Damage Assessment for Wind Turbine Blades from Heavy Atmospheric Particles

Cited by 12 publications

References 40 publications

Optimization of Wind Turbine Airfoils Subject to Particle Erosion

Optimization of Wind Turbine Airfoils Subject to Particle Erosion

Machine learnt prediction method for rain erosion damage on wind turbine blades

WRF Modeling of Deep Convection and Hail for Wind Power Applications

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