Effect of Surface Contamination on the Performance of a Section of a Wind Turbine Blade

Soltani, Mohammad Reza; Birjandi, Amir Hossein

doi:10.2514/6.2007-1081

Cited by 22 publications

(34 citation statements)

References 2 publications

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“…FOM and (C l /C d ) max are written into a logfile, 8. The Pareto front is computed, and the parents for the following generation are selected amongst the individuals lying on the Pareto front, 9. The algorithm proceeds back to step 2) until the number of maximum generations is reached or convergence is obtained.…”

Section: B Genetic Optimization Through Airfoil Inverse Designmentioning

confidence: 99%

“…The originally smooth surface of the blades may change considerably, and the increased roughness will cause a drop in gross annual energy production (GAEP) and an increase in cost of energy (COE). [7][8][9][10][11][12][13] Modern trends in the wind turbine market have shown the benefits of offshore megawatt-scale wind turbine installations 14,15 in order to maximize GAEP while reducing COE. However, offshore locations are subject to more intense sand erosion than the majority of land installations.…”

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: B Genetic Optimization Through Airfoil Inverse Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimization of Wind Turbine Airfoils Subject to Particle Erosion

Fiore

Selig

2015

33rd AIAA Applied Aerodynamics Conference

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“…Soltani tested a 660 kW turbine airfoil using 500 µm roughness [13]. His approach differed from that of previous researchers in that he applied roughness over the entire upper surface rather than just at the leading-edge.…”

Section: Ia Turbine Control Schemesmentioning

confidence: 99%

Roughness Sensitivity Comparisons of Wind Turbine Blade Sections

Wilcox

2017

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One explanation for wind turbine power degradation is insect roughness. Historical studies on insect-induced power degradation have used simulation methods which are either unrepresentative of actual insect roughness or too costly or time-consuming to be applied to wide-scale testing. Furthermore, the role of airfoil geometry in determining the relations between insect impingement locations and roughness sensitivity has not been studied.To link the effects of airfoil geometry, insect impingement locations, and roughness sensitivity, a simulation code was written to determine representative insect collection patterns for different airfoil shapes. Insect collection pattern data was then used to simulate roughness on an NREL S814 airfoil that was tested in a wind tunnel at Reynolds numbers between 1.6 × 10 6 and 4.0 × 10 6 . Results are compared to previous tests of a NACA 63 3 -418 airfoil.Increasing roughness height and density results in decreased maximum lift, lift curve slope, and lift-to-drag ratio. Increasing roughness height, density, or Reynolds number results in earlier bypass transition, with critical roughness Reynolds numbers lying within the historical range. Increased roughness sensitivity on the 25% thick NREL S814 is observed compared to the 18% thick NACA 63 3 -418.Blade-element-momentum analysis was used to calculate annual energy production losses of 4.9% and 6.8% for a NACA 63 3 -418 turbine and an NREL S814 turbine, respectively, operating with 200 µm roughness. These compare well to historical field measurements. iii ACKNOWLEDGEMENTS

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“…However, increasing mitigates these undesirable impacts. Soltani, Birjandi, & Seddighi Moorani (2011) experimentally proved that zigzag and strip tape roughness patterns have less effect on flow characteristics than that of distributed contamination. To summarize, all of the studies have been revealed that existence of roughness for any reason decreases the aerodynamic efficiency.…”

Section: Introductionmentioning

confidence: 98%

The Effects of Different Roughness Configurations on Aerodynamic Performance of Wind Turbine Airfoil and Blade

Jafari

Djavareshkian

Feshalami

2017

Int. J. Renew. Energy Dev.

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ABSTRACT. In this research, viscous and turbulent flow is simulated numerically on an E387 airfoil as well as on a turbine blade. The main objective of this paper is to investigate various configurations of roughness to find a solution in order to mitigate roughness destructive impacts. Hence, the sand grain roughness is distributed uniformly along pressure side, suction side and both sides during the manufacturing process. Navier-Stokes equations are discretized by the finite volume method and are solved by SIMPLE algorithm in the OpenFOAM software which is open source. Results indicated that in contrast with previous studies, the roughness will be useful if it is applied on only pressure side of the airfoil. In this condition, the lift coefficient is increased to 8.62% and 1.2% compare to the airfoil with rough and smooth sides, respectively. However, in 3-D simulation, the lift coefficient of the blade with pressure surface roughness is less than smooth blade, but still its destructive impacts are much less than of both surfaces roughness and suction surface roughness. Therefore, it can be deduced that in order to reveal the influence of roughness, the simulation must be accomplished in three dimensions.

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Effect of Surface Contamination on the Performance of a Section of a Wind Turbine Blade

Cited by 22 publications

References 2 publications

Optimization of Wind Turbine Airfoils Subject to Particle Erosion

Optimization of Wind Turbine Airfoils Subject to Particle Erosion

Roughness Sensitivity Comparisons of Wind Turbine Blade Sections

The Effects of Different Roughness Configurations on Aerodynamic Performance of Wind Turbine Airfoil and Blade

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