Research Papers

The Impact of Ice Formation on Wind Turbine Performance and Aerodynamics

[+] Author and Article Information
S. Barber, Y. Wang, S. Jafari, N. Chokani, R. S. Abhari

Laboratory for Energy Conversion, ETH Zürich, ETH Zürich MLJ33, Sonneggstrasse 3, 8092 Zürich, Switzerlandbarbers@ethz.ch

J. Sol. Energy Eng 133(1), 011007 (Jan 28, 2011) (9 pages) doi:10.1115/1.4003187 History: Received December 03, 2009; Revised November 08, 2010; Published January 28, 2011; Online January 28, 2011

Wind energy is the world’s fastest growing source of electricity production; if this trend is to continue, sites that are plentiful in terms of wind velocity must be efficiently utilized. Many such sites are located in cold, wet regions such as the Swiss Alps, the Scandinavian coastline, and many areas of China and North America, where the predicted power curves can be of low accuracy, and the performance often deviates significantly from the expected performance. There are often prolonged shutdown and inefficient heating cycles, both of which may be unnecessary. Thus, further understanding of the effects of ice formation on wind turbine blades is required. Experimental and computational studies are undertaken to examine the effects of ice formation on wind turbine performance. The experiments are conducted on a dynamically scaled model in the wind turbine test facility at ETH Zurich. The central element of the facility is a water towing tank that enables full-scale nondimensional parameters to be more closely matched on a subscale model than in a wind tunnel. A novel technique is developed to yield accurate measurements of wind turbine performance, incorporating the use of a torquemeter with a series of systematic measurements. These measurements are complemented by predictions obtained using a commercial Reynolds-Averaged Navier–Stokes computational fluid dynamics code. The measured and predicted results show that icing typical of that found at the Guetsch Alpine Test Site (2330 m altitude) can reduce the power coefficient by up to 22% and the annual energy production (AEP) by up to 2%. Icing in the blade tip region, 95–100% blade span, has the most pronounced effect on the wind turbine’s performance. For wind turbines in more extreme icing conditions typical of those in Bern Jura, for example, icing can result in up to 17% losses in AEP. Icing at high altitude sites does not cause significant AEP losses, whereas icing at lower altitude sites can have a significant impact on AEP. Thus, the classification of icing is a key to the further development of prediction tools. It would be advantageous to tailor blade heating for prevention of ice buildup on the blade’s tip region. An “extreme” icing predictive tool for the project development of wind farms in regions that are highly susceptible to icing would be beneficial to wind energy developers.

Copyright © 2011 by American Society of Mechanical Engineers
Your Session has timed out. Please sign back in to continue.



Grahic Jump Location
Figure 1

(a) The blade design and (b) defined ice shapes

Grahic Jump Location
Figure 2

(a) Predicted ice profiles at three spanwise locations for conditions in Table 1 and (b) comparison of case A with a photograph from Guetsch

Grahic Jump Location
Figure 3

ETH Zurich subscale model wind turbine test facility: (a) photograph and (b) schematic diagram

Grahic Jump Location
Figure 4

Torque measurement method

Grahic Jump Location
Figure 5

Computational domain

Grahic Jump Location
Figure 6

Lift and drag forces acting on an airfoil element and the resulting axial and tangential force components (20)

Grahic Jump Location
Figure 7

Power coefficient versus tip speed ratio validation (two-bladed rotor)

Grahic Jump Location
Figure 8

Power coefficient versus tip speed ratio graph, three-bladed rotor with no ice, experiment

Grahic Jump Location
Figure 9

Power coefficient versus tip speed ratio graph for the rotor with ice shapes attached to blades: (a) cases A–C compared with no ice case and (b) cases D–F compared with no ice case

Grahic Jump Location
Figure 10

ΔCP for cases A–E for three tip speed ratios

Grahic Jump Location
Figure 11

Estimated power curves for the clean rotor and case A

Grahic Jump Location
Figure 12

ΔCP for cases A–E for CFD (tip speed ratio=6.0) and experiment (tip speed ratio=5.9)

Grahic Jump Location
Figure 13

Force components in (a) the z-direction and (b) the x-direction acting on 16 radial sections for the no ice case, case A, case C, and case F

Grahic Jump Location
Figure 14

Effective angle of attack at 16 radial sections for the no ice case, case A, case C, and case F

Grahic Jump Location
Figure 15

(a) Lift coefficient and (b) drag coefficient for 16 radial sections for the no ice case, case A, case C, and case F

Grahic Jump Location
Figure 16

Streamlines over the blade for cases A and F compared with no ice

Grahic Jump Location
Figure 17

Power measurements at Guetsch (gray dots) and the bin-averaged power curve (black dots) compared with the manufacturer’s power curve (black line)




Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In