Research Papers

Design and Fatigue Performance of Large Utility-Scale Wind Turbine Blades

[+] Author and Article Information
Peter K. Fossum

e-mail: peterkal@stud.ntnu.no

Lars Frøyd

e-mail: lars.froyd@ntnu.no

Ole G. Dahlhaug

e-mail: ole.g.dahlhaug@ntnu.no
Norwegian University of Science
and Technology,
Høgskoleringen 1,
Trondheim 7491 Norway

Contributed by the Solar Energy Division of ASME for publication in the JOURNAL OF SOLAR ENERGY ENGINEERING. Manuscript received June 25, 2012; final manuscript received February 2, 2013; published online June 11, 2013. Assoc. Editor: Christian Masson.

J. Sol. Energy Eng 135(3), 031019 (Jun 11, 2013) (11 pages) Paper No: SOL-12-1163; doi: 10.1115/1.4023926 History: Received June 25, 2012; Revised February 18, 2013

Aeroelastic design and fatigue analysis of large utility-scale wind turbine blades have been performed to investigate the applicability of different types of materials in a fatigue environment. The blade designs used in the study are developed according to an iterative numerical design process for realistic wind turbine blades, and the software tool FAST is used for advanced aero-servo-elastic simulations. Elementary beam theory is used to calculate strain time series from these simulations, and the material fatigue is evaluated using established methods. Following wind turbine design standards, the fatigue evaluation is based on a turbulent wind load case. Fatigue damage is estimated based on 100% availability and a site-specific annual wind distribution. Rainflow cycle counting and Miner's sum for cumulative damage prediction is used together with constant life diagrams tailored to actual material S-N data. Material properties are based on 95% survival probability, 95% confidence level, and additional material safety factors to maintain conservative results. Fatigue performance is first evaluated for a baseline blade design of the 10 MW NOWITECH reference wind turbine. Results show that blade damage is dominated by tensile stresses due to poorer tensile fatigue characteristics of the shell glass fiber material. The interaction between turbulent wind and gravitational fluctuations is demonstrated to greatly influence the damage. The need for relevant S-N data to reliably predict fatigue damage accumulation and to avoid nonconservative conclusions is demonstrated. State-of-art wind turbine blade trends are discussed and different design varieties of the baseline blade are analyzed in a parametric study focusing on fatigue performance and material costs. It is observed that higher performance material is more favorable in the spar-cap construction of large blades which are designed for lower wind speeds.

Copyright © 2013 by ASME
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Fig. 1

Weibull wind distribution of the K13 Deep Water Site [16]

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Fig. 2

Blade cross-section seen from the root

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Fig. 3

Stress time series at low pressure side of a blade at Vhub = 15 m/s and I15 = 0.16 (class B turbulence)

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Fig. 4

95/95 S-N data for material QQ1

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Fig. 5

CLD of the QQ1 material. The lines are constructed with the method outlined in this paper.

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Fig. 6

GG2 and GH4 CLDs constructed with 95/95 strain S-N model

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Fig. 7

Location of fatigue damage of the 10 MW NOWITECH baseline blade design, shown as shaded areas. The darker shades means more fatigue damage. The upper blade shows tensile damage at pressure side for the GG2 material. The lower blade shows compressive damage at suction side for the GH4 material. The compressive damage is up-scaled to visualize damage locations.

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Fig. 8

Cross-sectional location of tensile damage (left) for GG2 and compressive damage (right) for GH4. Note that the bar lengths are considerably up-scaled in the right figure to visualize the damage.

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Fig. 9

Span-wise distributions of blade stiffness and annual mean bending moments

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Fig. 10

Comparison of the tailored GG2 CLD and a shifted linear CLD based on the GL 2010 standard

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Fig. 11

Fatigue damage locations on the baseline blade estimated using a GL 2010 shifted linear CLD

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Fig. 12

QQ1 CLDs constructed with S-N data for six R values versus three R-values

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Fig. 13

Weight trends of commercial wind turbine blades together with NOWITECH blade designs

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Fig. 14

GG2 CLD and WS* CLD (constructed with WindStrand HiPer-texTMR = 0.1 S-N data for T-T and T-C regions and QQ1 R = 10 S-N data for C-C)




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