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Assessing progressive failure in long wind turbine blades under quasi-static and cyclic loads

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  • Zuo, Yangjie
  • Montesano, John
  • Singh, Chandra Veer

Abstract

Predicting progressive failure and consequential loss in the load-bearing capability of large-scale composite wind blades is vital for accurately assessing their service life and maintenance. A physics-based multi-scale damage model describing progressive ply cracking and joint adhesive debonding in blades under both quasi-static and cyclic loading is presented. The complete structure of the blade was considered including the shell-spar adhesive joint and shell-root adhesive joint. For quasi-static loading, the geometrical transition region of the blade was observed as the critical ply crack damage region, which was in agreement with previous experimental results. The matrix micro-cracking damage was mainly caused by high gale wind speeds, and adhesive debonding ultimately initiated at the shell-spar joint. The blade tip deflection increased nonlinearly with increasing wind speeds, reaching 29.0% of the blade length at 19 m/s. For cyclic loading, sub-critical damage grew along the length of the blade with increasing cycles, gradually increasing the normal and shear stresses in the joint adhesive layer as the crack density increased, eventually leading to local shell-spar adhesive debonding. The simulation methodology presented here will be useful for assessing the durability and increasing the safety and accuracy of service life prediction of large-scale blade structures.

Suggested Citation

  • Zuo, Yangjie & Montesano, John & Singh, Chandra Veer, 2018. "Assessing progressive failure in long wind turbine blades under quasi-static and cyclic loads," Renewable Energy, Elsevier, vol. 119(C), pages 754-766.
    • Handle: RePEc:eee:renene:v:119:y:2018:i:c:p:754-766
    DOI: 10.1016/j.renene.2017.10.103
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    References listed on IDEAS

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    1. Jiménez, Alfredo Arcos & García Márquez, Fausto Pedro & Moraleda, Victoria Borja & Gómez Muñoz, Carlos Quiterio, 2019. "Linear and nonlinear features and machine learning for wind turbine blade ice detection and diagnosis," Renewable Energy, Elsevier, vol. 132(C), pages 1034-1048.
    2. Murray, Robynne E. & Roadman, Jason & Beach, Ryan, 2019. "Fusion joining of thermoplastic composite wind turbine blades: Lap-shear bond characterization," Renewable Energy, Elsevier, vol. 140(C), pages 501-512.

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