Inspection and monitoring of wind turbine blade-embedded wave defects during fatigue testing

Niezrecki, Christopher; Avitabile, Peter; Chen, Julie; Sherwood, James; Lundstrom, Troy; LeBlanc, Bruce; Hughes, Scott; Desmond, Michael; Beattie, Alan; Rumsey, Mark; Klute, Sandra M.; Pedrazzani, Renee; Werlink, Rudy; Newman, John

doi:10.1177/1475921714532995

Inspection and monitoring of wind turbine blade-embedded wave defects during fatigue testing

Journal Article · Tue May 20 00:00:00 EDT 2014 · Structural Health Monitoring

DOI:https://doi.org/10.1177/1475921714532995· OSTI ID:1122363

Niezrecki, Christopher ^[1]; Avitabile, Peter ^[1]; Chen, Julie ^[1]; Sherwood, James ^[1]; Lundstrom, Troy ^[1]; LeBlanc, Bruce ^[1]; Hughes, Scott ^[2]; Desmond, Michael ^[2]; Beattie, Alan ^[3]; Rumsey, Mark ^[3]; Klute, Sandra M. ^[4]; Pedrazzani, Renee ^[4]; Werlink, Rudy ^[5]; Newman, John ^[6]

Univ. of Massachusetts, Lowell, MA (United States)
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Luna Innovations Incorporated, Blacksburg, VA (United States)
Kennedy Space Center, FL (United States)
Laser Technology Inc., Norristown, PA (United States)

The research we present in this article focuses on a 9-m CX-100 wind turbine blade, designed by a team led by Sandia National Laboratories and manufactured by TPI Composites Inc. The key difference between the 9-m blade and baseline CX-100 blades is that this blade contains fabric wave defects of controlled geometry inserted at specified locations along the blade length. The defect blade was tested at the National Wind Technology Center at the National Renewable Energy Laboratory using a schedule of cycles at increasing load level until failure was detected. Our researchers used digital image correlation, shearography, acoustic emission, fiber-optic strain sensing, thermal imaging, and piezoelectric sensing as structural health monitoring techniques. Furthermore, this article provides a comparison of the sensing results of these different structural health monitoring approaches to detect the defects and track the resultant damage from the initial fatigue cycle to final failure.

Research Organization:: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: AC04-94AL85000

OSTI ID:: 1122363

Alternate ID(s):: OSTI ID: 1166668

Report Number(s):: SAND--2013-10376J; 491460

Journal Information:: Structural Health Monitoring, Journal Name: Structural Health Monitoring Journal Issue: 6 Vol. 13; ISSN 1475-9217

Publisher:: SAGE PublicationsCopyright Statement

Country of Publication:: United States

Language:: English

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Comparison of nondestructive testing techniques for the inspection of wind turbine blades' spar caps Martin, Robert W.; Sabato, Alessandro; Schoenberg, Andrew Wind Energy, Vol. 21, Issue 11 https://doi.org/10.1002/we.2208	journal	May 2018
Prospective challenges in the experimentation of the rain erosion on the leading edge of wind turbine blades Bartolomé, Luis; Teuwen, Julie Wind Energy, Vol. 22, Issue 1 https://doi.org/10.1002/we.2272	journal	September 2018
Wind Turbine Blade Damage Detection Using Supervised Machine Learning Algorithms Regan, Taylor; Beale, Christopher; Inalpolat, Murat Journal of Vibration and Acoustics, Vol. 139, Issue 6 https://doi.org/10.1115/1.4036951	journal	August 2017
Vertical Axis Wind Turbine Aerodynamics: Summary and Review of Momentum Models Mohammed, Amin A.; Ouakad, Hassen M.; Sahin, Ahmet Z. Journal of Energy Resources Technology, Vol. 141, Issue 5 https://doi.org/10.1115/1.4042643	journal	February 2019
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Active acoustic damage detection of structural cavities using internal acoustic excitations Beale, Christopher; Inalpolat, Murat; Niezrecki, Christopher Structural Health Monitoring, Vol. 19, Issue 1 https://doi.org/10.1177/1475921719835761	journal	March 2019
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Damage mode identification of composite wind turbine blade under accelerated fatigue loads using acoustic emission and machine learning Liu, Pengfei; Xu, Dong; Li, Jingguo Structural Health Monitoring, Vol. 19, Issue 4 https://doi.org/10.1177/1475921719878259	journal	September 2019

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17 WIND ENERGY
42 ENGINEERING
composites
damage detection
defect
digital image correlation
fiber-optic sensing
shearography
wind turbine blade

Inspection and monitoring of wind turbine blade-embedded wave defects during fatigue testing

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