Structural damage identification in wind turbine blades using piezoelectric active sensing with ultrasonic validation
Abstract
This paper gives a brief overview of a new project at LANL in structural damage identification for wind turbines. This project makes use of modeling capabilities and sensing technology to understand realistic blade loading on large turbine blades, with the goal of developing the technology needed to automatically detect early damage. Several structural health monitoring (SHM) techniques using piezoelectric active materials are being investigated for the development of wireless, low power sensors that interrogate sections of the wind turbine blade using Lamb wave propagation data, frequency response functions (FRFs), and time-series analysis methods. The modeling and sensor research will be compared with extensive experimental testing, including wind tunnel experiments, load and fatigue tests, and ultrasonic scans - on small- to mid-scale turbine blades. Furthermore, this study will investigate the effect of local damage on the global response of the blade by monitoring low-frequency response changes.
- Authors:
-
- Los Alamos National Laboratory
- Publication Date:
- Research Org.:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 984850
- Report Number(s):
- LA-UR-10-00416; LA-UR-10-416
TRN: US201016%%1638
- DOE Contract Number:
- AC52-06NA25396
- Resource Type:
- Conference
- Resource Relation:
- Conference: ASNT Spring Research Conference ; March 22, 2010 ; Williamsburg, VA
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 42 ENGINEERING; LANL; MONITORING; RESPONSE FUNCTIONS; SIMULATION; TESTING; TIME-SERIES ANALYSIS; TURBINE BLADES; ULTRASONIC WAVES; VALIDATION; WAVE PROPAGATION; WIND TUNNELS; WIND TURBINES
Citation Formats
Claytor, Thomas N, Ammerman, Curtt N, Park, Gyu Hae, Farinholt, Kevin M, Farrar, Charles R, and Atterbury, Marie K. Structural damage identification in wind turbine blades using piezoelectric active sensing with ultrasonic validation. United States: N. p., 2010.
Web.
Claytor, Thomas N, Ammerman, Curtt N, Park, Gyu Hae, Farinholt, Kevin M, Farrar, Charles R, & Atterbury, Marie K. Structural damage identification in wind turbine blades using piezoelectric active sensing with ultrasonic validation. United States.
Claytor, Thomas N, Ammerman, Curtt N, Park, Gyu Hae, Farinholt, Kevin M, Farrar, Charles R, and Atterbury, Marie K. 2010.
"Structural damage identification in wind turbine blades using piezoelectric active sensing with ultrasonic validation". United States. https://www.osti.gov/servlets/purl/984850.
@article{osti_984850,
title = {Structural damage identification in wind turbine blades using piezoelectric active sensing with ultrasonic validation},
author = {Claytor, Thomas N and Ammerman, Curtt N and Park, Gyu Hae and Farinholt, Kevin M and Farrar, Charles R and Atterbury, Marie K},
abstractNote = {This paper gives a brief overview of a new project at LANL in structural damage identification for wind turbines. This project makes use of modeling capabilities and sensing technology to understand realistic blade loading on large turbine blades, with the goal of developing the technology needed to automatically detect early damage. Several structural health monitoring (SHM) techniques using piezoelectric active materials are being investigated for the development of wireless, low power sensors that interrogate sections of the wind turbine blade using Lamb wave propagation data, frequency response functions (FRFs), and time-series analysis methods. The modeling and sensor research will be compared with extensive experimental testing, including wind tunnel experiments, load and fatigue tests, and ultrasonic scans - on small- to mid-scale turbine blades. Furthermore, this study will investigate the effect of local damage on the global response of the blade by monitoring low-frequency response changes.},
doi = {},
url = {https://www.osti.gov/biblio/984850},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Fri Jan 01 00:00:00 EST 2010},
month = {Fri Jan 01 00:00:00 EST 2010}
}