EAPSI: Damage detection and performance assessment of wind turbine blades

Zhao Yingjun, Davis CA

Investigators

Abstract

Current global competition for fossil fuels has motivated the prosperity of sustainable energy infrastructures such as wind turbine mills. Wind turbine blades, usually assembled by composite materials, are one of the most vulnerable structures due to their growing self-weight and harsh operational environment. Skyscraping maintenance cost spent on condition assessment through a turbine's service life has caused wind energy to be less cost-effective comparing to conventional resources. Reliable approaches are currently sought to assess turbine blade's structural performance without high expenditure. Considering that most structural failures are initiated from early material deteriorations such as cracks or corrosion, an in situ structural health monitoring system may be employed to alert early signs of material worn-outs, preventing catastrophic structural failures possibly taking place in the future. In collaboration with Dr. Yuan-Sen Yang at the National Taipei University of Technology in Taiwan, this projects aims to employ emerging damage sensing technologies to perform damage assessment of lab-scaled wind turbine blades. Dr. Yang's lab will provide unique dynamic testing facility for lab-scaled turbine blades, and expertise on dynamic characterization of turbine blade performance. A self-assembled, carbon nanotube-based nanocomposite material is employed as the strain sensor, whose damage sensing capability has been verified on composite material coupons. It will be customized to perform spatial damage detection for lab-scaled wind turbine blades. Structural dynamic responses of the same blades under rotational motion will be taken via the digital image correlation technique - a non-contact motion tracking method - through utilization of a high-speed video camera. A computational model based on both measurements will be established to simulate multidimensional damage aspects of a turbine blade under different operational conditions, thereby providing all-around performance assessment and predictions of the wind turbine's structural conditions. This NSF EAPSI award is funded in collaboration with the National Science Council of Taiwan.