Embedded Nanocomposite Sensors for Wind Turbine Damage Detection, Structural Health Monitoring, and Failure Prognosis
- Funding agency: National Science Foundation (NSF)
- Grant number: CMMI-HMSE 1200521
- Grant title: Collaborative Research: Integrated Wind Turbine Blade and Tower Health Monitoring and Failure Prognosis
- Collaborators: Valeria La Saponara; R. Andrew Swartz (Michigan Tech.)
- Graduate students: Shieh-Kung Huang; Sumit Gupta; Yisha Sun; Yingjun Zhao
The overarching goal of this research is to derive a probabilistic structural health monitoring and failure prognosis methodology for wind turbine structures. Specifically, the research activities will validate an in situ sensing technology for damage detection in composite materials, utilize experimental data for updating numerical models, and characterize structural demand for failure prognosis of critical elements within wind turbine structures. This study will begin by embedding thin films capable of spatial strain sensing in fiber-reinforced composites for detecting and localizing damage occurring at critical hotspots within the composite blade structure. Then, scaled wind turbine blades will be fabricated, tested in the lab under static and dynamic load configurations, and field tested using an existing wind turbine test bed at the University of California, Davis. Damage estimates will be used to update the resistance model of the structure based on the finite element method. Finally, failure prognosis is performed as a risk assessment step in which global vibrations of the structure are used to update aero-elastic analysis models and then used for estimating structural demand. This research will yield identified risk levels that will provide a rational basis for wind turbine maintenance, enhance structural safety, reduce downtimes, and lower cost of wind energy. The results will be useful for understanding wind turbine performance and the effects of blade damage coupled with varying input loads have on the demand on the entire structural system.
Wind turbines represent an important investment in sustainable energy production. Large and geographically remote wind farm facilities require robust and reliable information regarding the condition of individual turbine structures to assure efficient and safe operation. Successful completion of this project will lead to early-warning structural health monitoring systems that will warn operators when damage to turbine blades poses a risk of structural failure, and given the loading environment, quantifies failure risk in terms of a probability. This project integrates and advances disparate fields of composite structures, aero-elastic structure interaction theory, structural dynamics, and nanotechnology-based sensor design. Insights gained through the execution of this project will also be applicable for failure prognosis of other engineered systems subjected to random loadings. It will provide a link between damage detection and risk analysis that will provide a basis for decision making to protect structures and the public from danger. Educational broader impacts will also be achieved by integrating the design and construction of scaled wind turbines with the undergrad mechanical engineering Capstone design courses. Underrepresented, female, and economically disadvantaged students will also be recruited from various campus groups for participating and actively contributing to this project.