Wind turbines operate under extreme loads and suffer from reliability issues attributed to manufacturing defects, fatigue, or extreme weather conditions. Blades are one of the most critical and vulnerable parts of modern wind turbines and are subject to blade edge splits, holes, or cracks that can lead to failures that can be catastrophic. In order to help identify damage in wind turbine blades, several approaches have been used in the wind industry, most of which require transducers to be mounted on the turbine blades, have not been effective, or require visual inspection. This award supports the fundamental research needed to understand the influence of damage on the acoustic radiation from and through the turbine blades to enable an efficient health monitoring framework. A significant outcome of this research is to improve the reliability of wind energy harvesting turbines thereby lowering energy costs, making their use more globally widespread, resulting in a reduction of greenhouse gases that will help to retard climate change. As part of this project, the UML team will collaborate with the KidWind Project to impact K-12 students who constitute the next generation of scientists and engineers. These outreach efforts will be delivered in collaboration with a team of middle and high school teachers motivating women, and underrepresented minority groups to become interested in science and engineering, while at the same time educating the general public.
This project will generate scientific knowledge advancing acoustic interrogation techniques, allowing them to be implemented for operational damage detection and identification. The research will achieve a fundamental understanding of the influence of damage severity and location on acoustic radiation from cavity structures. The causal relationship of both controlled deterministic and naturally excited aerodynamic sources of sound on acoustic cavity radiation and response will be investigated. The sensitivity of several detection and identification techniques for different damage types, geometries, locations, frequency ranges, and severity levels will be assessed and quantified. A variety of empirical and computational investigations will be made to investigate the influence of damage on source-path-receiver dynamics of cavity structures. An understanding of the influence of acoustic source and sensor placement within a cavity will help to minimize the instrumentation required while achieving maximum damage detection capability.
