07/02/2021
By Murat Inalpolat

You are all invited to attend a doctoral dissertation defense in Energy Engineering on the "Computational Investigation into the Aeroacoustics of Wind Turbine Blades for Structural Health Monitoring.”

Ph.D. Candidate: Caleb Traylor
Date: Wednesday, July 7, 2021
Time: 9 to 11 a.m. (US EDT)
Location: This will be a virtual defense via Zoom. Those interested in attending should contact Caleb_Traylor@student.uml.edu and advisor Murat_Inalpolat@uml.edu at least 24 hours prior to the defense to request access to the meeting.

Committee Chair: Murat Inalpolat, Ph.D., Associate Professor, Mechanical Engineering, UMass Lowell (Advisor)

Dissertation Committee Members:

  • Christopher Niezrecki, Ph.D., Professor, Mechanical Engineering, UMass Lowell
  • David J. Willis, Ph.D., Associate Professor, Mechanical Engineering, UMass Lowell
  • Babak Moaveni, Ph.D., Professor, Civil and Environmental Engineering, Tufts University

Abstract:

Wind energy is one of the fastest growing renewable energy sources, and there is opportunity for improved cost efficiency with an operational wind turbine blade monitoring system. To meet this need, an acoustics-based structural health monitoring technique for wind turbine blades has been proposed based on a change in blade-internal cavity acoustics due to damage to the surface of the blade reducing acoustic transmission loss. In previous work, this has been studied primarily through experimental studies, which are crucial in demonstrating the effectiveness of the approach and identifying practical limitations and necessary improvements. However, experimental work is by nature limited in scope, so it is necessary to develop a complementary computational approach which enables large-scale sensitivity studies and customization to specific wind turbines.

In order to computationally investigate the effectiveness of the proposed structural health monitoring approach, there are two key components that must be modeled. First, it is necessary to predict the aeroacoustic sound generated by the flow over the blade. Thus, a significant portion of this research addresses this prediction and the modeling of sound as simple acoustic sources near the surface of the blade. Second, the propagation of sound into and throughout the blade cavity must be predicted. It is here that the SHM approach is evaluated for a particular blade, not only with regard to detection rate but also in selecting strategic locations for microphone placement. Both of these components are demonstrated first on a small airfoil section and then on a utility-scale wind turbine blade. Once this full process has been established, sensitivity studies are also performed to identify likely useful scenarios and reduce reliance on assumptions in the computational model. To conclude, an investigation into strategic microphone placement will be performed to identify microphone locations that would optimize damage detection on a case study and codify a methodology for designing a system that can be applied to any wind turbine blade.

The proposed work will be the first computational study into the flow-generated internal cavity acoustics of a wind turbine blade. This work builds off the experimental work that has already been done to present a predictive model of the structural health monitoring approach for the first time, enabling low-cost evaluation of potential systems and expanding the reach of future studies.