07/11/2022
By Sokny Long
The Francis College of Engineering, Department of Mechanical Engineering, invites you to attend a doctoral dissertation defense by Caleb Traylor on “Computational Investigation into the Aeroacoustics of Wind Turbine Blades for Structural Health Monitoring”.
Ph.D. Candidate: Caleb Traylor
Defense Date: Monday, July 25, 2022
Time: 9 to 11 a.m. EDT
Location: This will be a virtual defense via Zoom. Those interested in attending should contact the student (Caleb_Traylor@student.uml.edu) and the committee chair (Murat_Inalpolat@uml.edu) at least 24 hours prior to the defense to request access to the meeting.
Committee Chair(Advisor): Murat Inalpolat, Ph.D., Associate Professor, Department of Mechanical Engineering, University of Massachusetts Lowell
Committee Members:
- Christopher Niezrecki, Ph.D., Professor, Department of Mechanical Engineering, University of Massachusetts Lowell
- David J. Willis, Ph.D., Associate Professor, Department of Mechanical Engineering, University of Massachusetts Lowell
- Babak Moaveni, Ph.D., Professor, Department of Civil and Environmental Engineering, Tufts University
Brief Abstract:
Wind energy is one of the fastest growing types of energy generation due to decreasing costs and increased interest in clean energy. There is an opportunity for further improvement in cost efficiency through an operational wind turbine blade structural health monitoring (SHM) system. Blade repair and replacement costs can account for a significant portion of the maintenance expenses for a wind farm, so it is crucial to identify damage as early as possible to minimize the necessary repairs. To meet this need, an acoustics-based structural health monitoring technique for wind turbine blades has been proposed. When the blade surface is damaged, there is a change in acoustic transmission loss and thus a change in the blade-internal cavity acoustics. This technique monitors this internal cavity sound to identify damage and alert operators. In previous work, this approach has been investigated 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 that 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, two key components must be modeled. First, it is necessary to predict the aeroacoustic sound generated by the flow over the blade. In this research, two different approaches are taken for this. First, a reduced-order approach is applied to model the flow-generated sound over a healthy wind turbine blade as simple acoustic sources. Later, a high-order discontinuous Galerkin model is used to predict the damage-related aeroacoustics in a complementary investigation. The second component of the computational model for the SHM approach is the propagation of sound into and throughout the blade-internal cavity. The SHM approach is then evaluated for a particular blade using the predicted sound and its propagation. This computational approach is useful not only with regard to predicting the successful 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 performed to identify likely useful scenarios and reduce reliance on assumptions in the computational model. An investigation into strategic microphone placement on a utility-scale wind turbine blade is conducted to identify microphone locations that would optimize damage detection on a case study and construct a methodology for designing a system that can be applied to any wind turbine blade.
The results indicate likely success for the structural health monitoring approach. While detection rates can vary depending on the equipment used and criteria for damage detection, sensors in the first half of the blade were shown to be able to successfully identify damage near the blade tip. Detection rates and the range of potential microphone locations further increase when the damage itself contributes to the noise, which was shown to be the case when the damage is located in the transition or turbulent flow region. In all, this research demonstrates the success of the structural health monitoring approach, adds to the knowledge of the blade aeroacoustics problem, and establishes a computational modeling methodology that can be applied to future research.
All interested students and faculty members are invited to attend the online defense via remote access.