07/24/2026
By Marley O'Neil

The Francis College of Engineering, Department of Mechanical Engineering, invites you to attend a master's thesis defense by Christopher Lowell titled: "The Impact of Operational Blade Angles on Wind Turbine Blade Aeroacoustics."

Wednesday, August 5, 2026
11 a.m. to 12:30 p.m.
Southwick 240

Committee:

  • Advisor: Murat Inalpolat, Professor, Mechanical Engineering, University of Massachusetts Lowell
  • Christopher Niezrecki, Professor, Mechanical Engineering, University of Massachusetts Lowell
  • Yan Luo, Professor, Professor, Electrical and Computer Engineering/Robotics, University of Massachusetts Lowell

Abstract:
To maintain continuous demand for being an attractive option for new energy generation projects, wind farms must deliver the best financial returns, fastest setup times, and smoothest grid connection, thereby keeping the levelized cost of energy (LCOE) competitive with other energy sources. Operation and maintenance costs contribute significantly to the overall cost of implementing wind assets and must be tightly controlled so they can continue to fall. Blade damage is one of the most common causes of failure on wind turbines, and one of the costliest to repair. To date, there is no reliable blade monitoring system with a proven track record and thus can help evaluate the health of the blades during turbine operation. Consequently, a robust wind turbine blade monitoring system that operates in-situ can help continuously interrogate and provide critical blade structural health information back to wind farm operators enabling their planned-corrective action and can offer significant financial benefit to wind farm owners and operators by converting unscheduled maintenances into scheduled ones, thereby reducing the LCOE. A passive acoustic-based wind turbine blade structural health monitoring system can fulfill these needs at a low-cost point by measuring the aeroacoustic sound generated by the wind turbine blades in operation, promptly processing this data for damage indicators and proactively inform the operators. This requires a complete understanding on the baseline aeroacoustics generated by the blades, which has been hindered by the other neighboring sound sources on or around the wind turbines. While investigations into the aeroacoustics generated by airflow over airfoil sections and small-scale wind turbine blades exist, there have been relatively few investigations into the aeroacoustics of relevant large wind turbine blades in operation.

Moreover, though modeling- and measurement-based data on aeroacoustics of both healthy and damaged wind turbine blades have been documented in the existing literature, no acoustic measurements were made at the source. Most of the prior investigations in the field are limited to measurements made in the far-field and cannot capture baseline aeroacoustic noise generated by each individual blade. To enable accurately informed, robustly performing acoustics-based SHM systems, the baseline aeroacoustic sound generated during normal operation of utility-scale wind turbines must be experimentally quantified and carefully interpreted. In this work, analytical approaches and signal processing techniques have been implemented enabling experimental quantification of wind turbine blade baseline aeroacoustics by isolating it from the rest of the acoustic sources under the impact of varying blade angles in operation including the azimuth, pitch and yaw angles. 

The acoustic contribution from all individual blades of two representative utility-scale wind turbines are experimentally quantified at the source during normal operation using the techniques developed and implemented in this thesis. Through the continual development of the UMass Lowell Blade Acoustic Monitoring System (UML BAMS), the acoustic pressure recorded from inside of the blade cavities during an extended field test is used to separate the contribution from the blade angles from the overall acoustic levels. The UML BAMS consist of multi-modal sensor nodes capable of measuring inertial data, audio-level acoustics as well as local temperature and wirelessly transmitting the data for processing. A multivariate statistical model is developed, where the output of this model is used to decompose the blade angles and measure the change in sound pressure level (ΔSPL) across different operating conditions revealing the impact of operational blade angles. The outcomes of the investigation are expected to impact not only monitoring and inspection of wind turbine blades, but also their design and operation under different environmental conditions.