A computational investigation of airfoil aeroacoustics for structural health monitoring of wind turbine blades

Abstract

A generalized computational methodology for reduced order acoustic-structural coupled modeling of the aeroacoustics of a wind turbine blade is presented. This methodology is used to investigate the acoustic pressure distribution in and around airfoils to guide the development of a passive damage detection approach for structural health monitoring of wind turbine blades for the first time. The output of a k − ε turbulence model computational fluid dynamics simulation is used to calculate simple acoustic sources on the basis of model tuning with published experimental data. The methodology is then applied to a computational case study of a 0.3048-m chord NACA 0012 airfoil with two internal cavities, each with a microphone placed along the shear web. Five damage locations and four damage sizes are studied and compared with the healthy baseline case for three strategically selected acoustic frequencies: 1, 5, and 10 kHz. In 22 of the 36 cases in which the front cavity is damaged, the front cavity microphone measures an increase in sound pressure level (SPL) above 3 dB, while rear cavity damage only results in six out of 24 cases with a 3-dB increase in the rear cavity. The 1-and 5-kHz cases show a more consistent increase in SPL than the 10-kHz case, illustrating the spectral dependency of the model. The case study shows how passive acoustic detection could be used to identify blade damage, while providing a template for application of the methodology to investigate the feasibility of passive detection for any specific turbine blade. K E Y W O R D Sacoustic sensing, aeroacoustics, flow noise, passive damage detection, structural health monitoring, wind turbine blade | INTRODUCTIONThe amount of electricity generated by wind farms in the United States has grown from 34.4 × 10 6 MWh (0.83% of generated electricity) in 2007 to 25.4 × 10 7 MWh (6.3% of generated electricity) in 2017. 1 One quarter of electric power capacity additions in the United States in 2017 were wind farms, and $11 billion was invested in wind power, making it the third fastest growing source of electricity behind solar and natural gas. 2 The worldwide capacity reached 539 GW in 2017, an increase of 52.5 GW from 2016, and is expected to surpass 840 GW by the end of 2022. 3 As the wind energy industry continues to grow, it becomes increasingly important to reduce the levelized cost of energy (LCOE) for wind energy. The operation and maintenance (O&M) costs are a significant contributor to the overall LCOE and can account for between 11% and 30% LCOE of an onshore wind project with higher projected values for offshore projects. 4-6 Consequently, the LCOE can be mitigated by reducing the O&M costs.

Wind turbine blade trailing edge crack detection based on airfoil aerodynamic noise: An experimental study

“…The first approach [34,35] works by mounting audio speakers inside a wind turbine blade and measuring the sound radiated from the blade to identify damage within the structure (e.g., cracks, edge splits or holes. Another approach [36][37][38][39] is based on the use of microphones to detect trends, shifts, or spikes in the sound pressure level within the blade cavity. This approach mainly relies on the measurements of the acoustic pressure responses of the flow-induced noise within the blade cavity.…”

Optimal Temperature-Based Condition Monitoring System for Wind Turbines

“…For preventing such problems, optimal maintenance scheduling has to be applied using health monitoring on the key components [29]. A health monitoring system provides continuous evaluation of normal conditions of the WT key components and calculates an optimal maintenance scheduling [30]; as a result, it enhances the efficiency of the maintenance program and consequently increases the reliability of the system.…”

A generalized computational approach to predict high-frequency acoustic pressure response of cavity structures for structural health monitoring of wind turbine blades

“…In a previous paper by Traylor et al (2020), this reduced-order method (Croaker et al, 2011) was used to study the aeroacoustics of a small NACA 0012 cross-section only 0.46 m (1.5 ft.) in span. Simplified acoustic sources were calculated using the output of a turbulent flow simulation and then imported to computational finite element acoustic pressure analysis tool ANSYS Mechanical (2016a).…”

“…In that previous study (Traylor et al, 2020), the predictions were calculated using pressure acoustics with finite element analysis. This approach is useful for low frequencies and for relatively small geometries, as the acoustic pressure is solved at every location in the domain, but mesh requirements lead to a computational cost that can be prohibitive for high frequency analysis and large geometries (Savioja et al, 1999).…”