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.
As the use of wind power continues to increase globally, the need for improved structural health monitoring systems for wind turbine blades increases as well. Acoustics-based methods are deemed promising in this endeavor, as they are non-contact, nondestructive and enabling distributed sensing. To systematically design an acoustics-based blade health monitoring system, a representative computational study using a NACA 0012 airfoil was conducted to examine the impact of damage location, damage size, acoustic source location, and microphone placement on the detection rate. Structural-acoustic coupled simulations were performed using a commercially available finite element based tool. Results indicated that sound pressure levels (SPL) did not increase significantly when the damage was not near the source location, indicating that using a change in SPL to identify damage may not be the best method. However, spatial variability of the change in SPL in the blade’s internal cavity was found to be a strong indicator of damage, resulting in a detection rate of 81–92% depending on the damage size. Based on the computationally obtained results, the recommendation is for further investigation into the spatial variation of sound inside the blade cavity using 2–3 microphones per cavity to identify damage, when feasible, in addition to testing the effectiveness of a one microphone per cavity design with different blade geometries. The optimal positions of these microphones and the reliability of this method will continue to be investigated in future work.
This paper details the development of a generalized computational approach that enables prediction of cavity-internal sound pressure distribution due to flow-generated noise at high frequencies. The outcomes of this research is of particular interest for development of an acoustics-based structural health monitoring system for wind turbine blades. The methodology builds from existing reduced-order aeroacoustic modeling techniques and ray tracing based geometrical acoustics and is demonstrated on the model NREL 5 MW wind turbine blade as a case study. The computational predictions demonstrated that damage could be successfully detected in the first half of the blade cavity near the root and that the change in frequency content may be indicative of the type of damage that has occurred. This study provides a foundation to analyze specific blades and likely damage cases for determining key factors of system design such as number and placement of sensors as well as for hardware selection.
Noise generated by turbulent boundary layer over the trailing edge of a wind turbine blade under various flow conditions is predicted and analyzed for structural health monitoring purposes. Wind turbine blade monitoring presents a challenge to wind farm operators, and an in-blade structural health monitoring system would significantly reduce O&M costs. Previous studies into structural health monitoring of blades have demonstrated the feasibility of designing a passive detection system based on monitoring the flow-generated acoustic spectra. A beneficial next step is identifying the robustness of such a system to wind turbine blades under different flow conditions. To examine this, a range of free stream air velocities from 5 m/s to 20 m/s and a range of rotor speeds from 5 rpm to 20 rpm are used in a reduced-order model of the flow-generated sound in the trailing edge turbulent boundary layer. The equivalent lumped acoustics sources are predicted based on the turbulent flow simulations, and acoustic spectra are calculated using acoustic ray tracing. Each case is evaluated based on the changes detected when damage is present. These results can be used to identify wind farms that would most benefit from this monitoring system to increase efficiency in deployment of turbines.
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