Wind turbine noise code benchmark: A comparison and verification exercise

In a number of institutions and companies, researchers and engineers are developing numerical models and frameworks that are used to predict the aerodynamic noise emissions from wind turbine rotors. The simulation codes range from empirically tuned engineering models to high-fidelity computational ones. Their common feature is the fact that they all specifically model the main aerodynamic noise mechanisms occurring at the rotating blades (namely, the turbulent boundary layer): trailing-edge and turbulent inflow noise. Nevertheless, different modelling techniques and implementations may generate different results, even when assessed on the same rotor design and operating conditions, whicmodels. Trailing-edge noise is put at the forefront of the present study, as it is recognized to be the main source of audible noise from modern wind turbines. The present benchmark aims at comparing the results from different modelling approaches and drawing some conclusions from these comparisons. This effort, denoted as Wind Turbine Noise Code benchmark, was initiated in 2019 as a joint activity between the IEA Wind Task 39 (Quiet Wind Turbine Technology) and Task 29 (Detailed Aerodynamics of Wind Turbines, now Task 47). In addition to the investigation of the noise emissions themselves, the rotor aerodynamic characteristics are investigated, as they are the source of the noise generation mechanisms discussed herein. A number of test cases are defined, and the aerodynamic and aeroacoustic predictions from the various models are compared. A fair agreement between the aerodynamic predictions is observed. There exist some discrepancies between the different noise prediction methods, but it is difficult to conclude if one methodology is better than another in order to design a wind turbine with noise as a constraint

The rotor as a sensor— Observing shear and veer from the operational data of a large wind turbine

This paper demonstrates the observation of wind shear and veer directly from the operational response of a wind turbine equipped with blade load sensors. Two independent neural-based observers, one for shear and one for veer, are first trained using a machine learning approach, and then used to produce estimates of these two wind characteristics from measured blade load harmonics. The study is based on a data set collected at an experimental test site, featuring a highly-instrumented 8 MW wind turbine, an IEC-compliant met mast, and a vertical profiling lidar reaching above the rotor top. The present study reports the first demonstration of the measurement of wind veer with this technology, and the first validation of shear and veer with respect to lidar measurements spanning the whole rotor height. Results are presented in terms of correlations, exemplary time histories and aggregated statistical metrics. Measurements of shear and veer produced by the observers are very similar to the ones obtained with the widely adopted profiling lidar, while avoiding its complexity and associated costs