Prediction of variability in wind turbine noise calculations

Abstract : We propose in this work a method to predict the variability in wind turbine noise calculations due to wind speed and direction fluctuations. First, wind lidar data measurements during a 24-hour period are analyzed, and four periods with different atmospheric stability conditions are selected. Then, a wind turbine noise model based on Amiet's theory for trailing edge noise is presented and used to predict the sound pressure level at a fixed receiver during the 24-hour period. Finally, a Monte Carlo sampling method is described that allows us to accurately predict the statistics of sound pressure level during each selected period. The variability is seen to be much more pronounced during the day than during the night, and statistical quantities are shown to depend on the period duration considered.
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Benjamin Cotté, Yuan Tian. Prediction of variability in wind turbine noise calculations. 6th International Meeting on Wind Turbine Noise, Apr 2015, Glasgow, United Kingdom. ⟨hal-01206966⟩

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