PreWinT - Prediction and Mitigation of Wind-Turbine Noise and Its Impact on Humans
WP2 - Data collection and analysis
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Multiple sites will be selected to 4 capture diverse topographies and cover diurnal and seasonal atmospheric variations, prioritizing locations with notable noise challenges. Measurements are designed to serve both source modelling (emission characterisation) and propagation modelling (long-range levels and AM), with meteorological and LIDAR data synchronised for both purposes.
This data encompasses acoustic measurements and operational turbine parameters—both of which are vital for correlating specific environmental and turbine-related factors with wind turbine noise (WTN) emissions. Existing data sets will also be used to enrich the data pool.
Analysis methods will include exploratory and iterative approaches, such as:
- spectrogram visualization
- measurement quality assessments
- statistical techniques (e.g., Principal Component Analysis).
Unsupervised machine learning (e.g., autoencoders) may be used to detect hidden patterns and potential dependencies that might otherwise go unrecognized. Recognizing that wind flow characteristics play a key role in both WTN generation and propagation, WP2 will also apply Large-Eddy Simulation (LES) turbulence models coupled with an Actuator Line (AL) approach to model airflow around turbine blades and through wakes. These methods aim to improve understanding of wind fields, sound-wave propagation, and the influence of icing on rotor blades—a phenomenon that can lead to noise level increases of several decibels.
An iterative loop between data collection and analysis ensures that insights gained guide subsequent measurement campaigns, refining data capture strategies over time. This systematic approach strengthens the link between empirical observation, model validation, and eventual application in operational wind farm optimization.
This will be done through the following tasks:
- T2.1 Acoustic and Atmospheric Data Collection
- T2.2 Wind Turbine Data Collection
- T2.3 Wind Flow Modelling
- T2.4 Data Analysis