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Wind turbine noise uncertainty quantification for downwind conditions using metamodeling
Bill Kayser1, Benoit Gauvreau2, David Écotière1
1UMRAE, Cerema, Université Gustave Eiffel, Ifsttar 11, rue Jean Mentelin - BP 9, 67035 Strasbourg, France.
The Journal of the Acoustical Society of America
|February 2, 2022
Summary
Quantifying environmental uncertainty in wind turbine noise is crucial. This study uses a quasi-Monte Carlo method and metamodeling to accurately predict sound pressure levels and their variability in outdoor environments.
Area of Science:
- Acoustics
- Environmental Science
- Computational Modeling
Background:
- Environmental factors like ground and atmosphere introduce uncertainty in wind turbine noise predictions.
- Accurate sound level estimations require quantifying this uncertainty for realistic simulations.
Purpose of the Study:
- To develop and validate a method for quantifying uncertainty in wind turbine sound pressure levels.
- To improve the reliability of wind turbine noise predictions in complex outdoor environments.
Main Methods:
- Utilized a quasi-Monte Carlo method to sample influential environmental parameters.
- Coupled an Amiet emission model with a Parabolic Equation propagation model.
- Developed a kriging-based metamodel to reduce computational time for uncertainty quantification.
Main Results:
- Successfully calculated the probability distribution of sound pressure levels.
- Demonstrated that metamodeling significantly reduces computation time for stochastic analysis.
- Quantified statistics and uncertainties in downwind sound pressure levels.
Conclusions:
- The proposed uncertainty quantification method enhances understanding of sound pressure variability.
- Metamodeling is effective for managing computational costs in wind turbine noise modeling.
- This approach improves the quality control of wind turbine noise predictions in inhomogeneous environments.
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