Modeling a wind turbine sound field involves taking into account the main aeroacoustic sources that are generally dominant for modern wind turbines, as well as environmental phenomena such as atmospheric conditions and ground properties that are variable in both time and space. A crucial step to obtain reliable predictions is to estimate the relative influence of environmental parameters on acoustic emission and propagation, in order to determine the parameters that induce the greatest variability on sound pressure level. Thus, this study proposes a Morris sensitivity analysis of a wind turbine noise emission model combined with a sound propagation model in downwind conditions. The emission model is based on Amiet's theory and propagation effects are modeled by the wide-angle parabolic equation. The whole simulation takes into account ground effects (absorption through acoustic impedance and scattering through surface roughness) and micrometeorological effects (mean refraction through the vertical gradient of effective sound speed). The final results show that the parameters involved in atmospheric refraction and in ground absorption have a significant influence on sound pressure level. On the other hand, in the context of this study the relative air humidity and the ground roughness parameters appear to be negligible on sound pressure level sensitivity.
Input parameters of outdoor sound prediction models are related to environmental phenomena, such as atmospheric conditions and ground properties, which are variable in both time and space. In order to obtain reliable predictions, it is essential to get information on uncertainties by quantifying the sensitivity of numerical or analytical models to their input parameters, and thus determine the inputs that will be the main source of uncertainties. This paper focuses on ground parameters impact on sound propagation considering wind turbine noise. First, the implementation of ground roughness in a parabolic equation model validated against scale model measurements and analytical solution is proposed. Then, the sensitivity of the model to its ground parameters is performed with the Morris' screening method in order to access their relative influences. Three parameters are considered: the ground absorption through the airflow resistivity, the ground roughness through the roughness height, and correlation length. Results clearly show that the variations of ground roughness induce non-negligible differences in sound pressure levels regarding the ground absorption, even for high height sound source, i.e., nongrazing incidence.
The influence of the ground and atmosphere on sound generation and propagation from wind turbines creates uncertainty in sound level estimations. Realistic simulations of wind turbine noise thus require quantifying the overall uncertainty on sound pressure levels induced by environmental phenomena. This study proposes a method of uncertainty quantification using a quasi-Monte Carlo method of sampling influential input data (i.e., environmental parameters) to feed an Amiet emission model coupled with a Parabolic Equation propagation model. This method allows for calculation of the probability distribution of the output data (i.e., sound pressure levels). As this stochastic uncertainty quantification method requires a large number of simulations, a metamodel of the global (emission-propagation) wind turbine noise model was built using the kriging interpolation technique to drastically reduce calculation time. When properly employed, the metamodeling technique can quantify statistics and uncertainties in sound pressure levels at locations downwind from wind turbines. This information provides better knowledge of sound pressure variability and will help to better control the quality of wind turbine noise prediction for inhomogeneous outdoor environments.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.