Validity of the effective sound speed approximation in parabolic equation models for wind turbine noise propagation.

Parabolic equation (PE) based methods are widely used in outdoor acoustics because they can solve acoustic propagation problems above a mixed ground in a refractive and scattering atmosphere. However, recent research has shown phase error due to the effective sound speed approximation (ESSA). To overcome these limitations, a new PE formulation derived without the ESSA has been proposed recently.

NoiseCapture smartphone application as pedagogical support for education and public awareness.

Teaching science subjects such as acoustics to youth or the general public can be facilitated by illustrating physical phenomena or scientific issues using fun experiences. A few years ago, our team developed a smartphone application named NoiseCapture with the aim of offering to anyone the opportunity to measure their sound environment and to share their geolocated measurements with the community in order to build a collective noise map. Since then, NoiseCapture team members have experimented with numerous interventions in schools or scientific events for the general public based on the app to explain not only societal and environmental issues related to noise but also to teach acoustic notions and to address technical and scientific topics associated with sound measurement.

Wind turbine noise uncertainty quantification for downwind conditions using metamodeling.

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.

Quantification of Sound Exposure from Wind Turbines in France.

The WHO guidelines on environmental noise highlight that evidence on the health effects of wind turbine sound levels is either non-existent or of poor quality. In this context, a feasibility study was conducted in France in 2017. The objective was to suggest a methodology for calculating wind turbine sound levels in order to quantify the number of windfarms' residents exposed to this sound.

Automatic estimation of the sound emergence of wind turbine noise with nonnegative matrix factorization.

In many countries, the acoustic impact of wind farms is often constrained by a curtailment plan to limit their noise, which spreads in their surroundings. To update the plan, on/off cycle measurements are performed to determine the ambient noise (wind turbines in operation) and residual noise (wind turbines shut down), but these shutdown operations are limited in time, which reduces the representativeness of the estimated in situ emergence. Consequently, a machine learning technique, called nonnegative matrix factorization (NMF), is proposed to estimate the sound emergence of wind turbines continuously, i.

Environmental parameters sensitivity analysis for the modeling of wind turbine noise in downwind conditions.

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.

Sensitivity analysis of a parabolic equation model to ground impedance and surface roughness for wind turbine noise.

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.