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. We investigate the impact of such phase error on wind turbine noise modeling, as the classical wide-angle parabolic equation (WAPE) with ESSA is widely used in the research community. We propose a comparison between the classical WAPE with ESSA and the new WAPE derived without the ESSA in the context of wind turbine noise. We highlight large phase error (several dB) on monochromatic calculations with a point source. Using an extended sound source representative of a wind turbine, we show small phase error (<1 dB) in a wind turbine noise context where sound level variability far from the source is of several dB. The validity of previous works using WAPE with ESSA is, thus, not questioned, although we do recommend the use of the new WAPE derived without the ESSA to accurately model the effect of wind speed on sound propagation.

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. This paper describes some of the interventions implemented, in particular, in a school context through training courses given to middle school and university students, as well as teachers of secondary school, that focused on basic knowledge of buildings and environmental acoustics, on the practice of acoustic measurement, and on noise mapping. Some examples of interventions with the general public are also presented that were mostly integrated into scientific events.

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.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.

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.e., without stopping the machines. In the first step, the application of NMF on a corpus of various simulated scenes allows the determination of the optimal setting of the method to better estimate the sound emergence. The results show the proper adaptation of the method with regard to the influence of the propagation distance and atmospheric conditions. This method also proves to be efficient in cases in which the real emergence is less than 5 dB(A) with a mean error lower than 2 dB(A). The first comparison with in situ measurements validates these performances and allows the consideration of the application of this method to optimize wind farm operations.

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. 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.

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. 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.

Geostatistical modeling of sound propagation: Principles and a field application experiment.

The assessment of noise sources for environmental purposes requires reliable methods for mapping. Numerical models are well adapted for sophisticated simulations and sensitivity analyses; however, real-time mapping of large frequency bands must be based on fast and acceptable computations and honor in situ measurements. In this paper, a real-time mapping procedure of noise exposure is proposed. The procedure is based on geostatistical modeling of spatial variations and applied to a case study taken from an experimental campaign, where a point source was placed on a flat meadow. An analytical approximation of the acoustic field was first computed with the Embleton model. The difference between this approximation and the actual measurements (L(eq15 min) 1/3-octave bands samples from 19 microphones spread over the meadow) showed spatial structure, which has been modeled with a variogram. Finally, the geostatistical technique of kriging with external drift provided an optimal interpolation of the acoustic field data while encapsulating the first approximation from the Embleton model. Systematic geostatistical inference and real-time mapping with the proposed procedure can be envisaged in simple cases.

Traffic noise prediction with the parabolic equation method: validation of a split-step Padé approach in complex environments.

This study deals with sound propagation in typical traffic noise conditions. The numerical results are obtained through the split-step Padé method and the discrete random Fourier modes technique. These are first evaluated qualitatively, by color contour maps showing noise propagation, diffraction by an impedance discontinuity or a screen edge, and scattering by atmospheric turbulence. Next, our numerical results are quantitatively validated by comparison with analytical models and other parabolic equation models. For all the atmospheric conditions and geometrical configurations available in literature, the agreement between the different methods is very good, except for some cases involving the atmospheric turbulence. However, in those particular cases, the split-step Padé results are shown to be more consistent with physical theory. Finally, our method seems to be very powerful and reliable for traffic noise prediction.