Numerical sound propagation models include a wide range of methods with their own advantages depending on the physical phenomena under consideration. Due to its low-order scheme characteristic, the transmission line matrix (TLM) model is considered for coarse modeling of sound fields in complex outdoor environments. However, the space-time integration scheme of the method is dispersive and affects the free-field predictions. This paper extends a previous study of the numerical dispersion effect on the acoustic pressure field. An ideal case with specular reflections on a perfectly reflective ground is considered to represent the worst-case scenario, as the absence of absorption by the boundary maximizes the influence of numerical dispersion. First, a reminder of the model boundary conditions for specular reflection is provided, and the corresponding analytical solution is introduced as a reference to evaluate the reliability of the model. Then, a numerical experiment is presented and performed for different sound source characteristics. The result analysis shows that dispersion can induce misplaced interferences in the numerically simulated sound fields and the resulting errors are quantified in terms of sound pressure levels (SPL). Finally, an analytical comparison with a well-known finite difference numerical scheme gives a perspective on the TLM model performance regarding several applications.

We present efforts to adapt the time-domain Transmission Line Matrix (TLM) method for modeling sound propagation in forests. It is relevant to professionals and researchers in environmental acoustics and to those interested in studying sound propagation in outdoor environments using numerical modeling techniques. The study aims to demonstrate the applicability of the method in complex media where sound waves undergo multiple reflections combined with ground effects during their propagation from a sound source to a receiver point. The TLM method is used to numerically model sound propagation in a 3D forest geometry generated based on a real tree distribution case. Data from an experimental campaign conducted in the rainforest of French Guyana are used as a reference. The results show an encouraging comparison between them and the in situ measurements, even if the hypotheses made to adapt the input data in the model were strong. We find that more measurement points and more specifications (ground impedance measurements, canopy density, etc.) about the experimental site are needed for further simulations, even if obtaining them presents technical difficulties.

The PIBE project aims to improve wind turbine noise prediction methods and to explore new solutions for noise reduction. This five years project brought together French experts in aeroacoustics, sound propagation, experimental noise characterization and wind engineering, and was structured around three work packages. The first one aimed to study amplitude modulation phenomena and focused particularly the characterization of dynamic stall noise. Specific aerodynamic and acoustic measurements were carried out in a wind tunnel, showing the influence of several stall regimes on noise production. The second work package focused on quantifying the uncertainties of noise prediction methods. It developed an open-access online application (WindTUNE) that quantifies uncertainties on noise prediction of a wind farm, and a parametric and uncertainties calculation tool for the engineering application Code-TYMPAN. This axe also produced an open-access database of a 400 days campaign of meteorological and acoustical measurements around a wind farm. The last work package investigated new noise reduction devices using blades with modified leading and/or trailing edges. The efficiency of the solutions were characterized in a wind tunnel, both acoustically and aerodynamically. The paper presents the main results obtained at the end of the project.

The PIBE project is the first French collaborative research project on wind turbine noise. One of its objectives is to quantify the experimental dispersion observed in situ on acoustic quantities/metrics and influencing parameters, and to compare it with the numerical dispersion estimated using acoustic propagation models. A long-term experimental campaign was carried out over consecutive 410 days around a wind farm comprising eight 80-metre-high and 90-metre diameter turbines. Acoustic sensors (five Class I sound level meters) were deployed on site, recording third-octave spectra and overall A- and Z-weighted values of the Leq indicator and fractile indices (L10, L50 and L90), at distances from 325 m to 1400 m from the wind turbines row. In addition, three 3D ultrasonic anemometers and a Lidar system were used to measure meteorological variables influencing long-range sound propagation (e.g. wind and temperature verical profiles). Besides, train and aircraft passing close to the site were inventoried to identify periods of high background noise. All experimental data have been processed into an ElasticSearch database. An interactive web application has been developed in R Shiny to facilitate processing, visualization and analysis of the experimental database.

We present efforts to adapt the time-domain Transmission Line Matrix (TLM) method for modeling sound propagation in forests. It is relevant to professionals and researchers in environmental acoustics and to those interested in studying sound propagation in outdoor environments using numerical modeling techniques. The study aims to demonstrate the applicability of the method in complex media where sound waves undergo multiple reflections combined with ground effects during their propagation from a sound source to a receiver point. The TLM method is used to numerically model sound propagation in a 3D forest geometry generated based on a real tree distribution case. Data from an experimental campaign conducted in the rainforest of French Guyana are used as a reference. The results show an encouraging comparison between them and the in situ measurements, even if the hypotheses made to adapt the input data in the model were strong. We find that more measurement points and more specifications (ground impedance measurements, canopy density, etc.) about the experimental site are needed for further simulations, even if obtaining them presents technical difficulties.

Wind Turbine Noise (WTN) variability is often related to the influence of local micrometeorology as well as to space and time variations in the ground properties. Thus, the PIBE Project (2020-2024) aims to estimate these variabilities and their associated uncertainties. Its objective is to enable engineering companies or wind farm developers to estimate more accurately the risk of noise. In the WP2 of the PIBE project, 3 approaches are explored: (i) a NUMerical approach through the development of a metamodel, (ii) an EXPerimental approach through a major in-situ measurement campaign lasting more than 1 year, and (iii) a STATistical approach consisting in the evaluation of WTN variability. The NUM approach consists in sound emission calculation of each wind turbine using the Amiet's theory, and sound propagation calculation using a wide-angle Parabolic Equation (WAPE) modelling. The EXP part of the PIBE project has led to the development of a large database, accessible via a dedicated Shiny application. The last approach (STAT) has been carried out using both an open-access online tool called WindTUNE and an open-source tool based on Code_TYMPANTM, namely OCP. The 3 approaches (NUM + EXP + STAT) are presented and the corresponding results are discussed.

Representative predictions of wind turbine noise require to accurately model the main mechanisms and characteristics of acoustic emission (i.e., extended sound source with aeroacoustic noise generation) and acoustic propagation in outdoor environment (i.e., ground effects and atmospheric properties). As these phenomena fluctuate over time and space, it leads to great uncertainty on Sound Pressure Level (SPL) estimated at local resident bulidings/facades. Such uncertainty is not yet properly quantified by engineering noise prediction models. Thus, this paper presents a modeling tool developed in the framework of the French project PIBE, which aims at quantifying the SPL uncertainty involved in wind farm noise predictions. Ultimately, this modeling tool will be freely available online and will help to better understand the risk of noise pollution at each stage of a wind farm's life, in order to guarantee compliance with the regulatory requirements concerning the exposure of local populations.

The understanding and prediction of wind turbine noise (WTN) remain a subject on which progress is needed to better address the concerns of residents of some wind farms and to help wind farm operators to better optimize their wind farms. A large-scale and long-term measurement campaign was conducted by partners of the PIBE project (https://www.anr-pibe.com/en) near a French wind farm during 430 days, in order to study the emission and propagation of WTN. The campaign provided 100ms sound levels and acoustic spectra, together with periodically recorded 2 min audio samples, at 5 locations ranging from 350m to 1.3km from the wind farm, in different propagation directions. Meteorological data were simultaneously collected by a 80m meteorological mast, one or two Lidars and sonic anemometers, in order to characterize vertical profiles of wind speed, wind direction, temperature, and wind turbulence. 2 distinct weeks of intensive measurements (one in summer and one in winter) completed the data by including 15 additional acoustic points at other locations. The database will mainly be used within the project for the investigation of an uncertainty model of WTN, and of WTN amplitude modulations. This communication presents the database and first results about temporal variabilities of WTN.

This paper investigates the influence of atmospheric turbulence on the propagation of wind turbine noise using an aeroacoustic source model coupled with a parabolic equation propagation model. Sets of simulations with and without atmospheric turbulence are performed, allowing the determination of a simple formulation that quantifies the uncertainties of the A-weighted sound pressure level (SPL) when the modelling does not account for atmospheric turbulence. For the case study, the results show that atmospheric turbulence has a negligible effect on SPL up to 800 m from the wind turbine, even under upwind conditions. While the conclusions are specific to this case study, the method appears promising for simplifying the calculation of atmospheric turbulence effect in wind turbine noise studies. A freely accessible online application has been developed to present additional results.

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.

The study of wind turbine noise and its impact is of growing importance with the increase in the demand for green and clean energy. As it is known that wind turbine noise can be a cause of annoyance in the vicinity of wind farms it is beneficial to predict with certain accuracy the generated noise in the design phase itself. A crucial step is the validation of prediction models against field measurements (in-situ). This article presents a wind turbine noise prediction model that combines Amiet's theory to calculate trailing edge noise and turbulence interaction noise in free field with a wide-angle parabolic equation valid in moving media to account for the long-range acoustic propagation effects. The model considers the wind turbine as an extended noise source and the rotation effects (such as the convective amplification and Doppler effect) are taken into account. The predicted noise levels are compared to those obtained from a measurement campaign where acoustic, meteorological and ground impedance data have been recorded simultaneously. First, the sound source model is validated close to the wind turbines for different wind speeds and directions. Then, noise predictions are compared to SPL measurements at various distances from the sound source, between 350 and 1300meters.

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.

In this work, the influence of micrometeorological effects on sound propagation in an urban street canyon is investigated numerically and experimentally. Numerical simulations of acoustic propagation are based on sound particle propagation simulation method. Numerical data are generated for urban street canyons of various widths and height ratios. Experimental data are obtained from longitudinal measurements of urban street canyons in the United States. Temperature and wind profiles are obtained from ultrasonic anemometers and thermocouples. Measurements within and outside the street canyon are of particular interest. The experimental data are useful in integrating micrometeorological effects into the acoustic propagation model. Preliminary numerical results and measurements are presented and discussed.

Outdoor sound propagation modeling can be performed through various numerical models. Nowadays, time-domain methods are widely used and enable accurate simulations for most of the sound physical phenomena involved in environmental acoustics. However, the transmission line matrix (TLM) method remains relatively poorly documented. In this paper, a thorough review of the method and a stability analysis are provided. The review of the model leads to a more robust understanding of its link with the wave equation using Taylor expansions. Two different cases are considered: a homogeneous non-dissipative medium and an inhomogeneous dissipative one. The stability analysis shows the quantification of the model inherent dispersion error and highlights similarities with a finite difference scheme. To conclude, a numerical experiment aims to characterize precisely the limitations of the method applied to outdoor sound propagation.

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.