Comment on wes-2022-80

Sound propagation under water is a complex process. Sound does not propagate along straight-line transmission paths. Rather, it reflects, refracts, and diffracts. It scatters off rough surfaces (such as the sea surface and the seafloor) and off reflectors within the water column (e.g., gas bubbles, fish swim bladders, and suspended particles). It is transmitted into the seafloor and partially lost from the water. It is converted into heat by exciting molecular vibrations. There are common misconceptions about sound propagation in water, such as “low-frequency sound does not propagate in shallow water,” “over hard seafloors, all sound is reflected, leading to cylindrical spreading,” and “over soft seafloors, sound propagates spherically.” This chapter aims to remove common misconceptions and empowers the reader to comprehend sound propagation phenomena in a range of environments and appreciate the limitations of widely used sound propagation models. The chapter begins by deriving the sonar equation for a number of scenarios, including animal acoustic communication, communication masking by noise, and acoustic surveying of animals. It introduces the concept of the layered ocean, presenting temperature, salinity, and resulting sound speed profiles. These are needed to develop the most common concepts of sound propagation under water: ray tracing and normal modes. This chapter explains Snell’s law, reflection and transmission coefficients, and Lloyd’s mirror. It provides an overview of publicly available sound propagation software (including wavenumber integration and parabolic equation models). It concludes with a few practical examples of modeling propagation loss for whale song and a seismic airgun array.

A new multi-fidelity flow-acoustics simulation framework for wind farm application

This paper deals with the sound propagation modeling in urban area. This problematic requires to take into account many phenomena that can have a substantial impact as well in semienclosed spaces as on long-range outdoor sound propagation, such as reflections and absorption on the frontages and on the ground, atmospheric attenuation, sound velocity variations related with wind and temperature vertical gradients, atmospheric turbulences. The numerical method used is the TLM (transmission line modeling), which has been originated in electromagnetism and adapted for acoustics applications. It consists in a physical rendering of the waves propagation based on the Huygens' principle. It is established on a spatiotemporal discretization of the domain using an iterative temporal process for sound pressure propagation, instead of the resolution of mathematical equations. The TLM model has then been developed in two and three dimensions allowing to combine all the phenomena affecting the sound propagation in urban area. Numerical simulations are given for canyon streets.

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.

Matched-fieId inversion (MFI) undertakes to estimate the geometric and geoacoustic parameters in an ocean acoustic scenario by matching acoustic field data recorded at hydrophone array with numerical calculations of the field. The model which provides the best fit to the data is the estimate of the actual experimental scenario. MFI provides a comparatively inexpensive method for estimating ocean bottom parameters over an extensive area. The basic components of the inversion process are a sound propagation model and matching (minimization) algorithm. Since a typical MFI problem requires a large number of computationally intensive sound propagation calculations, both of these components have to be efficient. In this study, a hybrid inversion algorithm which uses a parabolic equation propagation model and combines the downhill simplex algorithm with genetic algorithms is introduced. The algorithm is demonstrated on synthetic range-dependent shallow-water data generated using the parabolic equation propagation model. The performance for estimating the model parameters is compared for realistic signal-to-noise ratios in the synthetic data.

Energy usage is on the rise in both Canada and the United States. Because of this, there is a growing demand and strain on the current infrastructure. More importantly though, there is a strong demand for the use of renewable energy sources to meet this demand. One of the most popular renewable energy sources at this time is the wind turbine. In Ontario, there are plans to implement a significant number of them throughout the province. There are concerns though from residents in the vicinity of them that they cause too much noise, as well as health issues. However, some argue that these complaints stem from incorrectly calculated setback distances due to the lack of use of a detailed sound propagation model. In this study, a sound propagation model was developed using a Finite-Difference Time-Domain method, for a three dimensional computational domain, and simulated using data for a Siemens SWT-2.3-101 wind turbine. The simulations produced data of the sound propagation characteristics of each emitted wave, for each tested case. The model was developed as a starting point and building block for the eventual use in simulations of large domains and complex flow phenomena.

Abstract. Prediction of outdoor sound levels in 3D space is important for noise management, soundscaping etc. Sound levels at outdoor can be predicted using sound propagation models which need terrain parameters. The existing practices of incorporating terrain parameters into models are often limited due to inadequate data or inability to determine accurate sound transmission paths through a terrain. This leads to poor accuracy in modelling. LIDAR data and Aerial Photograph (or Satellite Images) provide opportunity to incorporate high resolution data into sound models. To realize this, identification of building and other objects and their use for extraction of terrain parameters are fundamental. However, development of a suitable technique, to incorporate terrain parameters from classified LIDAR data and Aerial Photograph, for sound modelling is a challenge. Determination of terrain parameters along various transmission paths of sound from sound source to a receiver becomes very complex in an urban environment due to the presence of varied and complex urban features. This paper presents a technique to identify the principal paths through which sound transmits from source to receiver. Further, the identified principal paths are incorporated inside the sound model for sound prediction. Techniques based on plane cutting and line tracing are developed for determining principal paths and terrain parameters, which use various information, e.g., building corner and edges, triangulated ground, tree points and locations of source and receiver. The techniques developed are validated through a field experiment. Finally efficacy of the proposed technique is demonstrated by developing a noise map for a test site.

The accuracy of input data is a key issue in sound propagation model calculations. This paper aimed to assess the effect of building and land cover input data accuracy on CNOSSOS-EU sound propagation model outputs. Calculations were performed for a study site with a quite diverse land cover structure, located along a major road in Monor, Hungary. Nine test cases were defined based on building and land cover datasets with different accuracy. Comparing the results of the test cases to each other, it was found that in residential areas, the sound propagation model is more sensitive to the building data than to the land cover data. Therefore, it is recommended to use more detailed building input data in those areas, while using a land cover database with higher resolution than CORINE land cover data does not provide significantly better results. In non-residential areas, the influence of land cover input data on model results increases significantly.

Sound propagation of wind farms is typically simulated by the use of engineering tools that are neglecting some atmospheric conditions and terrain effects. Wind and temperature profiles, however, can affect the propagation of sound and thus the perceived sound in the far field. A better understanding and application of those effects would allow a more optimized farm operation towards meeting noise regulations and optimizing energy yield. This paper presents the parabolic equation (PE) model development for accurate wind turbine noise propagation. The model is validated against analytic solutions for a uniform sound speed profile, benchmark problems for nonuniform sound speed profiles, and field sound test data for real environmental acoustics. It is shown that PE provides good agreement with the measured data, except upwind propagation cases in which turbulence scattering is important. Finally, the PE model uses computational fluid dynamics results as input to accurately predict sound propagation for complex flows such as wake flows. It is demonstrated that wake flows significantly modify the sound propagation characteristics.

The sound-ray model SMART (Sound propagation model of the atmosphere using ray-tracing) simulates the modified sound propagation due to sound-ray refraction in a stratified and moving atmosphere. The study was focussed on the influence of refraction on the sound propagation because this effect can result in a significant deviation from the straight-ray approximation used by the acoustic tomography and by the SODAR technique. For validation of the simulated data a measuring campaign was carried out over flat terrain in autumn 2004. The comparison of the modelled with measured data during clear night conditions with strong temperature inversion shows a satisfactory agreement, which leads to the conclusion that the important effect of refraction on outdoor sound propagation is reliably described by the model SMART.

The proper evaluation of sound propagation between sources/receivers and scatterers is important in characterizing bottom volume scattering. In this article, several sound propagation models used in bottom volume scattering studies are evaluated and their results compared to the exact solution obtained through a numerical wave number integration technique. It is found that Hines' approach [J. Acoust. Soc. Am. 88, 324-334 (1990)] works well for the two isovelocity half-space case except when the grazing angle is close to the critical angle. The far-field approximation, given by Ivakin [Sov. Phys. Acoust. 32(6), 492-496 (1986)] and Mourad and Jackson [J. Acoust. Soc. Am. 94, 344-358 (1993)], has a performance depending upon the sound speed structure in the sediment. For an isovelocity slow bottom, it agrees well with the exact solution. However, discrepancies arise for an isovelocity fast bottom or a bottom with a complex sound speed structure. In addition, the appropriateness of using the equivalent surface scattering strength as a function of grazing angle in volume scattering characterizations is studied. In conclusion, precautions need to be taken in modeling both the propagation effects and the scattering mechanisms associated with the bottom volume scattering process.

The investigation of the effects of the noise generated by harvesting equipment on the environment is one of the important topics in sustainable forestry. During timber harvesting, not only workers but also wildlife are exposed to the noise generated. Exposure to noise has both direct and indirect effects on humans and wildlife. The negative effects of noise exposure can be observed depending on its intensity and duration. Noise exposure, which has various psychological and physiological effects on humans, also negatively affects plants and animals. In this study, sound measurements of the chainsaw were conducted during thinning operations within the boundaries of the Alara Forest Management Directorate in Alanya, Antalya Province. The measurement area is a Turkish red pine (Pinus brutia Ten.) stand with a canopy density of 60–65%, a slope of 30–35%, and tree diameters ranging from 20 to 35 centimeters. The noise emitted by the chainsaw during production, ranging from approximately 1 meter to 200 meters, has been modeled using a feedforward Artificial Neural Network (FANN) for sound propagation. The measurement data was used 60% for training, 20% for testing, and 20% for validation. Random trees were assigned to noise attenuation effects on the sound according to the stand characteristics of the study area. Thus, it was aimed to create a realistic sound propagation model and estimation maps. The average performance metrics of the model, RMSE and R² values, were calculated as 4.84 and 0.88, respectively. According to the sound propagation model predicted by the FANN as a static model, it is estimated that the distance at which the chainsaw could affect wildlife behavior is 400 meters or less.

The seabed possesses variations in its material properties, layering structure, slope and roughness making prediction, and analysis of sound propagation in shallow water highly complex. For certain frequency ranges, the effect of roughness becomes significant. Numerous models have been adopted to account for the scattering of acoustic waves by rough surfaces, including Kirchhoff approximation (for relatively large scales of roughness), small perturbation theory (for relatively small scales of roughness), and small slope approximation, which bridges across the two regimes under certain conditions. Although grounded in analytical theory, these approaches invariably involve the computation of numerical integrals so additional approximation theories have been explored including stationary phase methods and Taylor series approximations. In this study, these semi-analytical methods are incorporated within a new sound propagation model and compared with a numerical graphics processing unit (GPU)-accelerated finite element model incorporating roughness parameters (root mean square height and correlation length) for rough, sloped seabed geometries (with gradients ranging from 0° to 45°) in shallow water environments. The effect of large- and small-scale roughness on sound propagation is investigated in two- and three-dimensional space. The finite element modelling is used to validate the optimal choice of semi-analytical method for the different environments considered.

A tank experiment is being conducted to explore sound propagation in a continuously stratified ocean containing internal gravity waves that produce fluctuations in acoustic signals. The tank is filled with water of increasing salinity with increasing depth in the fluid; the variation in salinity results in a 150 m/s variation of the sound speed over the 50 cm-depth fluid. Sound arrival times determined in three-dimensional hydrophone scans map out the sound diffraction pattern in the stratified fluid. Good agreement is found between the measurements and results obtained from wavefront modeling by ray acoustics. Sound fluctuations produced by internal waves are measured for several acoustic track locations and at different phases of the internal waves that radiate from a mechanical wave generator. Internal wave fields measured by particle image velocimetry provide input for a parabolic equation model, which is used to predict the sound fluctuations for comparison with measurements. This research is designed to improve the understanding and modeling of sound propagation and scattering by internal waves in the realistic oceans. [Research support: The 2013-14 ASA F. V. Hunt Postdoctoral Research Fellowship (L. Zhang) and ONR MURI Grant N000141110701 (WHOI).]

Hourly modeled wind turbine power output time series – modeled from outputs from the mesoscale numerical weather prediction system WRF – are used to examine the spatial smoothing of various wind farm portfolios located on a complex isolated island group with a surface area of 1400 km2. Power spectral densities (PSD), hourly step change functions, and duration curves are generated, and the 5th and 95th percentiles of the step change functions are calculated. The spatial smoothing is identified from smaller high frequency PSD values, less hourly fluctuations, and more flat duration curves per installed wind power capacity, compared to single wind turbine outputs. A discussion on the limitation of the spatial smoothing for the region is included, where a smoothing effect is clear for periods up to 1–2 days, although most evident at the higher frequencies. By maximizing the smoothing effect, optimal wind farm portfolios are presented with the intention to minimize the overall wind power fluctuations. The focus is mainly on the smoothing effect in highest resolvable frequencies. Optimizing wind farm capacities at fourteen pre-defined good wind farm site locations has a minimal improvement on the hourly fluctuations. However, choosing good combinations of the individual wind farm site locations decrease the 1–3 hourly fluctuations considerably; the optimized wind farm portfolios consist of distant wind farms, while poor portfolios consist of clustered wind farms. The 5th and 95th percentiles are 15 % less for an optimized portfolio with four wind farms compared to a poor combination of four wind farms.