A tutorial on the parabolic equation (PE) model used for long range sound propagation in the atmosphere

For environmental noise prediction, it is practicable to use meteorological data available from local meteorological observatories. However, these observations have limitations induced by the methods of measuring and data processing. Usually only mean meteorological values averaged over one 10 min period every hour are calculated. To apply these mean meteorological variables to noise propagation appropriately, we need to investigate the characteristics of both acoustic and meteorological parameters within the 10 min period. We made simultaneous measurements of both parameters over flat grass-covered ground and estimated effective sound speed profiles by similarity theory, using the meteorological data measured under conditions similar to those at local observatories. The changes in sound pressure level in periods around sunrise and sunset were similar and were smaller than those around culmination, in which fluctuations of approximately 20 dB were measured at higher frequencies at a distance of 100 m. Noise predictions by the parabolic equation method and sound speed profiles determined from instantaneous meteorological variations generally agreed with the measurements except in the time period around culmination. When we used 10 min mean meteorological values in combination with the parabolic equation method, we obtained reasonable agreement with the measurements at middle frequencies in time periods around sunrise and sunset.

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“…9) [11,12]. In the PE method, a sound field is calculated by solving a PE that follows from a wave equation by introducing some assumptions.…”

Section: Comparison Of Relative Sound Pressure Levels Between Measurementioning

confidence: 99%

Influence of short-term meteorological variations over flat grass-covered ground on noise level fluctuation and application of mean meteorological data to noise prediction

Imaizumi

Takahashi

2010

Acoust. Sci. & Tech.

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“…Due to the nonstationary behaviour of the physical atmosphere, as well as the fact that no actual field data set encompasses the variability and randomness required for the large range of parameters used to describe the propagation environment, our data set was synthetically generated using a nondimensional parabolic equation (PE) model and Crank-Nicholson solution scheme (Mungiole & Wilson, 2006;Salomons, 2001;West et al, 1992). Part of the rationale for using the nondimensional PE model is that it reduces the number of parameters required to specify the propagation environment from 15 or more, to the 10 nondimensional neural network input parameters discussed in Mungiole and Wilson (2006).…”

Section: Review Of the Parabolic Equation Model For Sound Propagationmentioning

confidence: 99%

“…In this section, we briefly describe the main points in deriving the nondimensional PE model; a more thorough derivation is provided in Mungiole and Wilson (2006). The crux of our model as described in West et al (1992) is the two-dimensional narrow-angle PE, which approximates the full wave equation for atmospheric sound propagation by…”

Section: Review Of the Parabolic Equation Model For Sound Propagationmentioning

confidence: 99%

“…Originally developed for applications in underwater acoustics, several computational methods for modelling acoustic environments have been adapted and successfully applied (Gilbert & Di, 1993;Salomons, 2001;West, Gilbert, & Sack, 1992;West, Sack, & Walden, 1991) to atmospheric acoustic problems over several years. The fast-field program (FFP) (West et al, 1991), Crank-Nicholson parabolic equation (CNPE) (West et al, 1992), and Green's function parabolic equation (GFPE) (Gilbert & Di, 1993) are among the most extensively studied atmospheric acoustic models.…”

Section: Introductionmentioning

confidence: 99%

“…The fast-field program (FFP) (West et al, 1991), Crank-Nicholson parabolic equation (CNPE) (West et al, 1992), and Green's function parabolic equation (GFPE) (Gilbert & Di, 1993) are among the most extensively studied atmospheric acoustic models. Using any of these techniques, the calculated TL values are generally accepted as reliable (Attenborough et al, 1995), but the computational resources required to compute these TL values is highly variable.…”

Section: Introductionmentioning

confidence: 99%

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Environmentally adaptive acoustic transmission loss prediction in turbulent and nonturbulent atmospheres

2007

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An environmentally adaptive system for prediction of acoustic transmission loss (TL) in the atmosphere is developed in this paper. This system uses several back propagation neural network predictors, each corresponding to a specific environmental condition. The outputs of the expert predictors are combined using a fuzzy confidence measure and a nonlinear fusion system. Using this prediction methodology the computational intractability of traditional acoustic model-based approaches is eliminated. The proposed TL prediction system is tested on two synthetic acoustic data sets for a wide range of geometrical, source and environmental conditions including both nonturbulent and turbulent atmospheres. Test results of the system showed root mean square (RMS) errors of 1.84 dB for the nonturbulent and 1.36 dB for the turbulent conditions, respectively, which are acceptable levels for near real-time performance. Additionally, the environmentally adaptive system demonstrated improved TL prediction accuracy at high frequencies and large values of horizontal separation between source and receiver.

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Finite element simulation of sound propagation concerning meteorological conditions

Nomura

Takagi²,

Sato

2010

Numerical Methods in Fluids

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SUMMARYA finite element analysis method is investigated for the sound propagation problem in which meteorological conditions, such as wind or stratified atmospheric temperature distribution, are concerned. The basic equations are the coupled partial differential equation system in which the sound pressure and the fluid particle velocity are unknown variables. The quantities associated to the ambient atmospheric medium are assumed steady. Performance of the time integration methods are compared. The outgoing boundary condition and the impedance boundary conditions are introduced in the computational procedure. Vibrating thin flat plate as the sound source can be treated in terms of the fluid particle velocity on the plate surfaces. All these features are investigated through a number of numerical examples and the performances are satisfactory ones.

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A tutorial on the parabolic equation (PE) model used for long range sound propagation in the atmosphere

Cited by 117 publications

References 3 publications

Influence of short-term meteorological variations over flat grass-covered ground on noise level fluctuation and application of mean meteorological data to noise prediction

Influence of short-term meteorological variations over flat grass-covered ground on noise level fluctuation and application of mean meteorological data to noise prediction

Environmentally adaptive acoustic transmission loss prediction in turbulent and nonturbulent atmospheres

Finite element simulation of sound propagation concerning meteorological conditions

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