Acoustics in Moving Inhomogeneous Media

Ostashev, Vladimir E.; Wilson, D. Keith

doi:10.1201/b18922

Cited by 144 publications

(202 citation statements)

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“…The method uses the effective speed of sound approach wherein the moving atmosphere is replaced by a hypothetical motionless medium with an effective sound speed c eff ¼ c þ v x , where v x is the wind velocity component along the direction of propagation between the source and receiver. 42 In this study, the speed of sound is kept constant [c(x, z) ¼ 340 m/s] in the whole domain, and the propagation phenomena (i.e., refraction, diffraction, scattering) is solely due to the wind speed and its fluctuations around the wind turbine. This approach is representative of a neutrally stratified atmosphere on a day with high wind or thick cloud layers.…”

Section: Sound Propagation Modelmentioning

confidence: 99%

Consistent modelling of wind turbine noise propagation from source to receiver

Barlas

Zhu

Shen

et al. 2017

The Journal of the Acoustical Society of America

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The unsteady nature of wind turbine noise is a major reason for annoyance. The variation of far-field sound pressure levels is not only caused by the continuous change in wind turbine noise source levels but also by the unsteady flow field and the ground characteristics between the turbine and receiver. To take these phenomena into account, a consistent numerical technique that models the sound propagation from the source to receiver is developed. Large eddy simulation with an actuator line technique is employed for the flow modelling and the corresponding flow fields are used to simulate sound generation and propagation. The local blade relative velocity, angle of attack, and turbulence characteristics are input to the sound generation model. Time-dependent blade locations and the velocity between the noise source and receiver are considered within a quasi-3D propagation model. Long-range noise propagation of a 5 MW wind turbine is investigated. Sound pressure level time series evaluated at the source time are studied for varying wind speeds, surface roughness, and ground impedances within a 2000 m radius from the turbine.

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Section: Sound Propagation Modelmentioning

confidence: 99%

Consistent modelling of wind turbine noise propagation from source to receiver

Barlas

Zhu

Shen

et al. 2017

The Journal of the Acoustical Society of America

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“…The smaller value corresponds to the range of values reported in Ref. [15] near (flat) ground during a summer's day. Note that modelling temperature related turbulence is not fully consistent with the previously made assumption of a purely logarithmic wind speed profile; however, the main goal here is studying the effect of a scattering atmosphere.…”

Section: Turbulencementioning

confidence: 99%

“…The Kolmogorov turbulence spectrum can be considered as a universally applicable and realistic representation of the atmosphere in the inertial subrange [15]. Eight length scales are considered, ranging from 1.5 cm to 2 m, fitting in between the grid resolution and the extent of the simulation domain.…”

Section: Turbulencementioning

confidence: 99%

Meteorological effects on the noise reducing performance of a low parallel wall structure

et al. 2017

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“…The appropriateness of the log-normal distribution to weak scattering is discussed in many texts on wave propagation in random media (Flatté et al 1979, Rytov et al 1989, Andrews and Phillips 2005, Ostashev and Wilson 2015. It follows from the Rytov approximation for the log-amplitude and the assumption that received signal consists of many independent additive contributions.…”

Section: B Log-normal (Weak Scattering In the Rytov Approximation)mentioning

confidence: 99%

Scattered signal distributions, parametric uncertainties, and Bayesian sequential updating

Wilson

Hart

Pettit

et al. 2017

Proceedings of Meetings on Acoustics

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A variety of probability density functions (pdfs) have been proposed for scattered signals, which have varying analytical advantages and ranges of physical applicability. We discuss here modeling with a compound pdf, in which a basic pdf, describing the underlying scattering process, has uncertain parameters or is modulated by variability in the environment. The parameters of the modulating pdf are termed hyperparameters. Some previous examples of compound formulations include the K-distribution, for which strong scattering (exponential pdf) is modulated by a gamma pdf for the mean signal power, and scattering by intermittent turbulence, for which strong scattering is modulated by a log-normal pdf for the structure-function parameter. We describe some alternative formulations, including strong scattering modulated by a gamma pdf for the inverse mean power, and Rytov (log-normal) scattering modulated by a normal pdf for the log-mean of the signal. These lead to relatively simple marginalized signal power distributions (Lomax and log-normal, respectively). Furthermore, the basic scattered signal pdf may be viewed as a likelihood function in which the modulating pdf is the Bayesian conjugate prior. Hence the hyperparameters of the modulating process can be refined by sequential Bayesian updating as additional transmission data become available.

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Acoustics in Moving Inhomogeneous Media

Cited by 144 publications

References 0 publications

Consistent modelling of wind turbine noise propagation from source to receiver

Consistent modelling of wind turbine noise propagation from source to receiver

Meteorological effects on the noise reducing performance of a low parallel wall structure

Scattered signal distributions, parametric uncertainties, and Bayesian sequential updating

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