4.7 Article

Statistical analysis of sound level predictions in refracting and turbulent atmospheres

期刊

APPLIED ACOUSTICS
卷 185, 期 -, 页码 -

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.apacoust.2021.108426

关键词

Outdoor sound propagation; Atmospheric refraction; Turbulent scattering; Uncertainty

资金

  1. Defence Science and Technology Laboratory (Dstl) UK
  2. EPSRC CASE
  3. EPSRC [EP/R022275/1]
  4. EPSRC [EP/R022275/1] Funding Source: UKRI

向作者/读者索取更多资源

This paper fills a knowledge gap by statistically analyzing the atmospheric effects on sound pressure levels, finding that ground type and atmospheric state significantly impact the probability distribution of sound pressure levels. Under certain conditions, distributions can be remarkably similar, highlighting the increasing importance of considering meteorological effects when predicting sound pressure level distributions.
There is a lack of data on the statistical properties of the sound pressure level fluctuations which are caused by uncertainties in source/receiver heights, range, ground conditions and atmospheric effects. This paper contributes to this knowledge gap through statistical analysis of the atmospheric effects on sound pressure levels performed with a Green's function parabolic equation model and a turbulence scattering model. The probability density functions for the sound pressure level relative to free field propagation are calculated for the source and receiver close to the ground, up to 1 km propagation distance, for typical annual meteorological conditions and two types of ground. It is found that the ground type and refractive state of the atmosphere have a pronounced effect on the probability distribution. Under some conditions and at some frequency intervals, distributions can be remarkably similar. Furthermore, accounting for meteorological effects become increasingly important with range when predicting sound pressure level distributions. This work contributes to a better understanding of the role of uncertainties in outdoor sound propagation that might serve for improved accuracy of statistical source localisation and characterisation methods based on parameter inversion. (C) 2021 Elsevier Ltd. All rights reserved.

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