A Nondimensional Parameterization for Sound Propagation in the Atmosphere

Abstract

Parabolic equation (PE) techniques have been successfully used to obtain numerical solutions of sound pressure attenuation in which sound propagation is affected by turbulence and vertical gradients in wind and temperature. The PP models generally produce accurate attenuation values, but the execution time is excessive for applications when near real-time results are required. To obtain sound level attenuation predictions at selected location more quickly, we are developing an artificial neural network. As a first step in this effort, the PP and boundary conditions were modified to obtain a nondimensional version, written in the MATLAB code. This nondimensional version was developed to be used to train the artificial neural network because a fewer number of parameters (seven) would be required to be specified, resulting in a reduced number of model runs to develop the training algorithm. This report documents the derivation of the appropriate equations that are used in the modified (nondimensional) version of the acoustic propagation model. In addition, graphical data are provided that identify the sensitivity of sound pressure attenuation to each of the seven nondimensional parameters.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2003
Accession Number
ADA413843

Entities

People

  • D. K. Wilson
  • Michael Mungiole

Organizations

  • United States Army Research Laboratory

Tags

Communities of Interest

  • Advanced Electronics
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Acoustic Propagation
  • Algorithms
  • Atmospheres
  • Attenuation
  • Boundaries
  • Data Science
  • Difference Equations
  • Differential Equations
  • Equations
  • Frequency
  • Ideal Gas Law
  • Information Science
  • Military Research
  • Neural Networks
  • Sound Pressure
  • Wind
  • Wind Direction

Readers

  • Atmospheric Science / Meteorology, specifically Wind Wave Turbulence.
  • Computer Science.
  • Wave Propagation and Nonlinear Chaotic Dynamics.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Neural Networks