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Automatic source localization and spectra generation from sparse beamforming maps.

A Goudarzi1, C Spehr1, S Herbold2

  • 1Institute of Aerodynamics and Flow Technology, German Aerospace Center (DLR), Göttingen, Germany.

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Summary
This summary is machine-generated.

This study introduces automated methods for identifying aeroacoustic sources in beamforming data. These techniques overcome manual region definition, improving the analysis of sound sources in complex aerodynamic flows.

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Area of Science:

  • Aeroacoustics
  • Acoustic Imaging
  • Signal Processing

Background:

  • Beamforming is crucial for investigating aeroacoustic phenomena, generating high-dimensional data.
  • Current methods require manual definition of regions of interest (ROIs) for spectral analysis.
  • This limits efficiency and objectivity in identifying aeroacoustic sources.

Purpose of the Study:

  • To develop and evaluate automated methods for identifying aeroacoustic sources in sparse beamforming maps.
  • To enable automatic extraction of spectra corresponding to identified sources.
  • To overcome the limitations of manual ROI definition in aeroacoustic analysis.

Main Methods:

  • Two novel methods for automated aeroacoustic source identification were developed.
  • Method 1 utilizes the spatial normal distribution of broadband sources in beamforming maps.
  • Method 2 employs hierarchical clustering techniques for source localization.

Main Results:

  • Both automated methods successfully identified aeroacoustic sources in wind tunnel measurements.
  • The methods accurately predicted source existence, location, and spatial probability.
  • Automated ROI determination proved robust against statistical noise.

Conclusions:

  • Automated identification and spectral extraction of aeroacoustic sources are feasible.
  • The proposed methods offer an objective and efficient alternative to manual ROI definition.
  • These advancements enhance the analysis of aeroacoustic phenomena using beamforming data.