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3D Off-Grid Localization for Adjacent Cavitation Noise Sources Using Bayesian Inference.

Minseuk Park1, Sufyan Ali Memon1, Geunhwan Kim2

  • 1Department of Defense Systems Engineering, Sejong University, Seoul 05006, Republic of Korea.

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|March 11, 2023
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Summary
This summary is machine-generated.

This study introduces an efficient method for pinpointing propeller tip vortex cavitation (TVC) by separating nearby noise sources. The pairwise off-grid BSBL method accurately localizes cavitations with significantly reduced computational time.

Keywords:
adjacent noise sourcesincipient cavitationoff-gridsparse localization

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Area of Science:

  • Acoustics
  • Fluid Dynamics
  • Signal Processing

Background:

  • Propeller tip vortex cavitation (TVC) generates noise, complicating source separation.
  • Accurate localization of off-grid cavitations is crucial for noise source analysis.

Purpose of the Study:

  • To develop a computationally efficient sparse localization method for off-grid cavitations.
  • To improve the separation of adjacent noise sources from propeller tip vortex cavitation.

Main Methods:

  • A pairwise off-grid sparse Bayesian learning (BSBL) method was employed.
  • Two distinct grid sets with moderate intervals were used for redundant source representation.
  • Bayesian inference was utilized for iterative grid point updates.

Main Results:

  • The proposed pairwise off-grid BSBL method successfully separated adjacent off-grid cavitations.
  • The method demonstrated significantly reduced computational cost compared to conventional approaches.
  • Separation of adjacent off-grid cavitations using pairwise off-grid BSBL took 29 seconds, versus 2923 seconds for conventional off-grid BSBL.

Conclusions:

  • The pairwise off-grid BSBL method offers an efficient solution for localizing and separating propeller tip vortex cavitation.
  • This approach achieves high accuracy in cavitation separation with substantial computational savings.