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Two-point wavepacket modelling of jet noise.

I A Maia1, P Jordan1, A V G Cavalieri2

  • 1Institut PPRIME, CNRS, Université de Potiers, ENSMA, Poitiers 86036, France.

Proceedings. Mathematical, Physical, and Engineering Sciences
|August 20, 2019
PubMed
Summary

This study models jet noise using a kinematic wavepacket approach. Accurately representing wavepacket shape and coherence decay is crucial for predicting sound pressure levels, matching experimental data at low Strouhal numbers.

Keywords:
jet noisekinematic modellingwavepackets

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Area of Science:

  • Acoustics
  • Fluid Dynamics
  • Computational Aeroacoustics

Background:

  • Jet noise is a significant challenge in aerospace engineering.
  • Accurate prediction of sound pressure levels requires sophisticated modeling.
  • Turbulent jets generate noise through complex flow structures.

Purpose of the Study:

  • To develop and validate a kinematic wavepacket model for jet noise.
  • To identify key physical parameters influencing radiated sound.
  • To explore low-rank modeling using Spectral Proper Orthogonal Decomposition (SPOD).

Main Methods:

  • Utilized a kinematic wavepacket model based on two-point statistics.
  • Educated model parameters from large-eddy simulation data of a Mach 0.4 jet.
  • Compared model-predicted sound pressure levels with experimental acoustic data.
  • Applied SPOD to the model source to identify dominant acoustic traits.

Main Results:

  • The model accurately predicts sound pressure levels at low Strouhal numbers and specific polar angles when parameters are well-defined.
  • Coherence decay and wavepacket envelope shape are critical for accurate sound prediction.
  • A few SPOD modes effectively capture the essential acoustic characteristics of the jet noise source.

Conclusions:

  • The kinematic wavepacket model, when properly parameterized, provides a reliable tool for jet noise prediction.
  • Understanding wavepacket dynamics, particularly coherence and envelope, is vital for noise reduction strategies.
  • SPOD offers a promising approach for developing reduced-order models for efficient aeroacoustic analysis.