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Related Concept Videos

Sound as Pressure Waves01:17

Sound as Pressure Waves

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Sound waves, which are longitudinal waves, can be modeled as the displacement amplitude varying as a function of the spatial and temporal coordinates. As a column of the medium is displaced, its successive columns are also displaced. As the successive displacements differ relatively, a pressure difference with the surrounding pressure is created. The gauge pressure varies across the medium.
The pressure fluctuation depends on the difference in displacements between the successive points in the...
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An Open-Source Algorithm for Correcting Stress Wave Dispersion in Split-Hopkinson Pressure Bar Experiments.

Arthur Van Lerberghe1, Kin Shing O Li1, Andrew D Barr1

  • 1School of Mechanical, Aerospace & Civil Engineering, University of Sheffield, Sheffield S1 3JD, UK.

Sensors (Basel, Switzerland)
|January 11, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces SHPB_Processing.py to correct stress wave dispersion in split-Hopkinson pressure bar (SHPB) tests. Correcting dispersion enhances data accuracy and validity in high-strain-rate material testing.

Keywords:
dispersion correctionhigh-strain-rate testingmaterial applicationsopen-source algorithmsignal processingsplit-Hopkinson pressure barstress waves

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Area of Science:

  • Materials Science
  • Mechanical Engineering
  • Wave Propagation

Background:

  • Stress wave dispersion distorts high-frequency data in high-strain-rate tests.
  • Accurate material characterization requires addressing wave dispersion effects.

Purpose of the Study:

  • Demonstrate the benefits of correcting stress wave dispersion in split-Hopkinson pressure bar (SHPB) experiments.
  • Introduce and validate a new computational algorithm for dispersion correction.

Main Methods:

  • Developed an innovative computational algorithm: SHPB_Processing.py.
  • Processed SHPB test data from aluminium, sand, and kaolin clay samples.
  • Compared dispersion-corrected data with simple time-shifting methods.

Main Results:

  • Dispersion correction removed spurious oscillations in SHPB data.
  • Improved measurement accuracy at the specimen's front was observed.
  • Enhanced precision in stress and strain results was achieved.

Conclusions:

  • The SHPB_Processing.py algorithm significantly improves the validity, accuracy, and quality of high-strain-rate test results.
  • This tool is applicable to various strain rate testing scenarios involving cylindrical bars.
  • Future use cases include dispersion correction, confinement analysis, and stress equilibrium analysis.