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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
Summary
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.
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.
