An Open-Source Algorithm for Correcting Stress Wave Dispersion in Split-Hopkinson Pressure Bar Experiments - Arthur Van Lerberghe; Kin Shing O. Li; Andrew D. Barr; Sam D. Clarke (Sensors)
Stress wave dispersion can result in the loss or distortion of critical high-frequency data during high-strain-rate material tests or blast loading experiments. The purpose of this work is to demonstrate the benefits of correcting stress wave dispersion in split-Hopkinson pressure bar experiments under various testing situations. To do this, an innovative computational algorithm, <semantics>SHPB<mtext>_</mtext>Processing.py</semantics>, is created. Following the operational run through of <semantics>SHPB<mtext>_</mtext>Processing.py</semantics>’s capabilities, it is used to process test data acquired from split-Hopkinson pressure bar tests on aluminium, sand and kaolin clay samples, under various testing conditions. When comparing dispersion corrected and simple time shifting data obtained from SHPB experiments, accounting for dispersion removes spurious oscillations and improves the inferred measurement at the front of the specimen. The precision of the stress and strain results gathered from its application emphasises its importance through the striking contrast between its application and omission. This has a significant impact on the validity, accuracy and quality of the results. As a result, in the future, this tool can be utilised for any strain rate testing situation with cylindrical bars that necessitates dispersion correction, confinement, or stress equilibrium analysis.