Finite Element Modelling of a Modified Kolsky Bar Developed for High Strain Rate Testing of Elastomers
Bibliographic record
Abstract
Elastomers are finding a wide variety of dynamic applications in aerospace, automobile and biomedical industries. The response of these complex material is based on the loading conditions and the strain rate at which the loading is applied. To suit the designer’s requirement, there is an ever increasing need to characterize this application specific, dynamic behavior under high strain rates. The Kolsky bar apparatus, also known as the Split Hopkinson Bar, is the most common apparatus used to test engineering materials at strain rates between 100/s and 10000/s. In this paper a modified Kolsky bar to characterize soft material is numerically modeled using Finite Element Method. The focus of the study is to numerically analyze the modifications made to a conventional Kolsky bar to specifically test nonlinear hyperelastic, soft materials. The challenge for testing low strength materials is the impedance mismatch between the bar and specimen interfaces, which results in a very weak distorted signal. One of the solution is to use a hollow transmission bar instead of solid one. With the use of FEM it can be numerically verified that using a hollow bar increases the amplitude of the transmitted signal up to several times. It is known that the rise time of the elastic wave can be increased by using a copper pulse shaper. Different dimensions of pulse shaper are modeled and the effect on the incident pulse is analyzed. The main aim of this study is to provide a detailed numerical analysis on the testing parameters, and to model one way wave propagation in Kolsky bar experiment for hyperelastic materials. The constitutive equations used to model the parts of the apparatus are also discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".