Controlled multipulse loading with a stuffed striker in classical split Hopkinson pressure bar testing
Bibliographic record
Abstract
Controlled multipulse loading in classical split Hopkinson pressure bar (SHPB) testing is highly desirable for investigating loading history dependent phenomena but rarely explored. Here, we present a novel technique to achieve controlled multipulse loading in SHPB testing with a stuffed striker. This stuffed striker consists of a striker tube, and a striker bar and a gap enclosed inside the tube; upon impact on the input bar, it can produce two separated loading pulses. The gap controls the delay of the second pulse with respect to the first pulse, and the pulse separation (dwell time) can be continuously tuned from zero to hundreds of microseconds. The combination of the stuffed striker and the Lindholm technique [J. Mech. Phys. Solids 12, 317 (1964)] allows for controlled multipulse loading with triple or more pulses. We have validated the working principle of this technique with experiments, and demonstrated its feasibility and flexibility for acquiring relevant dynamic data with double- and triple-pulse loading on polycrystalline Cu. This precisely controlled multipulse loading technique is readily implementable and can be applied to investigating the dynamic response of a wide range of materials.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".