Hetero‐blast from a structural reactive material cylinder under explosive loading
Bibliographic record
Abstract
Abstract A structural reactive material (SRM) cylinder is considered here as a limiting case of a dense metallic energetic system in which a mixture of metal particles is consolidated to the theoretical maximum density excluding porosity, to possess both high energy density and mechanical strength. Dynamic fragmentation and free‐field explosion of a 103 mm inner diameter SRM cylinder charge is experimentally studied, with a wall thickness varying in a range of metal‐to‐explosive mass ratioM/C=1.3 to 4.0. Under explosive loading, the SRM cylinder produces a designated fragment size distribution divided into two groups: fine fragments with sizes on the order of 102 μm and below, and coarse fragments with sizes on the order ranging between 100‐101 mm. Prompt detonation shock‐induced reaction (DSIR) of the expanding cloud of high‐concentration fine fragments supplements the energy to enhance the primary blast as it propagates, while the coarse fragments form a high‐speed, high‐concentration metal momentum flux crossing the fireball and blast front to contribute to the total impulse loading to a nearby structure. Rapid impact‐induced reaction (IIR) of the secondary fragments from high‐speed coarse SRM fragments further enhances the reflected blast loading or generates a high interior explosion pressure as fragments perforate into the structure. The above distinctive characteristics of a unique hetero‐blast are coupled effectively in the near‐field range.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".