Particle segregation during explosive dispersal of binary particle mixtures
Bibliographic record
Abstract
The explosive dispersal of a layer of solid particles surrounding a spherical high explosive charge generates a turbulent, multiphase flow. The shock-compacted particle layer typically fractures into discrete fragments which move radially outwards on ballistic trajectories. The fragments shed particles in their wakes forming jet-like structures. The tendency to form jets depends on the mass-ratio of the particles to explosive and the type of particles. Brittle or soft, ductile particles are more susceptible to forming jets during compaction and dispersal, whereas particles that are comprised of material with moderate hardness, high compressive strength and high toughness are much less prone to forming jets. Experiments have been carried out to determine the degree of particle segregation that occurs during the explosive dispersal of a uniform, binary mixture containing both “jetting” (silicon carbide) and “non-jetting” (steel) particles with various mass fractions of each particle type. During the dispersal of mixtures that contain predominantly non-jetting (steel) particles, the steel particles form a stable layer whereas the jetting (silicon carbide) particles rapidly segregate and form jets which are confined within the shell of steel particles. As the fraction of silicon carbide particles increases, the jet structures dominate the particle motion and the steel particles are entrained into the jet structures.
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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) | 0.000 | 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".