Suppression of jet formation during explosive dispersal of concentric particle layers
Bibliographic record
Abstract
The explosive dispersal of a spherical layer of solid particles surrounding a high-explosive charge is investigated. The shock-consolidated particle layer fractures into discrete fragments which move radially outwards shedding particles in their wakes and forming jet-like structures. The tendency to form jets is partially dependent on the material properties of the particles with brittle ceramic particles as well as soft, ductile metal particles being more susceptible to forming jets, whereas particles that are comprised of materials with moderate hardness, high compressive strength and high toughness are much less prone to jet formation. During the explosive dispersal of binary mixtures of “jetting” and “non-jetting” particles, the particles rapidly segregate. The jetting response present in these binary mixtures persists to volume fractions as low as 10% with respect to the “jetting” species. In the present study, we examine the effect that concentrically layering the same two powder species, silicon carbide and steel shot, at varying volumetric ratios, has on the resulting particle dispersal. It is seen that through the inclusion of an inner layer of sufficient thickness of “non-jetting” particles (steel shot), the strength of the initial shock wave can be attenuated and the jetting response of a typically “jetting” material (silicon carbide) can be suppressed. Measurement of the velocity of the two types of particles shows that the velocity, normalized by the Gurney velocity, is not a function of the volume fraction of the particles or the geometrical arrangement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".