Particle jet formation during explosive dispersal of solid particles
Bibliographic record
Abstract
Previous experimental studies have shown that when a layer of solid particles is explosively dispersed, the particles often develop a non-uniform spatial distribution. The instabilities within the particle bed and at the particle layer interface likely form on the timescale of the shock propagation through the particles. The mesoscale perturbations are manifested at later times in experiments by the formation of coherent clusters of particles or jet-like particle structures, which are aerodynamically stable. A number of different mechanisms likely contribute to the jet formation including shock fracturing of the particle bed and particle-particle interactions in the early stages of the dense gas-particle flow. Aerodynamic wake effects at later times contribute to maintaining the stability of the jets. The experiments shown in this fluid dynamics video were carried out in either spherical or cylindrical geometry and illustrate the formation of particle jets during the explosive dispersal process. The number of jet-like structures that are generated during the dispersal of a dry powder bed is compared with the number formed during the dispersal of the same volume of water. The liquid dispersal generates a larger number of jets, but they fragment and dissipate sooner. When the particle bed is saturated with water and explosively dispersed, the number of particle jets formed is larger than both the dry powder and pure water charges. More extensive experiments that explore the effect of particle size, density and the mass ratio of explosive to particles on the susceptibility for jet formation are reported in Frost et al. (Proc. of 23rd ICDERS, Irvine, CA, 2011).
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".