Jetting instabilities of particles from explosive dispersal
Bibliographic record
Abstract
The formation of post-detonation ‘particle’ jets is widely observed in many problems associated with particle dispersal from explosives. This paper analyzes experimental observations to examine the mechanism for formation and growth of such particle jetting instabilities, and to propose a model to address the issues of jetting growth at a macroscopic level. The experiments involve cylindrical charges with a range of central explosive masses for dispersal of dry solid powder, pure liquid, or a hybrid mixture of solid powder and liquid. The results demonstrate that the jets form very early, and that the number of jets is dependent on shock pressure at the charge perimeter, within the range of particle sizes studied. The jetting instabilities may therefore be initiated by shock interaction with the dense solid particle interfaces near the charge surface, followed by early jet growth through transverse particle motion from the interaction of shocked and turbulent wake flow around the particles. From the experiments, a macroscopic model is proposed, in which an edge perturbation on a scale proportional to the number of jets as a function of shock pressure is employed, and the subsequent transverse particle motion is controlled by an attraction function. The model is implemented in the Chinook hydrocode, which is capable of modeling the initiation and growth of particle jetting structures in large-scale dispersal, and the results are validated against experiments.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".