Non-Gurney scaling of explosives heavily loaded with dense inert additives
Bibliographic record
Abstract
For most high explosives, the ability to accelerate material to some terminal velocity scales with the ratio of material-mass to charge-mass (M/C) according to the Gurney model. In planar geometry, the Gurney model accurately estimates the terminal velocity of the driven material until M/C is reduced to 0.2 or lower. Below this value, gasdynamic departures from the assumptions of the model result in under-prediction of the material terminal velocity. In the present study, a modified Gurney model was used to predict the scaling of flyer velocity with M/C for explosives heavily diluted with either low density (3M K1 glass microballoons—GMBs), or high density (steel beads) diluent. The modified model accounted for explosive energy being transferred to accelerate the diluent. The model was compared with a series of flyer experiments propelled using either Poly(methyl methacrylate)-gelled nitromethane (96% NM/4% PMMA) diluted with 10% GMBs by mass or diethylenetriamine-sensitized liquid nitromethane saturating a packed bed of steel particles. Both grazing and normally incident detonations of the test mixtures were used to propel the flyers. The Gurney model accurately predicted the terminal velocity for the gelled NM/GMB mixture although it did not account for the contribution from the detonation shock nor the initiating slapper plate. The model failed to predict flyer velocity for the NM/steel mixture. The propulsive efficiency of this mixture increased with M/C relative to the baseline explosive so that the model under-predicted velocity for small M/C but over-predicted for large M/C.
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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) | 0.001 | 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".