Impact induced detonation of cased explosive
Bibliographic record
Abstract
Fragments of aluminium impacting on Composition B explosives encased in rolled homogenous armour (RHA) steel were investigated using the explicit nonlinear finite element method. The investigation focused on shock to detonation simulations of Composition B, with the objective of determining the critical velocity which would generate a shock wave strong enough to cause detonation of the explosive and the resulting pressure profile of the detonation wave. Detonation scenarios at low, intermediate, high impact velocity were investigated. It was observed that a) at intermediate velocities detonation was due to the development of localized hot spots caused by the compression of the explosive from the initial shockwave; b) at high impact velocity, initiation of the explosive was caused by the initial incident wave behind the top casing/explosive interface. c) At low impact velocity, initiation of the explosive may be caused by the increased pressure of reflecting waves against the surfaces of the explosive casing. This case served to show the importance of capturing all confining surfaces correctly as any surface enhance detonation. Advanced features of the simulation includes Arbitrary-Lagrangian-Eulerian (ALE) approach, the Elastic Plastic Hydrodynamic constitutive material model and the Ignition and Growth of Reaction in High Explosive eq uation of state (IGRHE-EOS) to account for the probability that the explosive may not detonate when impacted.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".