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PLATE DENT TESTS AND SEDIMENTATION OF PARTICLES IN MELT-CAST EXPLOSIVES

2013· article· en· W1977246467 on OpenAlexaff
Patrick Brousseau, Serge Trudel, Pascal Beland

Bibliographic record

VenueInternational Journal of Energetic Materials and Chemical Propulsion · 2013
Typearticle
Languageen
FieldEngineering
TopicEnergetic Materials and Combustion
Canadian institutionsDefence Research and Development Canada
Fundersnot available
KeywordsExplosive materialMaterials scienceInertCylinderSedimentationComposite materialGeologyMechanical engineeringEngineeringChemistry

Abstract

fetched live from OpenAlex

A short experimental study demonstrated that the plate dent test is very sensitive to the last few centimeters of explosives at the bottom of the cylinders. The tests were performed by simply detonating an explosive cylinder with a small thickness (12.7−25.4 mm) of a different explosive (faster, slower, inert) at the bottom. The study will present how those small thicknesses influence the dent depth and hence the reported performance. Cylinders of explosives were cast and then cut to determine the extent of sedimentation of the HMX particles. Densities were taken at various places and concentrations of HMX were extrapolated from those. It was found that there was a difference of 22% in the percentage of HMX from the bottom and the top of the cylinder (66% vs. 44%; theoretical average was 52.8%). The explosive at the bottom was then significantly different and more powerful than the one at the top. The study will also demonstrate how the situation can be worse in real artillery shells. Given the results of the plate dent experiments reported before, it will be demonstrated how in theory one could be misled on the plate dent test by letting particles settle. Simple precautions can be taken to eliminate this variable and to ensure that the results of the plate dent test are meaningful. It also serves as a reminder that the composite explosives that we test, especially the melt-cast explosives, have large variations in their composition from sample to sample.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.259

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.214
Teacher spread0.207 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2013
Admission routes1
Has abstractyes

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