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Acceleration of plates using non-conventional explosives heavily-loaded with inert materials

2014· article· en· W2075760404 on OpenAlexaff
Julien Loiseau, Oren E. Petel, Justin Huneault, Matthew Serge, David L. Frost, Andrew Higgins

Bibliographic record

VenueJournal of Physics Conference Series · 2014
Typearticle
Languageen
FieldEngineering
TopicEnergetic Materials and Combustion
Canadian institutionsCarleton UniversityMcGill University
Fundersnot available
KeywordsExplosive materialDetonationAccelerationMaterials scienceInertNitromethaneMechanicsDetonation velocityTerminal velocityThermalisationThermodynamicsComposite materialPhysicsChemistryClassical mechanics

Abstract

fetched live from OpenAlex

The detonation behavior of high explosives containing quantities of dense additives has been previously investigated with the observation that such systems depart dramatically from the approximately "gamma law" behavior typical of conventional explosives due to momentum transfer and thermalization between particles and detonation products. However, the influence of this non-ideal detonation behavior on the divergence speed of plates has been less thoroughly studied and existing literature suggests that the effect of dense additives cannot be explained solely through the straightforward application of the Gurney method with energy and density averaging of the explosive. In the current study, the acceleration history and terminal velocity of aluminum flyers launched by packed beds of granular material saturated by amine-sensitized nitromethane is reported. It was observed that terminal flyer velocity scales primarily with the ratio of flyer mass to mass of the explosive component; a fundamental feature of the Gurney method. Velocity decrement from the addition of particles was only 20%-30% compared to the resulting velocity if propelled by an equivalent quantity of neat explosive.

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 score0.084
Threshold uncertainty score0.349

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 communication0.0000.001
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 head0.019
GPT teacher head0.211
Teacher spread0.192 · 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

Citations3
Published2014
Admission routes1
Has abstractyes

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