MétaCan
Menu
Back to cohort
Record W3096051623 · doi:10.1063/12.0000829

Simulating the propulsive capability of explosives loaded with inert and reactive materials

2020· article· en· W3096051623 on OpenAlexaff
Quentin Pontalier, Jason Loiseau, A. W. Longbottom, David L. Frost

Bibliographic record

VenueAIP conference proceedings · 2020
Typearticle
Languageen
FieldEngineering
TopicEnergetic Materials and Combustion
Canadian institutionsRoyal Military College of CanadaMcGill University
Fundersnot available
KeywordsExplosive materialInertDetonationMaterials scienceMechanicsParticle (ecology)AccelerationEnergetic materialAluminiumPhase (matter)Reactive materialParticle sizeComposite materialChemistryPhysicsClassical mechanicsPhysical chemistry

Abstract

fetched live from OpenAlex

The acceleration ability (AA) of explosives diluted with inert (glass) or reactive (aluminum) particles is investigated numerically. Computations are carried out with a multiphase hydrocode to compare the velocity of flyer plates accelerated by rectangular explosive layers, with previous experimental results [1, 2]. The detonation of the explosive phase is modeled by a programmed burn function with the parameters scaled for the neat explosives without particles. The particle phase is assumed compressible and a simplified model for the reaction of the aluminum particles specifies the reaction time and overall particle energy release as two independent parameters. The explosive model employed accurately predicts the flyer velocities at low particle mass loadings (< 16%). At these mass loadings, a substantial amount of the total metal mass reacts on a timescale of tens of microseconds. Nevertheless, for higher particle loadings, calculations overpredict the experimental flyer velocities, suggesting that the explosive model must be scaled with the detonation properties of the non-ideal mixtures.

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 score0.008
Threshold uncertainty score0.307

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 head0.019
GPT teacher head0.203
Teacher spread0.185 · 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
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueAIP conference proceedingsSame topicEnergetic Materials and CombustionFrench-language works237,207