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Record W2009579462 · doi:10.1177/0954410013495638

Reduced agglomeration in solid propellants containing porous aluminum

2013· article· en· W2009579462 on OpenAlexaff
Yinon Yavor, Valery Rosenband, Alon Gany

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering · 2013
Typearticle
Languageen
FieldEngineering
TopicEnergetic Materials and Combustion
Canadian institutionsMcGill University
Fundersnot available
KeywordsAgglomerateEconomies of agglomerationMaterials scienceAluminiumPropellantPorosityLaser ignitionParticle sizeParticle (ecology)CombustionIgnition systemChemical engineeringComposite materialMineralogyMetallurgyChemistry

Abstract

fetched live from OpenAlex

The effects of using porous aluminum particles in solid propellants were studied, with emphasis on the agglomeration phenomena. Burning strands containing either regular (as-received) or porous aluminum were photographed by a high-speed camera, and particulate combustion products were analyzed in a laser particle analyzer. Results obtained from experiments conducted in a pressure-range of 1–34 atmospheres show that porous aluminum particles produce smaller agglomerates than regular aluminum. The median diameter of agglomerates resulting from porous aluminum reached, on average, 70% of the one originating from regular aluminum. This reduction in agglomerate diameter corresponds to a substantial volume (and hence, mass) decrease of approximately 65%. It is assumed that the high-specific area of the porous aluminum particles (10–18 m 2 /g, similar to that of nano-Al) results in high reactivity, leading to shorter ignition time and hence to the formation of smaller agglomerates.

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 score0.277
Threshold uncertainty score0.625

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 head0.190
Teacher spread0.183 · 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

Citations17
Published2013
Admission routes1
Has abstractyes

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