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Record W2122504890 · doi:10.2514/6.2005-361

Metal Combustion in High-Speed Flow

2005· article· en· W2122504890 on OpenAlexaff
Vincent Tanguay, Patrick Batchelor, Ramzi El-Saadi, Andrew Higgins

Bibliographic record

Venue43rd AIAA Aerospace Sciences Meeting and Exhibit · 2005
Typearticle
Languageen
FieldEngineering
TopicCombustion and Detonation Processes
Canadian institutionsMcGill University
Fundersnot available
KeywordsCombustionFlow (mathematics)Materials scienceComputer scienceEnvironmental scienceMechanicsChemistryPhysics

Abstract

fetched live from OpenAlex

*† ‡ § The combustion of reactive metals (aluminum, magnesium, titanium, and zirconium) in a supersonic flow of shock-heated oxygen is investigated. The material samples are cylinders of sufficiently large size (1-3 mm diameter) that the interior of the samples remain cool for the duration of the experiment. The flow of oxygen is heated and accelerated by a strong normal shock wave driven by the detonation of an explosive charge at one end of a 1.2-mlong tube filled with gaseous oxygen; the samples were located at the other end of the tube. The shock Mach number ranged from Mach 5 to 9, generating supersonic (M > 2) flows with static temperatures of 1300 K to 3600 K. Intense surface luminosity was observed on the zirconium and titanium samples for even the weakest shock waves generated. Aluminum and magnesium were seen to be less reactive, requiring a stronger shock before the onset of reaction. The mass of material removed from the samples by reaction with the flow of oxidizer was measured as a function of shock strength. The luminosity and mass removal were shown to be combustion (as opposed to ablation followed by decoupled chemical reaction) by performing a control experiment with pure nitrogen, in which no mass removal or luminosity was observed on the sample surfaces. Nomenclature Eo

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.494

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.011
GPT teacher head0.224
Teacher spread0.213 · 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 designSimulation or modeling
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

Citations6
Published2005
Admission routes1
Has abstractyes

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