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Record W4391302582 · doi:10.2514/6.2024-0102

Influence of pressure and mixture composition on ignition kernel properties in inert and reactive configuration

2024· article· en· W4391302582 on OpenAlexaff
Alessandra Matino, Julien Sotton, Marc Bellenoue, Christophe Viguier

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicCombustion and Detonation Processes
Canadian institutionsSafran Electronics (Canada)
Fundersnot available
KeywordsIgnition systemSchlierenInertCombustionMaterials scienceKernel (algebra)Analytical Chemistry (journal)Volume (thermodynamics)Chemical engineeringChemistryThermodynamicsMechanicsMathematicsOrganic chemistryPhysics

Abstract

fetched live from OpenAlex

The impact of initial pressure and mixture composition was investigated in this study to observe their influence on kernel properties during energy depositing using a sunken fire igniter. Experiments were conducted in a cylindrical combustion chamber using high-speed Schlieren and direct visualizations. Comparison with reference tests performed in pure nitrogen highlighted the influence of composition variation on kernel volume and surface at the end of energy depositing (t = 130 µs). The effect of equivalence ratio was observed to be enhanced by lower pressure conditions. A dominant effect of pressure confirms results from previous studies. Filtered plasma chemiluminescence performed through direct visualization showed a negligible effect of composition and pressure during the first instants of kernel generation (~ 30 µs). Timing of intervening chemical reactions is traced comparing inert and reactive tests. This was done using two approaches. Specifically, the aim was to identify these variations and determine their occurrence in relation to the duration of energy depositing. It was observed that these start appearing already during energy depositing.

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 score0.222
Threshold uncertainty score0.164

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 head0.008
GPT teacher head0.209
Teacher spread0.200 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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