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Record W4407416289 · doi:10.2514/6.2025-0155

Aeronautic Igniter Abilities in Low Pressure Lean Turbulent Mixtures

2025· article· en· W4407416289 on OpenAlexaff
Alessandra Matino, Julien Sotton, Marc Bellenoue, Christophe Viguier

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicCombustion and flame dynamics
Canadian institutionsSafran Electronics (Canada)
Fundersnot available
KeywordsTurbulenceComputer scienceEnvironmental scienceAutomotive engineeringMechanicsEngineeringPhysics

Abstract

fetched live from OpenAlex

The characterization of real aeronautic igniter performance was observed to be fundamental to have a complete perspective of the energetic losses from the ignition unit to the gas medium. Equally speaking, kernel size and physical characteristics are necessary to model energy deposit and initialize numerical calculations for a loyal prediction of the ignition phase in GTE. In the pursuit of drawing nearer to this goal, this work aims to further advance investigating the early ignition phase of sunken fire igniters. The effect of a flowing methane-air premixed mixture over spark kernel characteristics is investigated for time delays of the order of the energy depositing phase. The velocity in the vicinity of the igniter is qualitatively explored by PIV diagnostic. Influence of initial pressure, composition and flow dynamics in terms of success rate and kernel evolution with time are assessed.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.112
Threshold uncertainty score0.350

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 head0.003
GPT teacher head0.197
Teacher spread0.194 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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