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Record W4317597172 · doi:10.2514/6.2023-0702

Dynamics and properties of ignition kernel generated by a helicopter sunken fire ignitor

2023· article· en· W4317597172 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAIAA SCITECH 2023 Forum · 2023
Typearticle
Languageen
FieldChemical Engineering
TopicAdvanced Combustion Engine Technologies
Canadian institutionsSafran Electronics (Canada)
Fundersnot available
KeywordsIgnition systemCombustionAerospace engineeringSchlierenKernel (algebra)Nuclear engineeringEnvironmental scienceComputer scienceIGNITORVolume (thermodynamics)Automotive engineeringMechanical engineeringSimulationProcess engineeringEngineeringPhysicsChemistry

Abstract

fetched live from OpenAlex

View Video Presentation: https://doi.org/10.2514/6.2023-0702.vid Environmental requirements imposed stringent measures such as the adoption of bio fuels as well as reduction of fuel consumption. As these are integrated, reliable engine operation for in flight re-ignition and high-altitude ground ignition must be ensured. Flame development and propagation in annular combustion chambers strongly rely on kernel characteristics and its interaction with a surrounding environment typically challenging to ignite. Therefore, a sharper understanding of kernel properties for detrimental conditions is sought. The main objective of the present study is to characterize ignition kernel properties using a real helicopter igniter for characteristic times for which fine and detailed information are lacking. These are indeed sought to properly initialize numerical simulations. Two diagnostics have been adopted for characterization: microcalorimetry and schlieren visualization, showing decreasing energy transfer efficiency and growing final volume as initial pressure decreases.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.512
Threshold uncertainty score0.667

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.014
GPT teacher head0.223
Teacher spread0.209 · 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