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Record W2562884704

Detailed Ignition Sequence Studied with a Fast Infrared Camera

2015· article· en· W2562884704 on OpenAlexaboutno aff
Frédérick Marcotte, Éric Guyot, Joël Jean, Alain Fossi, Sophie Ringuette

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Vision and Imaging
Canadian institutionsnot available
Fundersnot available
KeywordsIgnition systemCombustorInfraredCombustionCombustion chamberVolume (thermodynamics)Process (computing)ThermographyNuclear engineeringAutomotive engineeringComputer scienceMaterials scienceOpticsEngineeringAerospace engineeringPhysicsChemistry
DOInot available

Abstract

fetched live from OpenAlex

In an effort to better understand the interaction between the many parameters affecting ignition in a typical gas turbine combustor, a detailed study was made possible with a new facility available at Universite Laval in collaboration with the companies Telops, Defence Research and Development Canada - Valcartier and Pratt & Whitney Canada. The installation is primarily focused on characterizing ignition process of different biofuel mixtures compared with the reference Jet-A fuel. The experimental setup is configured with a 75 mm (3 in) sapphire infrared optical access window looking directly into the combustion chamber. A Telops high speed and high performance infrared (IR) camera was used to characterize the ignition process. Large pulsations are observed before a steady combustion finally gets established for a successful ignition. It was possible to estimate the velocity of the expanding ignition volume that compared to typical flame speed measurements. Few IR videos and photos will be presented to visualize the full phenomena that would not be visible otherwise to the naked eye. Good correlation with temperature was also established to reflect the strong virtue of IR cameras to measure accurately emitted energy.

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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.888
Threshold uncertainty score0.268

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 communication0.0000.001
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 head0.054
GPT teacher head0.298
Teacher spread0.244 · 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
GenreMethods

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
Published2015
Admission routes1
Has abstractyes

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