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Record W2092841823 · doi:10.1002/cjce.21674

A new reduced reaction mechanism of a surrogate fuel for kerosene

2012· article· en· W2092841823 on OpenAlexvenueaboutno aff
Wen Zeng, J.-T. Liu, Xiaoxiao Chen, Hong‐An Ma

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2012
Typearticle
Languageen
FieldChemical Engineering
TopicAdvanced Combustion Engine Technologies
Canadian institutionsnot available
FundersNational Science Foundation
KeywordsCombustionDecaneShock tubeCombustorJet fuelIgnition systemKeroseneReaction mechanismChemistryThermodynamicsChemical reactionKinetic energyMaterials sciencePhysical chemistryOrganic chemistryShock wavePhysicsCatalysis

Abstract

fetched live from OpenAlex

Abstract The introduction of detailed chemical reaction mechanisms for aviation fuels into complex multidimensional fluid dynamics problems is not practical at the present time. Simplified reaction mechanisms that have been thoroughly evaluated must be developed to address specific issues arising in realistic combustor configurations. A reduced chemical kinetic mechanism features 210 elemental reactions (including 92 reversible reactions and 26 irreversible reactions) and 50 species for the ignition and combustion of n ‐decane was compiled and validated for a wide range of combustion regimes. Validations were performed using experimental measurements on a premixed flame of Jet‐A1, O 2 and N 2 , stabilised at 1 atm on a flat‐flame burner, as well as from n ‐decane shock‐tube ignition experiments. Numerical calculations were performed using this reduced mechanism and the detailed mechanism respectively for n ‐decane surrogate fuel. The calculated values of ignition delay times at pressures of 12, 50 bar and equivalence ratio is 1.0, 2.0, respectively and the main reactants and main products mole fractions agree well with experimental data. The present study shows that this reduced mechanism for the n ‐decane surrogate can be employed to predict premixed combustion of kerosene. © 2012 Canadian Society for Chemical Engineering

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)0.001
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: none
Teacher disagreement score0.533
Threshold uncertainty score0.522

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.016
GPT teacher head0.218
Teacher spread0.203 · 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

Citations4
Published2012
Admission routes2
Has abstractyes

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