MétaCan
Menu
Back to cohort
Record W1981315025 · doi:10.1021/ef901292j

Methane/<i>n</i>-Butane Ignition Delay Measurements at High Pressure and Detailed Chemical Kinetic Simulations

2010· article· en· W1981315025 on OpenAlexaff
Darren Healy, Madeleine Kopp, Nolan Polley, Eric L. Petersen, Gilles Bourque, Henry J. Curran

Bibliographic record

VenueEnergy & Fuels · 2010
Typearticle
Languageen
FieldChemical Engineering
TopicAdvanced Combustion Engine Technologies
Canadian institutionsRolls-Royce (Canada)
Fundersnot available
KeywordsShock tubeIgnition systemAutoignition temperatureKinetic energyMethaneThermodynamicsButaneEquivalence ratioShock waveMaterials sciencePropaneChemistryMechanicsCombustionNuclear engineeringPhysicsPhysical chemistryOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

Autoignition delay time measurements were recorded for blends of CH 4 / n -C 4 H 10 in “air” at pressures of approximately 10, 16, 20, 25, and 30 atm from fuel-lean to fuel-rich conditions at two different fuel compositions, 90% CH 4 /10% n -C 4 H 10 and 70% CH 4 /30% n -C 4 H 10, and temperatures from 660 to 1330 K in both a rapid compression machine and a shock-tube facility. A detailed chemical kinetic model consisting of 1328 reactions involving 230 species was validated using the ignition delay data from this study. This mechanism has already been used to simulate previously published ignition delay times over a wide range of conditions. It was found that the model quantitatively reproduces the ignition delays from both rapid compression and reflected shock waves, accurately capturing reactivity as a function of the temperature, pressure, equivalence ratio, and fuel composition.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.230
Teacher spread0.216 · 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 source (direct Gemma or distilled Codex), 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

Citations103
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueEnergy & FuelsSame topicAdvanced Combustion Engine TechnologiesFrench-language works237,207