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Record W2034233465 · doi:10.1080/00102200600910874

A SKELETAL KINETIC MECHANISM FOR PRF COMBUSTION IN HCCI ENGINES

2007· article· en· W2034233465 on OpenAlexaff
Patrick Kirchen, Mahdi Shahbakhti, Charles Robert Koch

Bibliographic record

VenueCombustion Science and Technology · 2007
Typearticle
Languageen
FieldChemical Engineering
TopicAdvanced Combustion Engine Technologies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsHomogeneous charge compression ignitionShock tubeIgnition systemCombustionKinetic energyThermodynamicsAutoignition temperatureChemistryIgnition timingMechanicsNuclear engineeringMaterials scienceShock waveCombustion chamberPhysicsPhysical chemistryEngineering

Abstract

fetched live from OpenAlex

A single zone thermodynamic model, coupled to a kinetic mechanism, is developed and is capable of predicting the ignition timing of Primary Reference Fuels (PRFs) in a Homogeneous Charge Compression Ignition (HCCI) engine. A new combination of kinetic mechanisms is used, which includes 120 reactions and 58 species for both ignition and high temperature reactions. The model is validated using a step by step methodology. The validation compares ignition delays predicted by the model with published measurements from a rapid compression machine, shock tube as well as the cylinder pressure histories taken from two different experimental HCCI engines for various operating conditions. The model is able to qualitatively predict the effect of different parameters such as gas temperature, gas pressure, equivalence ratio and octane number on the HCCI ignition delay.

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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0010.000
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 head0.271
Teacher spread0.257 · 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

Citations46
Published2007
Admission routes1
Has abstractyes

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