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Record W2078385691 · doi:10.1115/icef2005-1220

A Simulation of the Combustion of N-Heptane Fueled HCCI Engines Using a 3-D Model With Detailed Chemical Kinetics

2005· article· en· W2078385691 on OpenAlexafffund
Chengke Liu, Ghazi A. Karim

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldChemical Engineering
TopicAdvanced Combustion Engine Technologies
Canadian institutionsUniversity of Calgary
FundersWestern Canada Research GridUniversity of Calgary
KeywordsCombustionAutoignition temperatureHomogeneous charge compression ignitionHeptaneMethaneHydrogenIgnition systemMaterials scienceChemistryCarbon monoxideThermodynamicsNuclear engineeringCombustion chamberOrganic chemistryPhysicsEngineering

Abstract

fetched live from OpenAlex

A CFD multi-dimensional computational approach has been developed through a combination of a modified KIVA3 code together with a detailed chemical kinetics scheme for the oxidation of n-heptane in air while considering the effects of turbulence. The effects of adding different quantities of hydrogen, methane and carbon monoxide to the heptane on the combustion characteristics of the HCCI engine under different conditions were investigated both experimentally and numerically. The effects of changes in the combustion chamber wall surface temperature on the combustion characteristics of the HCCI engine were examined. It was found that the presence with n-heptane of some hydrogen, methane or carbon monoxide could delay to various extents the autoignition, while changes in the values of the combustion chamber wall temperature influence the autoignition timing and its initial location. It is suggested that the supplementing of the liquid fuel with gaseous fuels and/or application of a suitable glow-plug surface of optimum size and location fitted with temperature control may aid in controlling the combustion process of an HCCI engine while obtaining higher power output without producing knock.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.403
Threshold uncertainty score0.412

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 communication0.0000.000
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 head0.018
GPT teacher head0.246
Teacher spread0.228 · 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
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

Citations5
Published2005
Admission routes2
Has abstractyes

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