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Record W2323239686 · doi:10.1115/icef2009-14117

A Study on the Performance of Combustion in a HCCI Engine Using n-Heptane by a Multi-Zone Model

2009· article· en· W2323239686 on OpenAlexaff
Hongsheng Guo, Hailin Li, W. Stuart Neill

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsNOxCombustionCompression ratioHomogeneous charge compression ignitionComputer simulationRange (aeronautics)Automotive engineeringHeptaneAir–fuel ratioMechanicsEnvironmental scienceMaterials scienceInternal combustion engineThermodynamicsChemistryCombustion chamberAerospace engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

A study of n-heptane combustion in an HCCI engine was carried out by a multi-zone numerical simulation that covers a complete engine cycle. A reaction mechanism that includes 177 chemical species and 1638 reactions was used. The results of the numerical simulations were compared to existing experimental data for a range of air/fuel ratios, compression ratios and engine speeds. It is shown that the numerical simulation is able to reasonably capture the experimental cylinder pressure data over a wide range of operation conditions. It also provides a qualitative trend of CO emissions. The numerical simulation overpredicted the combustion at some operating conditions, such as at extremely high air/fuel ratios and higher engine speeds. Some differences were observed between the experimental and numerical data for NOX emissions. The numerical simulation predicted a monotonic decrease in NOX emissions as air/fuel ratio increased or compression ratio decreased, while an increase in NOX emissions was observed experimentally when combustion became very weak at extremely high air/fuel ratios or low compression ratios. It is suggested that further experiments and numerical simulations should be performed to explain this discrepancy.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.414
Threshold uncertainty score0.365

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.043
GPT teacher head0.295
Teacher spread0.252 · 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

Citations15
Published2009
Admission routes1
Has abstractyes

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