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Record W2033374995 · doi:10.1115/icef2009-14012

Hydrocarbon Impacts on Diesel HCCI Engine Cycles

2009· article· en· W2033374995 on OpenAlexafffund
Ming Zheng, Usman Asad, Xiaoye Han, Meiping Wang, Graham T. Reader

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVehicle emissions and performance
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Windsor
KeywordsNOxDiesel fuelHomogeneous charge compression ignitionExhaust gas recirculationHydrocarbonCombustionEnvironmental scienceDiesel engineThermal efficiencyDiesel exhaustIgnition systemAutomotive engineeringWaste managementInternal combustion engineChemistryCombustion chamberEngineeringAerospace engineeringOrganic chemistry

Abstract

fetched live from OpenAlex

Thermal efficiency and NOx emission comparisons are made between the homogeneous charge compression ignition (HCCI) and the conventional diesel cycles on a number of common-rail diesel engine platforms of high compression ratios with conventional diesel fuel and dimethyl ether as a surrogate fuel. The empirical studies have been conducted under independently controlled exhaust gas recirculation (EGR), intake boost, and exhaust backpressure. The energy relevance of the combustible substances such as carbon monoxide and hydrocarbon species in the engine exhaust has been evaluated quantitatively. However, the impact of the hydrocarbons produced during the HCCI cycles on the attainment of ultra low levels of NOx is less understood and it is unclear if the hydrocarbon species are a precursor to the ultra low NOx and also contribute in part to the NOx reduction. Therefore, the chemical impact of the hydrocarbon species on the NOx emission under low temperature combustion cycles has been examined with crank-angle resolved in-cylinder sampling techniques and fast-response emission analyzers. This paper intends to identify the major impacts of the hydrocarbons on the fuel efficiency and emissions of diesel HCCI cycles.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.581
Threshold uncertainty score0.315

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 head0.008
GPT teacher head0.220
Teacher spread0.212 · 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

Citations0
Published2009
Admission routes2
Has abstractyes

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