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Record W2048225724 · doi:10.1109/acc.2014.6858943

Combustion phasing and work output modeling for homogeneous charge compression ignition (HCCI) engines

2014· article· en· W2048225724 on OpenAlexaff
Chen Song, Fengjun Yan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldChemical Engineering
TopicAdvanced Combustion Engine Technologies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsHomogeneous charge compression ignitionCombustionIgnition systemWork (physics)Work outputCompression (physics)Automotive engineeringThrust specific fuel consumptionComputer scienceAlgorithmThermodynamicsPhysicsChemistryCombustion chamberEngineeringPhysical chemistry

Abstract

fetched live from OpenAlex

Combustion phasing and work output are the two critical indicators for achieving and maintaining homogeneous combustion for homogeneous charge compression ignition (HCCI) engines. However, the complicated thermodynamic process keeps the combustion phasing model from being explicit. The current models for CA <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">50</inf> (crank angle when 50% fuel consumed) requires information that is hardly available for commercial engines. In this paper, polynomial models for combustion phasing and work output are proposed. The intake conditions, including intake manifold temperature, pressure and burned gas fraction as well as the fuel injection are chose as the four variables by whom the combustion phasing and work output are determined. The impacts of various compression ratios on the two indicators (represented by CA <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">50</inf> and IMEP) were investigated and have been classified into two groups: vertical shift and curve deformation and both can be well modeled and, therefore compensated. The models are validated through high-fidelity simulations.

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 score0.810
Threshold uncertainty score0.740

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 head0.026
GPT teacher head0.247
Teacher spread0.221 · 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

Citations2
Published2014
Admission routes1
Has abstractyes

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