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Record W2109002178 · doi:10.1109/acc.2007.4282329

Control Oriented Modeling of Combustion Phasing for an HCCI Engine

2007· article· en· W2109002178 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProceedings of the ... American Control Conference/Proceedings of the American Control Conference · 2007
Typearticle
Languageen
FieldChemical Engineering
TopicAdvanced Combustion Engine Technologies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsHomogeneous charge compression ignitionCombustionCrankAutomotive engineeringIgnition systemFuel efficiencyIgnition timingInternal combustion engineThrust specific fuel consumptionCrankshaftComputer scienceCylinderEngineeringCombustion chamberMechanical engineeringChemistryAerospace engineering

Abstract

fetched live from OpenAlex

A promising method for reducing emissions and fuel consumption of internal combustion engines is the Homogeneous charge compression ignition (HCCI) engine. Control of ignition timing must be realized before the potential benefits of HCCI combustion can be implemented in production engines. A model suitable for real time implementation is developed and this model is able to predict ignition timing with an average error of less than 2 crank angle degrees. A modified knock- integral model (MKIM), with correlations for gas exchange process and fuel heat release, is used to predict HCCI combustion timing (CA50, crank angle where 50% of the fuel mass is burnt). The MKIM model is parameterized using a thermokinetic simulation model. Experimental data from a single cylinder engine at several HCCI operation conditions and three fuel blends is used to validate the model.

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.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.833
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.002
Science and technology studies0.0000.003
Scholarly communication0.0000.001
Open science0.0030.000
Research integrity0.0000.001
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 head0.017
GPT teacher head0.260
Teacher spread0.243 · 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