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Record W2253644071 · doi:10.4271/2007-01-0771

Heat Release Based Adaptive Control to Improve Low Temperature Diesel Engine Combustion

2007· article· en· W2253644071 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

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2007
Typearticle
Languageen
FieldChemical Engineering
TopicAdvanced Combustion Engine Technologies
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsTemperature controlAutomotive engineeringCombustionDiesel engineHomogeneous charge compression ignitionDiesel fuelEnvironmental scienceInternal combustion engineAdaptive controlControl (management)Computer scienceCombustion chamberControl engineeringEngineeringChemistryArtificial intelligence

Abstract

fetched live from OpenAlex

<div class="htmlview paragraph">Heat-release and cylinder pressure based adaptive fuel-injection control tests were performed on a modern common-rail diesel engine to improve the engine operation in the low-temperature combustion (LTC) region. A single shot injection strategy with heavy amount of exhaust gas recirculation (EGR) was used to modulate the in-cylinder charge conditions to achieve the low-temperature combustion. Adaptive fuel-injection techniques were used to anchor the cylinder pressure characteristics in the desired crank angle window and thereby stabilize the engine operation. The response of the adaptive control to boost, fueling, and engine speed variations was also tested. A combination of adaptive fuel-injection and automatic boost/back-pressure controls had helped to make the transient emissions comparable to the steady-state LTC emissions.</div>

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)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesMeta-epidemiology (narrow), Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.844
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0020.003
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 head0.006
GPT teacher head0.226
Teacher spread0.220 · 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