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Record W2155224459 · doi:10.1115/1.4030521

A Study of Combustion Inefficiency in Diesel Low Temperature Combustion and Gasoline–Diesel RCCI Via Detailed Emission Measurement

2015· article· en· W2155224459 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Engineering for Gas Turbines and Power · 2015
Typearticle
Languageen
FieldChemical Engineering
TopicAdvanced Combustion Engine Technologies
Canadian institutionsUniversity of Windsor
FundersCanada Research Chairs
KeywordsDiesel fuelCombustionGasolineNOxIgnition systemHomogeneous charge compression ignitionHydrocarbonHydrogenCarbon monoxideWaste managementChemistryEnvironmental scienceMaterials scienceCombustion chamberOrganic chemistryThermodynamicsEngineering

Abstract

fetched live from OpenAlex

Reduction of engine-out NOx emissions to ultra-low levels is facilitated by enabling low temperature combustion (LTC) strategies. However, there is a significant energy penalty in terms of combustion efficiency as evidenced by the high levels of hydrocarbon (HC), carbon monoxide (CO), and hydrogen emissions. In this work, the net fuel energy lost as a result of incomplete combustion in two different LTC regimes is studied—partially premixed compression ignition (PPCI) using in-cylinder injection of diesel fuel and reactivity controlled compression ignition (RCCI) of port injected gasoline and direct injected diesel. A detailed analysis of the incomplete combustion products was conducted. Test results indicated that carbon monoxide (CO), hydrogen, and light hydrocarbon (HC) made up for most of the combustion in-efficiency in the PPCI mode, while heavier HC and aromatics were significantly higher in the RCCI mode.

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)0.000
metaresearch head score (Gemma)0.001
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: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.752

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.020
GPT teacher head0.247
Teacher spread0.227 · 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