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Record W2040363629 · doi:10.1243/14680874jer02008

Combustion in a heavy-duty direct-injection engine using hydrogen—methane blend fuels

2009· article· en· W2040363629 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

VenueInternational Journal of Engine Research · 2009
Typearticle
Languageen
FieldChemical Engineering
TopicAdvanced Combustion Engine Technologies
Canadian institutionsUniversity of British Columbia
FundersAUTO21 Network of Centres of ExcellenceNatural Sciences and Engineering Research Council of Canada
KeywordsCombustionMethaneEnvironmental scienceWaste managementNatural gasDiesel fuelDiesel engineInternal combustion engineChemistryAutomotive engineeringEngineering

Abstract

fetched live from OpenAlex

Adding hydrogen to the fuel in a direct injection natural gas engine offers the potential significantly to reduce local and global air pollutant emissions. This work reports on the effects of fuelling a heavy-duty engine with late-cycle direct injection of blended hydrogen—methane fuels and diesel pilot ignition over a range of engine operating conditions. The effect of hydrogen on the combustion event varies with operating condition, providing insight into the fundamental factors limiting the combustion process. Combustion stability is enhanced at all conditions studied; this leads directly to a significant reduction in emissions of combustion byproducts, including carbon monoxide, particulate matter, and unburned fuel. Carbon dioxide emissions are also significantly reduced by the lower carbon—energy ratio of the fuel. The results suggest that this technique can significantly reduce both local and global pollutant emissions associated with heavy-duty transport applications while requiring minimal changes to the fuelling system.

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)0.002
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 score0.393
Threshold uncertainty score0.793

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.060
GPT teacher head0.389
Teacher spread0.328 · 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