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Record W2921161403 · doi:10.1115/icef2018-9772

Cycle-to-Cycle NO and NOx Emissions From a HSDI Diesel Engine

2018· article· en· W2921161403 on OpenAlexfundno aff
Felix Leach, Martin Davy, Mark Peckham

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldChemical Engineering
TopicAdvanced Combustion Engine Technologies
Canadian institutionsnot available
FundersUniversity of British ColumbiaUniversity of Oxford
KeywordsNOxDiesel engineDiesel fuelEnvironmental scienceExhaust gas recirculationTwo-stroke engineAutomotive engineeringDriving cycleCrankCombustionInternal combustion engineCylinderChemistryEngineeringMechanical engineeringPower (physics)Physics

Abstract

fetched live from OpenAlex

Engine-out NOx emissions from diesel engines continue to be a major topic of research interest. While substantial understanding has been obtained of engine-out (i.e. before any aftertreatment) NOx formation and reduction techniques, not least EGR which is now well established and fitted to production vehicles, much less data are available on cycle resolved NOx emissions. In this work, crank-angle resolved NO and NOx measurements have been taken from a high-speed light duty diesel engine at test conditions both with and without EGR. These have been combined with 1D data of exhaust flow and this used to form a mass average of NO and NOx emissions per cycle. These results have been compared with combustion data and other emissions. The results show that there is a very strong correlation (R2 > 0.95) between the NOx emitted per cycle and the peak cylinder pressure of that cycle. In addition, the crank-angle resolved NO and NOx measurements also reveal that there is a difference in NO : NO2 ratio (where NO2 is assumed to be the difference between NO and NOx) during the exhaust period, with proportionally more NO2 being emitted during the blowdown period compared to the rest of the exhaust stroke.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.566
Threshold uncertainty score0.677

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)0.0010.001

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 head0.008
GPT teacher head0.241
Teacher spread0.233 · 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 designBench or experimental
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

Citations8
Published2018
Admission routes1
Has abstractyes

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