MétaCan
Menu
Back to cohort
Record W2736157529 · doi:10.1177/0954407017706858

Impacts of multiple pilot diesel injections on the premixed combustion of ethanol fuel

2017· article· en· W2736157529 on OpenAlexaff
Tongyang Gao, Shui Yu, Tie Li, Ming Zheng

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering · 2017
Typearticle
Languageen
FieldChemical Engineering
TopicAdvanced Combustion Engine Technologies
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsCombustionDiesel fuelFuel injectionDiesel engineEnvironmental scienceVolatility (finance)Ignition systemWaste managementSecondary air injectionAutomotive engineeringChemistryEngineeringAerospace engineering

Abstract

fetched live from OpenAlex

Engine experiments were carried out to study the impact of multiple pilot injections of a diesel fuel on dual-fuel combustion with a premixed ethanol fuel, using compression ignition. Because of the contrasting volatility and the reactivity characteristics of the two fuels, the appropropriate scheduling of pilot diesel injections in a high-pressure direct-injection process is found to be effective for improving the clean and efficient combustion of ethanol which is premixed with air using a low-pressure port injection. The timing and duration of each of the multiple pilot injections were investigated, in conjunction with the use of exhaust gas recirculation and intake air boosting to accommodate the variations in the engine load. For correct fuel and air management, an early pilot injection of fuel acted effectively as the reactivity improver to the background ethanol, whereas a late pilot injection acted deterministically to initiate combustion. The experimental results further revealed a set of pilot injection strategies which resulted in an increased ethanol ratio, thereby reducing the emission reductions while retaining a moderate pressure rise rate during combustion.

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)0.001
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
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 score0.415
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.022
GPT teacher head0.245
Teacher spread0.223 · 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.

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

Citations7
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Institution of Mechanical Engineers Part D Journal of Automobile EngineeringSame topicAdvanced Combustion Engine TechnologiesFrench-language works237,207