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Record W1994717031 · doi:10.1115/1.4028519

Preliminary Investigation of Direct Injection Neat n-Butanol in a Diesel Engine

2014· article· en· W1994717031 on OpenAlexafffund
Tadanori Yanai, Xiaoye Han, Graham T. Reader, Ming Zheng, Jimi Tjong

Bibliographic record

VenueJournal of Energy Resources Technology · 2014
Typearticle
Languageen
FieldChemical Engineering
TopicAdvanced Combustion Engine Technologies
Canadian institutionsFord Motor Company (Canada)University of Windsor
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsUniversity of WindsorFord Motor Company
KeywordsDiesel engineMean effective pressureAutomotive engineeringn-ButanolBar (unit)Thermal efficiencyExhaust gas recirculationButanolDiesel fuelMaterials scienceFuel injectionIgnition systemCombustionEnvironmental scienceHomogeneous charge compression ignitionNOxInternal combustion engineCombustion chamberChemistryCompression ratioThermodynamicsEngineeringPhysicsEthanolOrganic chemistry

Abstract

fetched live from OpenAlex

The characteristics of combustion, emissions, and thermal efficiency of a diesel engine with direct injection (DI) neat n-butanol were investigated. The engine ran at a load of 6.5–8.0 bar indicated mean effective pressure (IMEP) at 1500 rpm engine speed and the injection pressure was controlled to 900 bar. The intake boost pressure, injection timing, and EGR rate were adjusted to investigate the engine performance. The tests demonstrated that neat n-butanol had the potential to achieve ultralow emissions. However, challenges related to reducing the pressure rise rate and improving the ignition controllability were identified.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.205
Teacher spread0.199 · 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 source (direct Gemma or distilled Codex), 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

Citations19
Published2014
Admission routes2
Has abstractyes

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