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Record W2080387141 · doi:10.1115/imece2007-42682

The Impact of Liquid Properties of Edible-Oil Diesel Fuel on the In-Cylinder Combustion Process

2007· article· en· W2080387141 on OpenAlexaff
Joan Boulanger, W. Stuart Neill, Fengshan Liu, Lei‐Yong Jiang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicBiodiesel Production and Applications
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsDiesel fuelCombustionCylinderDiesel engineMaterials scienceEnvironmental scienceLiquid fuelFuel oilWettingPetroleum engineeringWaste managementAutomotive engineeringComposite materialChemistryMechanical engineeringEngineeringOrganic chemistry

Abstract

fetched live from OpenAlex

Pure edible oils may be used as a diesel fuel. They are nevertheless likely to produce deposits harmful to the engine. The liquid properties have been identified as a major issue but the details of the physical process leading to deposits are not well understood. This paper deals with simulations of a single-cylinder research diesel engine using virtual fuels which show the effects of different liquid properties. The aim is to investigate the impact on the in-cylinder processes of each property change from a conventional alkane to a fatty acid. The critical temperature, which makes fatty acid much less volatile than conventional diesel, has the biggest impact. It strongly delays the release of fuel into the gas phase and extends the combustion time. The effects of droplet break-up, heating and density are marginal. Globally, a longer survival time of droplets tends to spread the combustion and rich mixture zones as well as increase the probability of wall wetting. The swirl motion is likely to expose the droplets to the cooler cylinder walls, which leads to further problems in vaporizing the fuel and completing the combustion process. The findings may be used to explain carbon deposits and lubricating oil contamination.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.376
Threshold uncertainty score0.131

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.030
GPT teacher head0.266
Teacher spread0.236 · 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

Citations0
Published2007
Admission routes1
Has abstractyes

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