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Record W2256404019 · doi:10.4271/2010-01-1259

Numerical Study on the Effects of Biodiesel Fuel on Combustion and Emission Characteristics in a Direct Injection Diesel Engine

2010· article· en· W2256404019 on OpenAlexaff
Yi Ren, Xianguo Li, Ehab Abu‐Ramadan

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2010
Typearticle
Languageen
FieldEngineering
TopicBiodiesel Production and Applications
Canadian institutionsUniversity of Waterloo
FundersSandia National Laboratories
KeywordsBiodieselDiesel engineAutomotive engineeringCombustionDiesel fuelHomogeneous charge compression ignitionEnvironmental scienceInternal combustion engineDiesel cycleComputer scienceMaterials scienceProcess engineeringCombustion chamberEngineeringPetrol engineChemistryCatalysisOrganic chemistry

Abstract

fetched live from OpenAlex

The effects of the physical and chemical properties of biodiesel fuels on the combustion process and pollutants formation in Direct Injection (DI) engine are investigated numerically by using multi-dimensional CFD models. In the current study, methyl butanoate (MB) and n-heptane are used as the surrogates for the biodiesel fuel and the conventional diesel fuel. Detailed kinetic chemical mechanisms for MB and n-heptane are implemented to simulate the combustion process. It is shown that the differences in the chemical properties between the biodiesel fuel and the diesel fuel affect the whole combustion process more significantly than the differences in the physical properties. While the variations of both the chemical and the physical properties between the biodiesel and diesel fuel influence the soot formation at the equivalent level, the variations in the chemical properties play a crucial role in the NO emissions formation.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
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 head0.008
GPT teacher head0.229
Teacher spread0.221 · 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 designSimulation or modeling
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

Citations5
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueSAE technical papers on CD-ROM/SAE technical paper seriesSame topicBiodiesel Production and ApplicationsFrench-language works237,207