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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

<div class="section abstract"><div class="htmlview paragraph">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</div><div class="htmlview paragraph"> emissions formation.</div></div>

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.989
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.008
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 teacher head, not a consensus.

Study designObservational
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