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Record W2034001512 · doi:10.4271/2011-01-1385

Numerical Simulation of the Soot and NO<sub>x</sub> Formations in a Biodiesel-Fuelled Engine

2011· article· en· W2034001512 on OpenAlex
Yi Ren, Xianguo Li

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2011
Typearticle
Languageen
FieldChemical Engineering
TopicAdvanced Combustion Engine Technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsSootBiodieselEnvironmental scienceAutomotive engineeringMaterials scienceComputer scienceAerospace engineeringPetroleum engineeringProcess engineeringCombustionEngineeringChemistry

Abstract

fetched live from OpenAlex

<div class="section abstract"><div class="htmlview paragraph">The importance of using biodiesel as an alternative in diesel engines has been demonstrated previously. A reduction in the soot, CO and HC emissions and an increase in the NO</div><div class="htmlview paragraph"> emission burning biodiesel fuels were reported consistently in previous technical papers. However, a widely accepted NO</div><div class="htmlview paragraph"> formation mechanism for biodiesel-fueled engines is currently lacking. As a result, in past multi-dimensional simulation studies, the NO</div><div class="htmlview paragraph"> emission of biodiesel combustion was predicted unsatisfactorily. In this study, the interaction between the soot and NO</div><div class="htmlview paragraph"> formations is considered during the prediction of the soot and NO</div><div class="htmlview paragraph"> emissions in a biodiesel-fueled engine. Meanwhile, a three-step soot model and an eight NO</div><div class="htmlview paragraph"> model which includes both the thermal NO mechanism and prompt mechanism are implemented. To simulate biodiesel combustion, a biodiesel combustion model containing 56 species and 158 reactions is currently developed based on an existing reduced model which consists of 53 species and 156 reactions. The results show that the predicted in-cylinder pressure using the current combustion model matches well with the experimental measurements. The ignition delay of the biodiesel combustion is also satisfactorily predicted using the current biodiesel combustion model. It is found that an increase in the soot formation leads to a reduction in the NO</div><div class="htmlview paragraph"> formation as the soot formation and the prompt NO formation compete for the CH species. The results also reveal that the NO</div><div class="htmlview paragraph"> emission is underpredicted using a NO</div><div class="htmlview paragraph"> model without the prompt NO mechanism. The NO</div><div class="htmlview paragraph"> emission in the biodiesel engine is acceptably predicted in this study. Moreover, the trend of the lift-off length changing versus crank angle is also reasonably predicted using the current biodiesel combustion model.</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.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.953
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.014
GPT teacher head0.228
Teacher spread0.214 · 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