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Record W4241595302 · doi:10.1115/icef2005-1291

An Improved Soot Formation Model for 3-D Diesel Engine Simulations

2005· article· en· W4241595302 on OpenAlexaff
Joan Boulanger, Fengshan Liu, W. Stuart Neill, Gregory J. Smallwood

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldChemical Engineering
TopicAdvanced Combustion Engine Technologies
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsSootCombustionDiesel engineDiesel exhaustDiesel fuelMechanicsMaterials sciencePetroleum engineeringEnvironmental scienceAutomotive engineeringPhysicsChemistryEngineeringPhysical chemistry

Abstract

fetched live from OpenAlex

In this study, a phenomenological three-equation soot model was developed for modelling soot formation in diesel engine combustion based on considerations of acceptable computational demand and a qualitative description of the main features of the physics of soot formation. The model was developed based on that of Tesner et al. The model was implemented into the commercial STAR-CD CFD package and was demonstrated in the modelling of soot formation in a single-cylinder research version of Caterpillar 3400 series diesel engine with exhaust gas recirculation. Numerical results show that the new soot formulation overcomes most of the drawbacks in the existing soot models and demonstrates a robust and consistent behaviour with experimental observation. Compared to the existing soot models for engine combustion modelling, some distinct features of the new soot model include: no soot is formed at low temperature, minimal model parameter adjustment for application to different fuels, and there is no need to prescribe the soot particle size.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.680
Threshold uncertainty score0.467

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 communication0.0000.001
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 head0.021
GPT teacher head0.281
Teacher spread0.260 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations2
Published2005
Admission routes1
Has abstractyes

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