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Record W2287409354 · doi:10.1115/gt2015-43063

Development and Validation of a Partially Coupled Soot Model for Turbulent Kerosene Combustion in View of Application to Gas Turbines

2015· article· en· W2287409354 on OpenAlexaff
Bijan Shahriari, Murray J. Thomson, Seth B. Dworkin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicCombustion and flame dynamics
Canadian institutionsToronto Metropolitan UniversityUniversity of Toronto
Fundersnot available
KeywordsSootKeroseneCombustionTurbulenceVolume fractionEnvironmental scienceChemistryAerospace engineeringPetroleum engineeringMaterials scienceMechanicsThermodynamicsPhysicsEngineeringOrganic chemistry

Abstract

fetched live from OpenAlex

Soot emissions are by-products of combustion that are well documented to have adverse effects on human health and the environment. Consequently, these emissions are becoming a target for stricter regulations. However, obstacles exist in the implementation of soot models in Computational Fluid Dynamics codes with complex geometry, such as ensuring carbon mass conservation as soot forms. This challenge is due to the thermochemistry interactions in turbulent codes being preprocessed (included in look-up tables), and not solved for directly. This study considers the development of a soot model for kerosene combustion. Coupling is introduced between the soot and gas phase by including nucleation rates within the flamelet library, and by adjusting the concentrations of key soot precursors through additional transport equations. Validation has been performed for turbulent coflow kerosene flames at pressures of 1 and 4.8 bar. This simplified model reasonably predicts the soot volume fraction without tuning of the inception rate.

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: Empirical · Consensus signal: none
Teacher disagreement score0.471
Threshold uncertainty score0.268

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 head0.028
GPT teacher head0.249
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.

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

Citations2
Published2015
Admission routes1
Has abstractyes

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