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Record W2129731832 · doi:10.1115/gt2005-68403

Turbulence Modeling in a Model Combustor

2005· article· en· W2129731832 on OpenAlexaff
Lei‐Yong Jiang, Ian Campbell

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicCombustion and flame dynamics
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsTurbulenceCombustorMechanicsReynolds stressTurbulence modelingLaminar flowLaminar sublayerCombustion chamberTurbulence kinetic energyReynolds stress equation modelCombustionReynolds numberMaterials scienceThermodynamicsK-omega turbulence modelPhysicsChemistry

Abstract

fetched live from OpenAlex

The flow field of a propane-air diffusion flame combustor with interior and exterior conjugate heat transfers was numerically investigated. Solutions obtained from four turbulence models together with a laminar flamelet combustion model, discrete ordinates radiation model and enhanced wall treatment are presented and discussed. The numerical results are compared, in detail, with a comprehensive database obtained from a series of experimental measurements. It is found that the Reynolds stress model (RSM), a second moment closure, illustrates superior performance over three popular two-equation eddy-viscosity models. Although the main flow features are captured by all four turbulence models, only the RSM is able to successfully predict the lengths of both recirculation zones and the turbulence kinetic energy distribution in the combustor chamber. In addition, it provides fairly good predictions for all Reynolds stress components, except for the circumferential normal stress at downstream sections. However, the superiority of the RSM is not so obvious for the temperature and species predictions in comparison with eddy-viscosity models, except for the standard k-ε model. This suggests that coupling between the RSM and combustion models needs to be further improved in order to enhance its applications in practical combustion systems.

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 score0.623
Threshold uncertainty score0.253

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 head0.017
GPT teacher head0.203
Teacher spread0.186 · 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

Citations1
Published2005
Admission routes1
Has abstractyes

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