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Record W1997296902 · doi:10.1115/1.2966417

Radiation Benchmarking in a Model Combustor

2008· article· en· W1997296902 on OpenAlexafffund
Lei‐Yong Jiang, Ian Campbell

Bibliographic record

VenueJournal of Engineering for Gas Turbines and Power · 2008
Typearticle
Languageen
FieldEngineering
TopicRadiative Heat Transfer Studies
Canadian institutionsNational Research Council Canada
FundersNational Research Council Canada
KeywordsCombustorHeat transferMechanicsRadiationTurbulenceThermal radiationMaterials scienceHeat fluxCombustionPhysicsThermodynamicsOpticsChemistry

Abstract

fetched live from OpenAlex

Radiation heat transfer in a model combustor with interior and exterior conjugate heat transfers has been numerically studied. The previous investigations on turbulence, combustion, and scalar transfer modeling (Reynolds analogy), and comparisons with a comprehensive experimental database provide a reliable base to evaluate the effect of radiation heat transfer on the flow field and NO emission in the combustor. Some of the numerical results with and without radiation are presented and compared with the experimental measurements. It is found that the total radiation heat flux through the combustor wall is about 4.2% of the total energy released from the input fuel. The effect of radiation on the flow field is minor, particularly to the velocity field. In contrast, it has significant effects on the NO field, where the predicted values without radiation are two times higher than those with radiation or the experimental data. A considerable effect of radiation on the combustor wall temperature is also observed. In summary, to provide valuable predictions of NO emission and combustor liner temperature, the radiation heat transfer should be properly taken into account in numerical simulations.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.203
Teacher spread0.192 · 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 source (direct Gemma or distilled Codex), 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

Citations12
Published2008
Admission routes2
Has abstractyes

Explore more

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