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Record W1965504930 · doi:10.1115/gt2014-25405

Air Distribution Over a Combustor Liner

2014· article· en· W1965504930 on OpenAlexafffund
Lei‐Yong Jiang, P. Andrew Corber

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicCombustion and flame dynamics
Canadian institutionsNational Research Council Canada
FundersMinistère de la Défense Nationale
KeywordsCombustorAirflowMechanicsCombustion chamberFlow (mathematics)CombustionComputational fluid dynamicsSecondary air injectionGas turbinesMechanical engineeringEnvironmental scienceEngineeringPhysicsThermodynamicsChemistry

Abstract

fetched live from OpenAlex

The splitting of the airflow that passes through the openings in the combustor liner is vital to its performance. Traditionally, numerical simulations of the gas turbine combustor have limited the computational domain to only the flow field inside the liner, while the airflow distribution over the liner is estimated based on semi-empirical correlations. In addition, the airflow rates are assumed to be the same through the identical open passages at each combustor axial cross-section. In the present study, the internal and external flow fields of a practical gas turbine combustor liner are directly coupled, so the air splitting is determined by comprehensive simulations. The predicted results for the air distribution are closely correlated to the dataset estimated from recently improved semi-empirical correlations for passage discharge-coefficients. The simulations also show that the effects of the combustion process on the air splitting can be neglected. Lastly, the results reveal that the airflow through identical passages at the same axial cross-section are not equal, and can vary by up to ±25% of the mean. In summary, the present study suggests that when performing computational models of gas turbine combustor flows, the simulations should couple the liner’s internal and external air splits whenever possible.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.763
Threshold uncertainty score0.205

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 head0.003
GPT teacher head0.174
Teacher spread0.171 · 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

Citations3
Published2014
Admission routes2
Has abstractyes

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