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Record W4404093286 · doi:10.23977/jemm.2024.090218

Investigation of the conjugate heat transfer and cooling mechanism of film cooling holes with different ellipticities

2024· article· en· W4404093286 on OpenAlexvenueno aff

Bibliographic record

VenueJournal of Engineering Mechanics and Machinery · 2024
Typearticle
Languageen
FieldEngineering
TopicTribology and Lubrication Engineering
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsConjugateMechanism (biology)Heat transferMaterials scienceFree coolingActive coolingWater coolingMechanicsPhysicsThermodynamicsMathematicsMathematical analysis

Abstract

fetched live from OpenAlex

The cooling characteristics and cooling mechanisms of various elliptical film cooling holes were analyzed at blowing ratios of 0.5, 1.0, and 1.5. Five different elliptic film cooling hole structures were obtained based on the cylindrical hole model by varying the aspect ratio of the film cooling holes. To examine the cooling performance of individual film cooling apertures under both adiabatic and conjugate heat transfer conditions, numerical simulations were carried out using computational fluid dynamics (CFD) and conjugate heat transfer (CHT) techniques. The cooling mechanisms were elucidated based on these simulations. The results indicate that elliptical film cooling apertures exhibit the maximum cooling efficiency at a moderate blowing ratio. A decrease in ellipticity leads to a structural alteration in the shear layer vortex within the flow field. Apertures with lower ellipticity induce more robust shear layer vortices, improving the cooling effectiveness. Additionally, among the analyzed elliptical holes, those with an ellipticity of 0.5 exhibited superior comprehensive cooling effectiveness and film coverage efficiency, showing the best film cooling performance.

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: Empirical
Teacher disagreement score0.381
Threshold uncertainty score0.391

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 head0.007
GPT teacher head0.175
Teacher spread0.168 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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