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Record W2765295401 · doi:10.1115/ht2017-4796

Analysis of Film Cooling With High-Aspect-Ratio Holes: Heat Transfer Mechanisms

2017· article· en· W2765295401 on OpenAlexaff
Hao-Ming Li, Wahid Ghaly, Ibrahim Hassan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer Mechanisms
Canadian institutionsConcordia University
Fundersnot available
KeywordsAspect ratio (aeronautics)CoolantDensity ratioMaterials scienceHeat transferMechanicsPosition (finance)Trailing edgeMass ratioDiameter ratioVortexContrast ratioMechanical engineeringOpticsComposite materialPhysicsOptoelectronicsAerospace engineeringEngineering

Abstract

fetched live from OpenAlex

A new advanced film cooling scheme, named high-aspect-ratio holes has been proposed. Four configurations were designed, and numerically simulated under density ratio of 2 and different blowing ratios. All configurations demonstrate extremely high film cooling effectiveness values, some are as high as the so-called perfect performance, while their mechanical strength are similar to the conventional schemes. The new scheme exhibits two traits distinctive from the conventional geometries: Its film cooling effectiveness is much higher than the coverage ratio (t/P), and the high film cooling effectiveness is obtained under strong counter-rotating vortex pair (CRVP). It has been found that, in the new scheme, along with the aspect ratio value increase, the CRVP move away from the coolant-mainstream interface, and the coolant laterally expands in the vicinity of the exit. Consequently, a continuous coolant film would occur near the trailing edge position if aspect ratio is high enough. The approach of high-aspect-ratio holes could be used to design the highest film cooling performance geometries.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.212
Teacher spread0.200 · 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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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