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Record W2240397085 · doi:10.1115/ihtc14-23262

An Experimental Investigation of Film Cooling Performance of Louver Scheme

2010· article· en· W2240397085 on OpenAlexaff
Mohamed Ghorab, Ibrahim Hassan, Terry Lucas

Bibliographic record

Venue2010 14th International Heat Transfer Conference, Volume 5 · 2010
Typearticle
Languageen
FieldEngineering
TopicTurbomachinery Performance and Optimization
Canadian institutionsConcordia University
Fundersnot available
KeywordsLouverAirfoilMaterials scienceHeat transferMechanicsHeat transfer coefficientHeat fluxMechanical engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

The experimental investigation of the film cooling performance of louver schemes using Thermochromic Liquid Crystal technique is presented in this paper. The louver scheme allows the cooling flow to pass through a bend and impinges with the blade material, which then exits to the outer surface of the aerofoil through the film cooling hole. The cooling performance for the louver scheme was analyzed across blowing ratios of 0.5 to 1.5 at a density ratio of 0.94. The results showed that the louver scheme enhances the local and the average film cooling performances in terms of film cooling effectiveness, and net heat flux reduction better than other published film hole configurations. As well, it provides a widely spread of the secondary flow extensively over the downstream surface, thus, it enhances the lateral film cooling performance. Moreover, the louver scheme produces a lower heat transfer coefficient ratio than other film hole geometries at low and high blowing ratios. As a result, the louver scheme is expected to reduce the gas turbine airfoil’s outer surface temperature and provides superior cooling performance which increases airfoil life time.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.221
Teacher spread0.210 · 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 designBench or experimental
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
Published2010
Admission routes1
Has abstractyes

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Same venue2010 14th International Heat Transfer Conference, Volume 5Same topicTurbomachinery Performance and OptimizationFrench-language works237,207