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Record W1984497457 · doi:10.1115/gt2011-45585

Experimental Investigation of Louver Cooling Scheme on Gas Turbine Vane Suction Side

2011· article· en· W1984497457 on OpenAlexaff
Tarek Elnady, O. Hassan, Ibrahim Hassan, Lyes Kadem, Terry Lucas

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTurbomachinery Performance and Optimization
Canadian institutionsConcordia University
Fundersnot available
KeywordsLouverMechanicsMach numberMaterials scienceReynolds numberCascadeSuctionChord (peer-to-peer)Heat transferThermodynamicsMechanical engineeringPhysicsEngineeringComputer science

Abstract

fetched live from OpenAlex

An experimental investigation has been performed to measure the film cooling performance of louver scheme over a scaled vane of high-pressure gas turbine using a two-dimensional cascade. Two rows of axially oriented louver scheme are used to cool the suction side and their performance is compared with two similar rows of standard cylindrical holes. The effect of hole location on the cooling performance is investigated for each row individually, then the row interaction is investigated for both rows at four different blowing ratios ranging from 1 to 2 with a 0.9 density ratio. The exit Reynolds number based on the true chord is 1.5E5 and exit Mach number is 0.23. The temperature distribution on the vane is mapped using a transient Thermochromic Liquid Crystal (TLC) technique to obtain the local distributions of the heat transfer coefficient and film cooling effectiveness. The louver scheme shows a superior cooling effectiveness than that of the cylindrical holes at all blowing ratios in terms of protection and lateral coverage. The row location highly affects the cooling performance for both the louver and cylindrical scheme.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.137
Threshold uncertainty score0.319

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 head0.019
GPT teacher head0.204
Teacher spread0.185 · 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 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".

Quick stats

Citations1
Published2011
Admission routes1
Has abstractyes

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