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Record W2053513354 · doi:10.1115/imece2012-85875

Experimental Investigation of Double Rows Film Cooling on Vane Pressure Side

2012· article· en· W2053513354 on OpenAlexaff
Tarek Elnady, Ibrahim Hassan, Lyes Kadem, Terry Lucas

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTurbomachinery Performance and Optimization
Canadian institutionsConcordia University
Fundersnot available
KeywordsCoolantMechanicsMaterials scienceReynolds numberHeat transferRowHeat transfer coefficientPressure dropNusselt numberCurvatureInletChord (peer-to-peer)ThermodynamicsPhysicsMechanical engineeringMathematicsGeometryTurbulence

Abstract

fetched live from OpenAlex

An experimental investigation has been performed to study the effect of hole shape and position on the cooling performance of a gas turbine stator. Two rows of laid-back fan-shaped holes are placed on the pressure side of a scaled vane in a two-dimensional cascade and compared with two identical rows of standard cylindrical exit. Both hole shapes have the same base diameter and were investigated at three different blowing ratios (1, 1.35, and 1.7) with the same coolant flow rate used in each case. The experiments are conducted for the first row of holes only, then for the second row only, and finally for both two rows together at a 0.9 density ratio. The mainstream inlet Reynolds number based on the true chord is 1.4E5 and the exit Mach number is 0.23. The local distributions of the heat transfer coefficient and film cooling effectiveness are obtained using a transient TLC technique. The second row of holes, with by a higher local radius of curvature, shows a 40% decrease in the cooling effectiveness as well as a 10% increase in the heat transfer coefficient near downstream of the hole compared with that obtained by the first hole. The double injection provides a slight increase in the cooling effectiveness and a lower heat transfer coefficient due to the favorable interaction between both injections.

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: Empirical
Teacher disagreement score0.668
Threshold uncertainty score0.261

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 head0.014
GPT teacher head0.223
Teacher spread0.209 · 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

Citations1
Published2012
Admission routes1
Has abstractyes

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