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Record W2987864301 · doi:10.1115/gt2019-90518

Film Cooling of Compound Angle Upstream Sister Holes

2019· article· en· W2987864301 on OpenAlexaff
Sana Abd Alsalam, B.A. Jubran

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTurbomachinery Performance and Optimization
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsReynolds numberTurbulenceMaterials scienceUpstream (networking)MechanicsSisterFluentOpticsFlow (mathematics)Computational fluid dynamicsPhysicsEngineeringTelecommunications

Abstract

fetched live from OpenAlex

Abstract This study introduces a novel and simple strategy; compound angle upstream sister holes (CAUSH) to increase film cooling performance of the cylindrical hole by combining two techniques: Sister holes; (two small round holes placed upstream the primary hole) and compound angle hole. Whereas the upstream sister holes were injected at several compound angles β = 0°, 45°, 75°, and 90°, while the main hole was injected to the streamwise direction at 35° on a flat plate. FLUENT-ANSYS code was used to perform the simulation by solving the 3D Reynolds Averaged Navier-Stokes Equations. The capability of three types of k-ε turbulence modeling combined with the enhanced wall treatment is investigated to predict the film cooling performance of sister holes. A detailed computational analysis of the cooling performance of the (CAUSH) and the flow field was done at a density ratio equal to two (D.R = 2) and four blowing ratios M = 0.25, 0.5, 1.0 and 1.5 to predict the centerline and laterally averaged film cooling performance. The centerline effectiveness results showed that the highest cooling performance from the examined (CAUSH) was obtained at β = 0°, 45°, and 90° for low and high blowing ratio, the highest laterally averaged film cooling performance was captured at β = 0° and 90° for all tested blowing ratios. Also, the results indicated that the upstream sister hole with 90° compound angle holes has the best overall film cooling effectiveness while the worst performance is attained at β = 75°.

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 score0.048
Threshold uncertainty score0.376

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 head0.005
GPT teacher head0.187
Teacher spread0.182 · 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

Citations4
Published2019
Admission routes1
Has abstractyes

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