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Record W1992558432 · doi:10.1115/1.3026568

Computational Study of the Effects of Shock Waves on Film Cooling Effectiveness

2009· article· en· W1992558432 on OpenAlexaff
C. X.-Z. Zhang, Ibrahim Hassan

Bibliographic record

VenueJournal of Engineering for Gas Turbines and Power · 2009
Typearticle
Languageen
FieldEngineering
TopicTurbomachinery Performance and Optimization
Canadian institutionsConcordia University
Fundersnot available
KeywordsAirfoilMechanicsLouverTransonicShock waveMach numberBoundary layerMaterials scienceOblique shockCoolantJet (fluid)Shock (circulatory)Leading edgePhysicsThermodynamicsMechanical engineeringEngineeringAerodynamics

Abstract

fetched live from OpenAlex

The performance of a louver cooling scheme on a transonic airfoil has been studied numerically in this paper. Film cooling holes are located near the passage throat. The Mach number at the location of the jet exit is close to unity. A comparison of film cooling effectiveness between numerical prediction and experimental data for a circular hole shows that the numerical procedures are adequate. In addition to the shock-wave effects and compressibility, curvature effect was also studied by comparing cooling effectiveness on the airfoil surface with that on a flat plate. Substantially higher cooling effectiveness for the louver cooling scheme on the airfoil was predicted at blowing ratios below 1 in comparison to other cooling configurations. At higher blowing ratios than 2 the advantages of the louver cooling scheme become less obvious. It was also found that for the same cooling configuration the cooling effectiveness on the transonic airfoil is slightly higher than that on a flat plate at moderately low blowing ratios below 1. At high blowing ratios above 2 when the oblique shock becomes detached from the leading edge of the hole exits, dramatic reduction in cooling effectiveness occurs as a result of boundary layer separation due to the strong shock waves. A coolant-blockage and shaped-wedge similarity was proposed and found to be able to qualitatively explain this phenomenon satisfactorily.

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 score0.304
Threshold uncertainty score0.264

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 head0.003
GPT teacher head0.202
Teacher spread0.199 · 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

Citations6
Published2009
Admission routes1
Has abstractyes

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