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Record W1986864662 · doi:10.1115/gt2011-45267

Computational Study of Micro-Jet Impingement Heat Transfer in a High Pressure Turbine Vane

2011· article· en· W1986864662 on OpenAlexaff
Karan Anand, B.A. Jubran

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer Mechanisms
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsTaperingAirfoilMechanicsJet (fluid)NozzleHeat transferFinite volume methodFluentTurbineCurvatureMaterials scienceComputational fluid dynamicsPhysicsThermodynamicsGeometryMathematicsComputer science

Abstract

fetched live from OpenAlex

The purpose of this numerical investigation is to study the micro-jet impingement heat transfer characteristics and hydromechanics in a 3-D, actual-shaped turbine vane geometry. No concession is made on either the skewness or curvature profile of the airfoil in the streamwise direction, nor to the lean, airfoil twist or tapering of the vane in the spanwise direction. The problem on hand consists of a constant property flow of air via an array of 42 round micro jets impinging onto the inner surface of the airfoil. For simplicity, validation and better understanding of the nature of impingement heat transfer, the airfoil surfaces are provided with a constant temperature boundary condition. Validation is performed against existing numerical results on a simplified model with no spanwise tapering or twisting. The modeled volume spans a total of 12D and consists of three rows of jets; each row contains 14 inline jets. Governing equations are solved using a finite volume method in FLUENT. Effects of jet inclination (+45° and −45° inclinations) and decrease in nozzle diameter (0.51, 0.25 and 0.125 mm) are studied. Inclination of −45° produced enhanced mixing and secondary peaks with marginal decrease in stagnation values. The effect of reducing the diameter of the jets yielded positive results; the tapering effect too enhanced the local heat transfer values, which is attributed to the increase in local velocities at jet exit.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.615
Threshold uncertainty score0.688

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)0.0010.000

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 head0.209
Teacher spread0.191 · 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

Citations5
Published2011
Admission routes1
Has abstractyes

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