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Record W2746900447 · doi:10.1115/gt2017-63306

Numerical Investigation of a Laser-Drilled Cooling Hole

2017· article· en· W2746900447 on OpenAlexaff
D. J. Cerantola, A. M. Birk

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer Mechanisms
Canadian institutionsQueen's University
Fundersnot available
KeywordsNozzleMaterials scienceMechanicsConical surfaceHeat transferDeep hole drillingTurbulenceDrillingOpticsMechanical engineeringComposite materialPhysicsMetallurgyEngineering

Abstract

fetched live from OpenAlex

Modern aeroengines utilized effusion cooling technology to further protect the components from degrading at the operating temperatures. Most studies did not address the influence of the manufacturing process used to form the cooling holes on the flow physics where percussion laser drilling was a common technique that produced irregularly shaped holes with roughened surfaces. The investigated as-drilled hole surface was statistically homogeneous, non-isotropic, and generally composed of gradually transitioning plateaus that had imperfections with an average height of 0.32 hole diameters. A conjugate heat transfer CFD study was completed on cylindrical, conical nozzle, and as-drilled holes, all yielding the same hole mass flow rates, with the realizable k-ε turbulence model at representative engine conditions. The cylindrical hole had higher film cooling effectiveness due to lower effluent velocity, and better in-hole heat transfer performance due to higher on-average in-hole flow velocities. The as-drilled hole had nominally better film cooling than the conical nozzle hole due to the higher in-hole turbulence production caused by the roughened surface texture. Ultimately, the hole area profile more significantly influenced the averaged metal temperature.

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 score0.144
Threshold uncertainty score0.238

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 head0.022
GPT teacher head0.232
Teacher spread0.210 · 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
Published2017
Admission routes1
Has abstractyes

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