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Record W4245612059 · doi:10.1115/gt2017-63304

Quantifying Blowing Ratio for Shaped Cooling Holes

2017· article· en· W4245612059 on OpenAlexaff
D. J. Cerantola, A. M. Birk

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer Mechanisms
Canadian institutionsQueen's University
Fundersnot available
KeywordsCombustorTurbulenceMechanicsAdiabatic processMaterials scienceMass fluxMass flow rateTurbine bladeMass flowTurbinePhysicsThermodynamicsChemistry

Abstract

fetched live from OpenAlex

Effusion cooling was a popular technology integrated into the design of gas turbine combustor liners. A staggering amount of research was completed that quantified performance with respect to operating conditions and cooling hole geometric properties; however, most of these investigations did not address the influence of the manufacturing process on the hole shape. This study completed an adiabatic wall numerical analysis using the realizable k-ε turbulence model of a laser-drilled hole that had a nozzled profile with an area ratio of 0.24 and five additional cylindrical, nozzled, diffusing, and filleted holes that yielded the same hole mass flow rate at representative engine conditions. The traditional methods for quantifying blowing ratio yielded the same value for all holes that was not useful considering the substantial differences in film cooling performance. It was proposed to define hole mass flux based on the outlet y-cross sectional area projected onto the inclination angle plane. This gave blowing ratios that correlated to better and worse cooling performance for the diffusing and nozzled holes respectively. The diffusing hole delivered the best film cooling due to having the lowest effluent velocity and greatest amount of in-hole turbulent production, which coincided with the worst discharge coefficient.

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: none
Teacher disagreement score0.835
Threshold uncertainty score0.436

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 head0.080
GPT teacher head0.299
Teacher spread0.219 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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