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Record W4226148863 · doi:10.1115/1.4053232

Cooling Performance of Additively Manufactured Pin Fins in Stacked Microchannels for the Inside-Out Ceramic Turbine Shroud-Cooling Ring

2021· article· en· W4226148863 on OpenAlexafffund
P. K. Dubois, Alexandre Landry-Blais, Rym Gazzah, S. Sivić, Vladimir Braïlovski, Mathieu Picard

Bibliographic record

VenueJournal of Turbomachinery · 2021
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer Mechanisms
Canadian institutionsÉcole de Technologie SupérieureUniversité de Sherbrooke
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of Canada
KeywordsShroudMaterials scienceNusselt numberFinTurbineHeat transferWater coolingMechanicsCeramicComposite materialMechanical engineeringEngineeringReynolds numberTurbulencePhysics

Abstract

fetched live from OpenAlex

Abstract The inside-out ceramic turbine (ICT), a novel microturbine rotor architecture, has an air-cooled ring, which keeps its composite rotating structural shroud within operating temperature. The cooling ring must achieve a significant radial temperature gradient with a minimal amount of cooling. The cooling ring is made through additive manufacturing, which opens the design space to tailored cooling geometries. Additively manufactured pin fin heat transfer enhancers are explored in this study to assess whether they hold any significant performance benefit over current rectangular cross section open channels. Experimental friction factors and Nusselt numbers were determined for small, densely-packed pin fins over an asymmetrical thermal load. Results indicate that pressure loss is similar to what can be expected for additively manufactured pin fins, whereas heat transfer is lower due to the extremely tight streamwise pin spacing, in both in-line and staggered pin configurations. A design study presented in this article suggests that pin fins are beneficial to an ICT for reducing cooling mass flowrate up to 40%, against an increase in cooling ring mass of roughly 50%.

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)0.001
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 score0.239
Threshold uncertainty score0.897

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.016
GPT teacher head0.235
Teacher spread0.218 · 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

Citations2
Published2021
Admission routes2
Has abstractyes

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