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Record W4413217610 · doi:10.1115/gt2025-151522

Design and Flow Considerations of Additively Manufactured, Internal Cooling Geometries for Small Industrial Gas Turbines

2025· article· en· W4413217610 on OpenAlexaff
Matthew Searle, Daniel R. Cassar, F. E. Ames, Douglas Straub

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer Mechanisms
Canadian institutionsSiemens (Canada)
Fundersnot available
KeywordsTrailing edgeFinAirfoilSolidityBody orificeLeading edgeCascadeFlow (mathematics)Mechanical engineeringTurbineMaterials scienceEngineeringStructural engineeringAerospace engineeringComputer scienceMechanicsPhysics

Abstract

fetched live from OpenAlex

Abstract Additive manufacturing is now a mainstream technology and can be utilized to rapidly develop and test turbine airfoil cooling networks. This paper reports on an ongoing effort to integrate advanced internal cooling architectures in a realistic blade profile. The airfoils are currently being tested in a highspeed cascade. It is important to note that the cooling architectures are developed for small industrial gas turbines, but the results could be applied to larger frame engines. In the present study, various 1x-scale coupons were printed with features from the cascade airfoils (i.e., orifices, pin fin arrays, and converging trailing edge sections). Cold flow test results are presented for four orifice shapes (circular, raindrop, 2:1 elliptical, and 3:1 elliptical), and high and low solidity pin fin arrays. In addition, flow test results are reported for both metal and plastic test articles representing three different trailing edge designs. For the orifices, the elliptical shapes had the least deviation from design intent and the highest discharge coefficients. The friction factors for pin fin arrays were higher than both existing correlations and data available in the literature. The difference is attributed to the high roughness of the 80 μm build layer height utilized to reduce fabrication time. The trailing edges included three different geometries for pin fin arrays (ranked in order of increasing friction factor): 45° angled pin fins, horizontal pin fins supported with chamfers, and horizontal pin fins supported with a novel “gusset” feature. The ratio of the friction factor for the metal coupons to the plastic coupons is approximately equal to the friction factor augmentation resulting from the metal roughness alone. The friction factor augmentation was similar for each design despite the different geometries, varying from 1.5 to 2. The results obtained here may be utilized to advise future studies and internal cooling designs.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.042
GPT teacher head0.230
Teacher spread0.189 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2025
Admission routes1
Has abstractyes

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