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Record W2041931393 · doi:10.1115/1.4027656

Flow Control in an Aggressive Interturbine Transition Duct Using Low Profile Vortex Generators

2014· article· en· W2041931393 on OpenAlexafffund
Yanfeng Zhang, Shuzhen Hu, Xuefeng Zhang, Michael Benner, Ali Mahallati, Edward Vlasic

Bibliographic record

VenueJournal of Engineering for Gas Turbines and Power · 2014
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsNational Research Council Canada
FundersNational Research Council Canada
KeywordsCasingBoundary layerVortex generatorMechanicsVortexAdverse pressure gradientFlow separationDuct (anatomy)Reynolds numberTurbulenceStarting vortexPhysicsAnnulus (botany)Boundary layer thicknessSecondary flowMaterials scienceVortex ringComposite materialAnatomy

Abstract

fetched live from OpenAlex

This paper presents an experimental investigation of the flow mechanisms in an aggressive interturbine transition duct with and without low-profile vortex generators flow control. The interturbine duct had an area ratio of 1.53 and a mean rise angle of 35 deg. Measurements were made inside the annulus at a Reynolds number of 150,000. At the duct inlet, the background turbulence intensity was raised to 2.3% and a uniform swirl angle of 20 deg was established with a 48-airfoil vane ring. Results for the baseline case (no vortex generators) showed the flow structures within the duct were dominated by counter-rotating vortices and boundary layer separation in both the casing and hub regions. The combination of the adverse pressure gradient at the casing's first bend and upstream low momentum wakes caused the boundary layer to separate on the casing. The separated flow on the casing appears to reattach at the second bend. Counter-rotating and corotating vortex generators were installed on the casing. While both vortex generators significantly decreased the casing boundary layer separation with consequential reduction of overall pressure losses, the corotating configuration was found to be more effective.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.693

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 head0.004
GPT teacher head0.195
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 designSimulation or modeling
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

Citations15
Published2014
Admission routes2
Has abstractyes

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