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Record W4241731673 · doi:10.1115/1.4039936

Effects of Area Ratio and Mean Rise Angle on the Aerodynamics of Interturbine Ducts

2018· article· en· W4241731673 on OpenAlexafffund
Yanfeng Zhang, Shuzhen Hu, Ali Mahallati, Xuefeng Zhang, Edward Vlasic

Bibliographic record

VenueJournal of Turbomachinery · 2018
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsNational Research Council Canada
FundersNational Research Council CanadaPratt and Whitney Canada
KeywordsAdverse pressure gradientCasingMechanicsDuct (anatomy)InletFlow separationBoundary layerVortexReynolds numberAerodynamicsPressure gradientMaterials scienceMean flowTotal pressureStatic pressureGeologyPhysicsTurbulenceGeomorphologyAnatomy

Abstract

fetched live from OpenAlex

This work, a continuation of a series of investigations on the aerodynamics of aggressive interturbine ducts (ITD), is aimed at providing detailed understanding of the flow physics and loss mechanisms in four different ITD geometries. A systematic experimental and computational study was carried out by varying duct outlet-to-inlet area ratios (ARs) and mean rise angles while keeping the duct length-to-inlet height ratio, Reynolds number, and inlet swirl constant in all four geometries. The flow structures within the ITDs were found to be dominated by the boundary layer separation and counter-rotating vortices in both the casing and hub regions. The duct mean rise angle determined the severity of adverse pressure gradient in the casing's first bend, whereas the duct AR mainly governed the second bend's static pressure rise. The combination of upstream wake flow and the first bend's adverse pressure gradient caused the boundary layer to separate and intensify the strength of counter-rotating vortices. At high mean rise angle, the separation became stronger at the casing's first bend and moved farther upstream. At high ARs, a two-dimensional separation appeared on the casing and resulted in increased loss. Pressure loss penalties increased significantly with increasing duct mean rise angle and AR.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.859
Threshold uncertainty score0.346

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 head0.005
GPT teacher head0.188
Teacher spread0.183 · 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

Citations13
Published2018
Admission routes2
Has abstractyes

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