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Record W4235191408 · doi:10.1115/1.1645533

The Influence of Leading-Edge Geometry on Secondary Losses in a Turbine Cascade at the Design Incidence

2004· article· en· W4235191408 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Turbomachinery · 2004
Typearticle
Languageen
FieldEngineering
TopicTurbomachinery Performance and Optimization
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAirfoilCascadeTrailing edgeLeading edgeSecondary flowVortexInletMechanicsFlow (mathematics)TurbineEnhanced Data Rates for GSM EvolutionUpstream (networking)GeometryPhysicsMaterials scienceStructural engineeringEngineeringMechanical engineeringMathematicsTurbulence

Abstract

fetched live from OpenAlex

The paper presents detailed experimental results of the secondary flows from two large-scale, low-speed linear turbine cascades. The aerofoils for the two cascades were designed for the same inlet and outlet conditions and differ mainly in their leading-edge geometries. Detailed flow field measurements were made upstream and downstream of the cascades using three and seven-hole pressure probes and static pressure distributions were measured on the aerofoil surfaces. All measurements were made exclusively at the design incidence. The results from this experiment suggest that the strength of the passage vortex plays an important role in the downstream flow field and loss behavior. It was concluded that the aerofoil loading distribution has a significant influence on the strength of this vortex. In contrast, the leading-edge geometry appears to have only a minor influence on the secondary flow field, at least for the design incidence.

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.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.898
Threshold uncertainty score0.467

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.008
GPT teacher head0.226
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