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Record W2031755271 · doi:10.1115/1.2101852

Effects of Surface-Roughness Geometry on Separation-Bubble Transition

2005· article· en· W2031755271 on OpenAlexafffund
S. K. Roberts, M. I. Yaras

Bibliographic record

VenueJournal of Turbomachinery · 2005
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsCarleton University
FundersPratt and Whitney Canada
KeywordsSurface finishSkewnessSurface roughnessRoughness lengthMechanicsInviscid flowBubbleGeometryMaterials scienceInstabilityRange (aeronautics)OpticsGeologyComposite materialPhysicsMathematicsTurbineThermodynamicsStatistics

Abstract

fetched live from OpenAlex

This paper presents measurements of separation-bubble transition over a range of surfaces with randomly distributed roughness elements. The tested roughness patterns represent the typical range of roughness conditions encountered on in-service turbine blades. Through these measurements, the effects of size and spacing of the roughness elements, and the tendency of the roughness pattern toward protrusions or depressions (skewness), on the inception location and rate of transition are evaluated. Increased roughness height, increased spacing of the roughness elements, and a tendency of the roughness pattern toward depressions (negative skewness) are observed to promote earlier transition inception. The observed effects of roughness spacing and skewness are found to be small in comparison to that of the roughness height. Variation in the dominant mode of instability in the separated shear layer is achieved through adjustment of the streamwise pressure distribution. The results provide examples for the extent of interaction between viscous and inviscid stability mechanisms.

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 score0.007
Threshold uncertainty score0.479

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 head0.003
GPT teacher head0.215
Teacher spread0.212 · 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

Citations58
Published2005
Admission routes2
Has abstractyes

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