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Record W4256299899 · doi:10.1115/1.2812948

Numerical Study of Turbulent-Spot Development in a Separated Shear Layer

2008· article· en· W4256299899 on OpenAlexafffund
Brian McAuliffe, M. I. Yaras

Bibliographic record

VenueJournal of Turbomachinery · 2008
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of CanadaNational Research Council CanadaPratt and Whitney Canada
KeywordsFreestreamTurbulenceMechanicsPhysicsVortexK-epsilon turbulence modelLaminar flowBoundary layerReynolds numberInstabilityClassical mechanics

Abstract

fetched live from OpenAlex

The development of turbulent spots in a separation bubble under elevated freestream turbulence levels is examined through direct numerical simulation. The flow Reynolds number, freestream turbulence level, and streamwise pressure distribution are typical of the conditions encountered on the suction side of low-pressure turbine blades of gas-turbine engines. Based on the simulation results, the spreading and propagation rates of the turbulent spots and their internal structure are documented, and comparisons are made to empirical correlations that are used for predicting the transverse growth and streamwise propagation characteristics of turbulent spots. The internal structure of the spots is identified as a series of vortex loops that develop as a result of low-velocity streaks generated in the shear layer. A frequency that is approximately 50% higher than that of the Kelvin–Helmholtz instability is identified in the separated shear layer, which is shown to be associated with the convection of these vortex loops through the separated shear layer. While freestream turbulence is noted to promote breakdown of the laminar separated shear layer into turbulence through the generation of turbulent spots, evidence is found to suggest coexistence of the Kelvin–Helmholtz instability, including the possibility of breakdown to turbulence through this mechanism.

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 score0.278
Threshold uncertainty score0.607

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 head0.016
GPT teacher head0.235
Teacher spread0.219 · 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

Citations9
Published2008
Admission routes2
Has abstractyes

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