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Record W1980599245 · doi:10.1115/fedsm2014-21357

The Structure of Forward Facing Step Flows in Adverse Pressure Gradient

2014· article· en· W1980599245 on OpenAlexafffund
Weijie Shao, Martin Agelin‐Chaab

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsOntario Tech University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAdverse pressure gradientPressure gradientTurbulenceMechanicsParticle image velocimetryBoundary layerTurbulence kinetic energyPlane (geometry)Flow (mathematics)Reynolds numberPhysicsFlow separationMathematicsGeometry

Abstract

fetched live from OpenAlex

This paper reports an investigation of the effects of adverse pressure gradient on turbulent flows over forward facing step. Three adverse pressure gradients were created for this study using diverging channels. A particle image velocimetry technique was used to conduct measurements in the streamwise-wall-normal (x-y) planes at the mid-plane of test section at several locations downstream to 68 step heights. A Reynolds number of Reh = 4800 and δ/h = 4.7 were employed, where h is the mean step height and δ is the approach boundary layer thickness. The results include the mean flow and turbulence quantities as well as proper orthogonal decomposition analysis. The mean reattachment length obtained indicates that the adverse pressure gradient created in this study does not have significant effects on the reattachment length. The triple velocity correlations imply that there is negative transport of turbulence kinetic energy close to the wall and positive transport away from the wall. In addition to the physical insight, the high quality data reported are useful for assessing the ability of turbulence models to reproduce the behaviour of complex flows.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.171
Teacher spread0.168 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2014
Admission routes2
Has abstractyes

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