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Record W2081334162 · doi:10.1002/cjce.20355

Investigation of turbulent mixing layer flow in a vertical water channel by particle image velocimetry (PIV)

2010· article· en· W2081334162 on OpenAlexvenueaboutno aff
Fude Guo, Bin Chen, Liejin Guo, Ximin Zhang

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2010
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsnot available
Fundersnot available
KeywordsVorticityReynolds numberParticle image velocimetryTurbulenceMechanicsPhysicsDimensionless quantityReynolds stressSplitter plateTurbulence kinetic energyVortexMixing (physics)GeometryMathematics

Abstract

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Abstract Turbulent mixing layer flow in a vertical water channel was experimentally investigated by particle image velocimetry (PIV). The mixing layer is produced by a specially designed insert plate placed in the channel with a low‐ and high‐speed side velocity ratio of 0.25. The Reynolds number based on the velocity difference of two streams and the spanwise vorticity thickness at the place where the mixing layer start merging ranges from 2184 to 14 672. The results show that there are large coherent vortex structures near the centreline of the mixing layer. Both instantaneous kinetic energy and spanwise vorticity always concentrate at the location where the coherent structures connect or meet each other. The normalised dimensionless Reynolds stresses and average spanwise vorticity show self‐similar, respectively, under different Reynolds numbers at the same cross‐section in the down streamwise direction. Every component of Reynolds stresses increases but the vorticity decreases with the downstream distance. For all Reynolds number, the peak values of mean vorticity in the streamwise direction appear the same decay speed. The splitter plane wake causes a negative peak of the mean vorticity where the mixing layer merges. The negative peak values of vorticity increase with the Reynolds number. The dimensionless negative peak values decrease exponentially with Reynolds number and reach a constant when the Reynolds number is large enough. Le débit de couche de mélange turbulent dans une conduite d'eau verticale a été analysé de façon expérimentale par vélocimétrie par images de particules (PIV). La couche de mélange est produite à l'aide d'une plaque d'insertion de conception particulière placée dans le conduit avec un rapport de vitesse latérale basse et élevée de 0,25. Le nombre de Reynolds fondé sur la différence de vitesse de deux courants et l'épaisseur du tourbillon dans le sens de l'envergure à l'endroit où la couche de mélange commence à fusionner se situe entre 2,184∼14,672. Les résultats indiquent qu'il existe de grandes nappes de tourbillons cohérentes près de la ligne centrale de la couche de mélange. À la fois l'énergie cinétique instantanée et le tourbillon dans le sens de l'envergure se concentrent toujours à l'endroit où les nappes cohérentes se rencontrent. Les tensions de Reynolds normalisées adimensionnelles et le tourbillon dans le sens de l'envergure moyen démontrent qu'ils sont autosimilaires respectivement sous différents nombres de Reynolds à la même section transversale en aval. Chaque élément des tensions de Reynolds s'accroît, mais le tourbillon diminue avec la distance en aval. Pour tout nombre de Reynolds, les valeurs de crête du tourbillon moyen dans la direction du flux semblent être de la même vitesse de désintégration. Le sillage plan de séparation cause une crête négative de tourbillon moyen lorsque la couche de mélange est fusionnée. Les valeurs de crête négatives du tourbillon augmentent avec le nombre de Reynolds. Les valeurs de crête négatives adimensionnelles diminuent de façon exponentielle avec le nombre de Reynolds et atteignent une constante lorsque le nombre de Reynolds est suffisamment grand. Can. J. Chem. Eng. © 2010 Canadian Society for Chemical Engineering

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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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.007

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)0.0000.000

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 head0.006
GPT teacher head0.166
Teacher spread0.160 · 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 designBench or experimental
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".

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Citations3
Published2010
Admission routes2
Has abstractyes

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