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Record W2589279147 · doi:10.1615/tsfp8.2030

PARTICLE-TURBULENCE INTERACTIONS IN THE PRESENCE OF A ROUGH WALL

2013· article· en· W2589279147 on OpenAlexaff
Godwin F.K. Tay, D. Kuhn, Mark F. Tachie

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicParticle Dynamics in Fluid Flows
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsTurbulenceMechanicsParticle image velocimetryParticle (ecology)Reynolds numberReynolds stressLaw of the wallTurbulence kinetic energyPhysicsIntensity (physics)Surface finishSurface roughnessFlow velocityMaterials scienceOpticsClassical mechanicsFlow (mathematics)ThermodynamicsComposite materialGeology

Abstract

fetched live from OpenAlex

Experiments were conducted over smooth and rough walls in a low Reynolds number horizontal turbulent channel flow laden with small (64 µm) glass particles. A particle image velocimetry technique was used to measure velocities of both the carrier fluid and particles. Various turbulent characteristics were examined to investigate the impact of wall roughness on the particle-turbulence interactions. The results show that particles increased the turbulent intensities near the wall, and reduced them in the outer layer, but these effects were dampened for the rough wall. On the contrary, particles increased the peak value of the Reynolds shear stress in the presence of the rough wall when compared to the unladen flow. Particle velocity fluctuation intensities matched those of the unladen fluid for the smooth wall, but the peak velocity fluctuation intensities were enhanced in the presence of wall roughness due to particle-wall collisions. The effect is larger for the streamwise velocity fluctuation intensity than the wall-normal velocity fluctuation intensity. The present results indicate that the particle motion is more responsive to the presence of the rough wall than the particle-laden fluid.

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 score0.198
Threshold uncertainty score0.217

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 head0.018
GPT teacher head0.249
Teacher spread0.231 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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