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Record W2065002488 · doi:10.1063/1.4868489

Drag coefficients and rotational behavior of spheres descending through liquids along an inclined wall at high Reynolds numbers

2014· article· en· W2065002488 on OpenAlexaff
Leigh Wardhaugh, Michael C. Williams

Bibliographic record

VenuePhysics of Fluids · 2014
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsReynolds numberSPHERESPhysicsDrag coefficientDragSlippingLift (data mining)MechanicsSlip (aerodynamics)GeometryThermodynamicsTurbulenceMathematics

Abstract

fetched live from OpenAlex

Spherical particles immersed in liquids were observed in their descent along a glass wall inclined at various angles α, over a range of particle-based Reynolds numbers (Rep) extending to high values (15 < Rep < 50 000), rarely reported in such flows. Plastic, ceramic, and metal spheres were used, characterized as to surface roughness and their friction coefficients against the glass. Liquids were selected to achieve a viscosity variation by a factor of 300, as well as having widely differing chemical natures. A drag coefficient (Cp) used to correlate sphere velocity data was found to define a near-universal curve Cp (Rep) over the entire Rep-range, provided that spheres rolled down the wall without slipping, and here there was no need to accommodate roughness effects of solid-to-solid friction explicitly. This correlation was especially good for Rep > 103. For lower Rep, deviations appeared in systematic fashion, falling below the universal curve when slip was present. Several unexpected features were observed: (a) a threshold angle, α0, needed before sphere motion could begin; (b) spheres lifting off from the wall at high Rep, but always at the same maximum angle αm = 74°; and (c) prior to lift-off, a buzzing sound (not reported previously) for which explanations are offered.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.821

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 head0.012
GPT teacher head0.237
Teacher spread0.225 · 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 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".

Quick stats

Citations7
Published2014
Admission routes1
Has abstractyes

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