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Record W2305519643 · doi:10.1002/aic.15235

Investigating the dynamics of cylindrical particles in a rotating drum using multiple radioactive particle tracking

2016· article· en· W2305519643 on OpenAlexafffund
Majid Rasouli⃰, Olivier Dubé, François Bertrand, Jamal Chaouki

Bibliographic record

VenueAIChE Journal · 2016
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTracking (education)Particle (ecology)MechanicsTransverse planeFlow (mathematics)Dynamics (music)PhysicsWork (physics)Boundary (topology)Materials scienceClassical mechanicsThermodynamicsEngineeringAcousticsMathematics

Abstract

fetched live from OpenAlex

The behavior of granular flows inside rotating drums is an ongoing area of research. Only a few studies have investigated non‐spherical particles despite the fact that particle shape is known to have a significant impact on flow behavior. In addition, the experimental techniques limit the interpretation of the results of these studies. In this work, we compared the flow behavior of cylindrical and spherical particles using the multiple radioactive particle tracking technique to capture the positions and orientations of cylindrical particles simultaneously. We analyzed two important components of the transverse flow dynamics, that is, the boundary between the active and passive layers, and the velocity profile on the free surface. For the cylindrical particles, two general models are proposed to calculate the velocity profiles on the free surface and the effective particle sizes in the active and passive layers. © 2016 American Institute of Chemical Engineers AIChE J , 62: 2622–2634, 2016

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)0.001
metaresearch head score (Gemma)0.001
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
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 head0.026
GPT teacher head0.242
Teacher spread0.216 · 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".

Quick stats

Citations44
Published2016
Admission routes2
Has abstractyes

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