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Record W3122227910 · doi:10.1002/aic.17207

Turbulence damping above the cloud height in suspensions of concentrated slurries in stirred tanks

2021· article· en· W3122227910 on OpenAlexafffund
Inci Ayranci, Suzanne M. Kresta

Bibliographic record

VenueAIChE Journal · 2021
Typearticle
Languageen
FieldEngineering
TopicParticle Dynamics in Fluid Flows
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTurbulenceSlurryMechanicsImpellerRoot mean squareTurbulence kinetic energyMixing (physics)Constant (computer programming)Particle (ecology)PhysicsMaterials scienceThermodynamicsGeology

Abstract

fetched live from OpenAlex

Abstract Poor mixing in the clear liquid layer above the cloud height has been reported by several authors. This study uses LDV measurements to quantify turbulence above the cloud using a liquid level of 1.5 T to remove the barrier of a free surface at H = T . A D = T /3, down‐pumping PBT was used at an off‐bottom clearance of C = T /3. Three slurries were tested at impeller speeds 0.8, 1, and 1.2 N js . The change in turbulence was quantified using the normalized root mean square (RMS) of the fluctuating velocity summed and averaged over each radial traverse. A significant difference between the fluctuating values of the cloud height—minimum, average, and maximum—was observed. The turbulence decays until the maximum cloud height. Beyond that, it remains constant and near zero. The effects of both particle size and solids concentration prove to be important.

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)0.001
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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.008
GPT teacher head0.214
Teacher spread0.206 · 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

Citations8
Published2021
Admission routes2
Has abstractyes

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