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Record W2967064107 · doi:10.1021/acs.iecr.9b03656

Effects of Contact Angle on Single and Multiscale Bubble Motions in the Aluminum Reduction Cell

2019· article· en· W2967064107 on OpenAlexafffund
Meijia Sun, Roozbeh Mollaabbasi, Baokuan Li, Houshang Alamdari, Mario Fafard, Seyed Mohammad Taghavi

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2019
Typearticle
Languageen
FieldEngineering
TopicMetallurgical Processes and Thermodynamics
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of ChinaPrecursory Research for Embryonic Science and TechnologyCanada Foundation for Innovation
KeywordsVolume of fluid methodBubbleContact angleMechanicsAnodeMaterials scienceWork (physics)Transient (computer programming)Solid surfaceVolume (thermodynamics)PhysicsComposite materialElectrodeThermodynamicsChemical physicsFlow (mathematics)

Abstract

fetched live from OpenAlex

This work deals with the effects of the contact angle as an essential factor in bubble motions underneath downward-facing surfaces of the anode in the aluminum reduction cell. First, a transient three-dimensional (3D) mathematical model is employed to study a single bubble motion with the volume-of-fluid (VOF) method. In addition, a transient 3D model coupled with the discrete phase model and the VOF method is employed to track the microdispersed (∼micrometer or ∼millimeter) and macroscale (∼centimeter) bubbles in various contact angles. A discrete continuum transition model is developed to link the micro- to macroscale of bubbles and analyze the multiscale bubbles co-existing beneath the anode. The predicted gas coverage gives a reasonable match with the experimental data in the literature. The bubble release frequency decreases with increasing the contact angle provided that the contact angle is smaller than 90°, whereas the opposite occurs when the contact angles are larger than 90°.

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 score0.083
Threshold uncertainty score0.400

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 integrity0.0000.001
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 head0.030
GPT teacher head0.256
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

Citations11
Published2019
Admission routes2
Has abstractyes

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