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Record W2905131889 · doi:10.1002/cjce.23428

Characterization of fluid mixing in a closed container under horizontal vibrations

2018· article· en· W2905131889 on OpenAlexvenueno aff
Xiaobin Zhan, Zhibin Sun, Yu He, Baojun Shen, Tielin Shi, Xiwen Li

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicCharacterization and Applications of Magnetic Nanoparticles
Canadian institutionsnot available
FundersChina Postdoctoral Science FoundationNational Natural Science Foundation of China
KeywordsMixing (physics)MechanicsBreakupInstabilityVibrationContainer (type theory)Materials sciencePhysicsComposite materialAcoustics

Abstract

fetched live from OpenAlex

The mixing characteristics of two initially stratified miscible fluids in a closed container vibrated horizontally are studied numerically by a validated computational fluid dynamics model. The flow characteristics and interfacial dynamics in the closed vibrating container are analyzed, and the effects of vibration parameters and gravity on mixing efficiency are evaluated both quantitatively and qualitatively. The results show that interfacial instability occurs in the closed container, which leads to interfacial deformation and breakup and quickens mixing. Two distinct stages of the mixing process can be observed in the closed vibrating container. During the macro‐mixing, an increasingly sharp gradient of the concentration field is generated. During the micro‐mixing, the mixing process is dominated by the diffusion of the concentration field and ends up with a nearly homogeneous mixture. The intensification of mixing process by vibration is most pronounced in the absence of static gravity. The gravity can suppress the interfacial instability and retard the mixing process. Over the range of conditions investigated in this paper, vibration is an efficient approach for mixing miscible liquids in a closed container.

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 score0.042
Threshold uncertainty score0.267

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 head0.007
GPT teacher head0.181
Teacher spread0.174 · 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

Citations6
Published2018
Admission routes1
Has abstractyes

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