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Record W4295942119 · doi:10.1002/cjce.24660

Experimental investigation on dynamic behaviour of bubbles emerging from micro‐capillary orifice in a flow channel

2022· article· en· W4295942119 on OpenAlexvenueno aff
Chenglong Guo, Wang Yin Chai, Qian Lyu, Ming Min, Wei Wang, Lin Liang

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsnot available
FundersNatural Science Foundation of Hebei Province
KeywordsBubbleMechanicsBody orificeVolumetric flow rateCapillary actionFlow (mathematics)Materials scienceVolume (thermodynamics)Liquid bubbleInrush currentThermodynamicsMechanical engineeringPhysicsEngineeringElectrical engineeringComposite material

Abstract

fetched live from OpenAlex

Abstract The flow phenomenon of liquid with bubbles is widespread in various industrial fields, which determines the mass transfer characteristics of the equipment. In this work, the dynamic behaviour of bubbles emerging from micro‐capillary orifice in a flow channel was studied by a visualization experiment, while the effects of gas flow rate and liquid flow rate on these processes of bubble growth, departure, and inrush were explored. The experimental results showed that one bubble formation cycle can be divided into three stages: Waiting, departure, and inrush, as well as the dynamic behaviour of bubble emerging from micro‐capillary orifice in a flow channel, were significantly affected by gas flow rate and liquid flow rate. At a higher gas flow rate, the growth time and the departure time were shorter, as well as the departure volume of the leading bubble and the inrush volume of the trailing bubble were smaller, while the transverse longitudinal ratio fluctuated more violently, and the swing amplitude of the bubble centroid was greater. With an increasing liquid flow rate, the growth time, the departure time, and the inrush time shortened, while the departure volume of the leading bubble decreased and the fluctuation of the bubble centroid weakened. These findings are conducive to improving the performance of the equipment by optimizing the design of the aerator to regulate the dynamic behaviour of bubbles.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.215
Threshold uncertainty score0.518

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 head0.174
Teacher spread0.167 · 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 designSimulation or modeling
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

Citations1
Published2022
Admission routes1
Has abstractyes

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