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Record W2993252628 · doi:10.1515/ijcre-2019-0139

Investigation of Hydrodynamic and Heat Transfer Characteristics of Gas-liquid Taylor flow in Square Microchannel

2019· article· en· W2993252628 on OpenAlexfundno aff
Zunlong Jin, Qiqi Sun, Dingbiao Wang, Yongqing Wang

Bibliographic record

VenueInternational Journal of Chemical Reactor Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicInnovative Microfluidic and Catalytic Techniques Innovation
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBubbleSlug flowMechanicsMicrochannelHeat transferTwo-phase flowHydraulic diameterThermodynamicsSuperficial velocityFlow (mathematics)Materials sciencePhysicsReynolds numberTurbulence

Abstract

fetched live from OpenAlex

Abstract Heat transfer and flow characteristics under air-water Taylor flow in a square microchannel with T-junction were investigated in this work. Different hydraulic diameters of models were discussed numerically by VOF method. Flow patterns such as bubbly flow, slug flow, annular flow and churn flow were identified by both numerical simulation and experimental methods. Simulation results including bubble formation process, bubble length, bubble velocity, void fraction and heat transfer fit well with literature data. The pressure differential of two sides in gas phase played an important role in bubble development. The gas and liquid superficial velocities were found to have a significant impact on bubble behavior. And the higher liquid viscosity would promote higher bubble velocity, also enhance heat transfer, but weaken the void fraction. The results showed a tiny but not ignorable effect of geometric dimensioning on bubble and liquid slug lengths. An appropriate correlation was proposed to estimate bubble length, and the deviation was −10 ~ + 15 %. By using moving frame of reference technique, the internal circulations inside the moving slugs were displayed more clearly.

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 score0.036
Threshold uncertainty score0.495

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 head0.006
GPT teacher head0.200
Teacher spread0.194 · 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

Citations4
Published2019
Admission routes1
Has abstractyes

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