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Record W3095669883 · doi:10.1139/cjp-2020-0355

The effect of fluctuating pressure gradient on the coalescence of Taylor bubble

2020· article· en· W3095669883 on OpenAlexvenueno aff
Ying Zhang, Hui Gao, Qiang Liu, Mengjun Yao, Jin Bao, Meng Xu

Bibliographic record

VenueCanadian Journal of Physics · 2020
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Boiling Studies
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsBubbleCoalescence (physics)PhysicsMechanicsReynolds numberAmplitudeOscillation (cell signaling)Pressure gradientOpticsTurbulenceChemistry

Abstract

fetched live from OpenAlex

The oscillatory coalescence phenomenon of the Taylor bubble flow subjected to the action of a fluctuating pressure gradient in a pulsating heat pipe was investigated using the front tracking method (FTM). The effects of amplitude and frequency of fluctuating pressure, bubble size, Reynolds number (Re), and Weber number (We) on the bubble coalescence process were studied. The results demonstrated that the lower the pulsation frequency, the longer the period of bubble oscillation, which could provide enough time for bubbles to drain and promoted the coalescence of the bubbles. The larger Euler number (Eu) was, the more easily bubbles coalesced. On the contrary, when Eu was small, the bubbles were slow to coalesce. The size of the top bubble and the bottom bubble had different effects on coalescence. An increase of the length of the top bubble (L t ) was beneficial to bubble coalescence while increasing the length of the bottom bubble (L b ) restrained bubble coalescence. Time required for bubble coalescence increased with the increase of Re and decreased with increasing We.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.831
Threshold uncertainty score0.163

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 head0.013
GPT teacher head0.194
Teacher spread0.181 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

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