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Record W2088095153 · doi:10.1080/01457630701328031

Optical Measurement of Void Fraction and Bubble Size Distributions in a Microchannel

2007· article· en· W2088095153 on OpenAlexaff
Hideo Ide, Ryuji Kimura, Masahiro Kawaji

Bibliographic record

VenueHeat Transfer Engineering · 2007
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Boiling Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMicrochannelBubbleMaterials scienceSpark plugSuperficial velocityPorosityTwo-phase flowMechanicsPlug flowOpticsAnalytical Chemistry (journal)Flow (mathematics)Composite materialThermodynamicsPhysicsChemistryChromatographyNanotechnology

Abstract

fetched live from OpenAlex

An optical measurement system was developed to investigate gas-liquid two-phase flow characteristics in a circular microchannel of 100 μ m diameter. By using multiple optical fibers and infrared photodiodes, void fraction, gas and liquid plug lengths, and their velocities were measured successfully. The probes responded to the passage of gas and liquid phases through the microchannel adequately so that the time-average void fraction could be obtained from the time fraction for each phase. Also, by cross-correlating the signals from two neighboring probes, the interface velocity representing gas plug velocity or ring-film propagation velocity depending on the flow pattern could be computed. Within the ranges of superficial gas and liquid velocities covered in the experiments (j L = 0.2∼0.4 m/s and j G = 0∼5 m/s), the gas plug length was found to increase with the increasing superficial gas velocity, but the liquid plug length was found to decrease sharply as the superficial gas velocity was increased; thus, the total length of the gas-liquid plug unit decreased with the superficial gas velocity.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.011
GPT teacher head0.204
Teacher spread0.193 · 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 source (direct Gemma or distilled Codex), 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

Citations40
Published2007
Admission routes1
Has abstractyes

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