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Record W1964008173 · doi:10.1615/jpormedia.v3.i2.10

Gas-Liquid Countercurrent Flows Through Packed Towers

2000· article· en· W1964008173 on OpenAlexaff
Shijie Liu, Jun Long

Bibliographic record

VenueJournal of Porous Media · 2000
Typearticle
Languageen
FieldEngineering
TopicHeat and Mass Transfer in Porous Media
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCountercurrent exchangePacked bedMechanicsPorous mediumVolume (thermodynamics)Flow (mathematics)PorosityTwo-phase flowMaterials scienceThermodynamicsMultiphase flowLiquid phasePressure dropChromatographyChemistryPhysics

Abstract

fetched live from OpenAlex

The volume-averaged governing equations for multiphase flows in porous media proposed by Liu (1999a) are applied to the study of gas-liquid countercurrent flows in packed towers. Model parameters are proposed for two-phase flows by extending the model of saturated single-phase flows in porous media. Numerical solutions are obtained for developed axisymmetrical flows in packed columns. Flow velocity profiles of both phases, pressure drops, and liquid holdup distributions are obtained. All of the predictions agree well with published experimental results. It is thus demonstrated that the volume-averaged governing equations are satisfactory for describing two-phase flows in packed columns.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.231
Teacher spread0.218 · 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

Citations10
Published2000
Admission routes1
Has abstractyes

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