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Record W1970458133 · doi:10.1002/cjce.22005

Parameters measurement of hydrodynamics and CFD simulation in multi‐stage bubble columns

2014· article· en· W1970458133 on OpenAlexvenueno aff
Haibo Jin, Yichen Lian, Ling Qin, Suohe Yang, Guangxiang He, Zhiwu Guo

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2014
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsnot available
Fundersnot available
KeywordsComputational fluid dynamicsBubbleMechanicsTrayStage (stratigraphy)Plate columnSuperficial velocityFlow (mathematics)Column (typography)ChemistryMaterials scienceChromatographyGeologyGeometryMechanical engineeringPhysicsEngineeringMathematics

Abstract

fetched live from OpenAlex

The effect of superficial gas velocity and structural parameters of plate (perforated plate and tongue plate) on the hydrodynamics parameters of column are studied in a multi‐stage bubble column, with 2000 mm height and 282 mm diameter of the multi‐stage bubble column, by using the electrical resistance tomography (ERT) technique and computational fluid dynamics (CFD). According to the results, ERT and CFD techniques can be well applied to the measurement and estimation of gas–liquid two‐phase processes. The gas holdups, the radial distribution of gas holdup and gas cap height strongly depend on the operating condition and tray structure. By increasing the superficial gas velocity, the gas holdup in the multi‐stage bubble column increases. With a decrease of the open area ratio, the hole diameter and tongue angle, the gas holdups and gas cap height below the perforated plate increase. The addition of the perforated plate enhances the flow behaviour of gas–liquid in bubble column. The CFD can then aid the prediction of gas cap height and gas holdup in a multi‐stage bubble column.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.319

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 head0.015
GPT teacher head0.189
Teacher spread0.174 · 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

Citations12
Published2014
Admission routes1
Has abstractyes

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