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Record W2012733049 · doi:10.1002/cjce.21688

Flow regime transition identification in three phase co‐current bubble columns

2012· article· en· W2012733049 on OpenAlexvenueaboutno aff
Sanjeev Kumar, Nannuri Srinivasulu, Prabhat Munshi, A. Khanna

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2012
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsnot available
FundersHindustan Petroleum
KeywordsBubblePhase transitionAnalytical Chemistry (journal)Superficial velocitySlurryLiquefactionMaterials scienceThermodynamicsMechanicsChemistryFlow (mathematics)ChromatographyPhysics

Abstract

fetched live from OpenAlex

Abstract Bubble columns have wide applications in absorption, bio‐reactions, catalytic slurry reactions, coal liquefaction; and are simple to operate, have less operating costs; provide good heat and mass transfer. Experiments have been performed for identifying transition regime in a 15 cm diameter bubble column with liquid phase as water and air as the gas phase. Glass beads of mean diameter 35 µm have been used as solid phase. The superficial gas velocity is in the range 0 ≤ U g ≤ 16.3 cm/s and superficial liquid velocity in the range of 0 ≤ U l ≤ 12.26 cm/s. Solid loading up to 9% (w/v) has been used. Pressure signals have been measured using differential pressure transducers (DPTs) at four different axial locations. Classical analysis (Wallis approach and Zuber–Findlay approach), Statistical analysis and Fractal analysis have been used for regime transition identification. Statistical analysis and Fractal analysis have shown almost the same transition points for all the liquid and gas velocities. Effect of solid concentration, liquid velocity and gas velocity over transition regime has also been studied. As the solid concentration is increased it has insignificant effect over transition regime for lower values (<1%), while transition values decrease for higher solid concentration (>1%). © 2012 Canadian Society for Chemical Engineering

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 score0.339
Threshold uncertainty score0.375

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 head0.010
GPT teacher head0.209
Teacher spread0.199 · 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

Citations10
Published2012
Admission routes2
Has abstractyes

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