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Record W2270072695 · doi:10.1002/aic.15130

Local hydrodynamic parameters of bubble column reactors operating with non‐Newtonian liquids: Experiments and models development

2015· article· en· W2270072695 on OpenAlexafffund
Amin Esmaeili, Christophe Guy, Jamal Chaouki

Bibliographic record

VenueAIChE Journal · 2015
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBubbleNon-Newtonian fluidBubble column reactorMechanicsMixing (physics)Newtonian fluidMaterials scienceThermodynamicsChromatographyChemistryPhysicsGas bubble

Abstract

fetched live from OpenAlex

The effects of liquid phase rheology on the local hydrodynamics of bubble column reactors operating with non‐Newtonian liquids are investigated. Local bubble properties, including bubble frequency, bubble chord length, and bubble rise velocity, are measured by placing two in‐house made optical fiber probes at various locations within a bubble column reactor operating with different non‐Newtonian liquids. It was found that the presence of elasticity can noticeably increase the bubble frequency but decreases the bubble chord length and its rise velocity. The radial profiles of bubble frequency, bubble chord length, and bubble rise velocity are shown to be relatively flat at low superficial gas velocity while they become parabolic at high superficial gas velocity. Moreover, the bubble size and gas holdup are correlated with respect to dimensionless groups by considering the ratio between dynamic moduli of viscoelastic liquids. The novel proposed correlations are capable of predicting the experimental data of bubble size and gas holdup within a mean absolute percentage error of 9.3% and 10%, respectively. © 2015 American Institute of Chemical Engineers AIChE J , 62: 1382–1396, 2016

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 score0.067
Threshold uncertainty score0.534

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 head0.018
GPT teacher head0.208
Teacher spread0.191 · 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

Citations23
Published2015
Admission routes2
Has abstractyes

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