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Record W4396733638 · doi:10.1002/cjce.25304

Mass transfer and bubble hydrodynamics in stirred tank with multiple properties fluid via a <scp>CFD‐PBM</scp> method

2024· article· en· W4396733638 on OpenAlexvenueno aff
Hao Chen, Zhe Chen, Xu‐Qing Wang, Xiu‐Guang Yi, Xi‐Bao Zhang, Zheng‐Hong Luo

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsnot available
Fundersnot available
KeywordsComputational fluid dynamicsBubbleMass transferMechanicsAir bubbleFluid simulationPhysicsMaterials science

Abstract

fetched live from OpenAlex

Abstract The performance of a stirred bioreactor was evaluated in this study in terms of the bubble hydrodynamics and the mass transfer efficiency, using a non‐viscous Newtonian fluid of water, a viscous non‐Newtonian fluid of xanthan, and a viscous non‐Newtonian fluids of xanthan with dispersed soybean powder, respectively. The computational fluid dynamics–population balance model (CFD‐PBM) method coupled with the viscosity model and the mass transfer model is established to simulate the gas–liquid mass transfer process and bubble size distribution in the stirred bioreactor. The results demonstrate that the rheological properties of the fluid play an important role in determining the gas holdup, the mass transfer efficiency, and the bubble size distribution. Viscosity of the fluid exhibits a negative impact on gas–liquid mass transfer rate and gas holdup. Moreover, by properly adjusting the operating conditions such as the stirrer speed, it is possible to modulate the gas dispersion and mass transfer rate in the reactor.

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 score0.408
Threshold uncertainty score0.596

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 integrity0.0000.001
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 head0.005
GPT teacher head0.161
Teacher spread0.156 · 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

Citations7
Published2024
Admission routes1
Has abstractyes

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