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Record W4383109484 · doi:10.3390/ecp2023-14642

Hydrodynamics and Gas Hold-Up of a Gas–Liquid Coaxial Mixing System at Different Scales Containing a Non-Newtonian Fluid

2023· article· en· W4383109484 on OpenAlexafffund
Ali Rahimzadeh, Farhad Ein‐Mozaffari, Ali Lohi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMixing (physics)ImpellerCoaxialMechanicsComputational fluid dynamicsNewtonian fluidMixing patternsMaterials scienceAerationNon-Newtonian fluidMechanical engineeringPhysicsEngineeringWaste management

Abstract

fetched live from OpenAlex

The gas–liquid mixing phenomenon that occurs in a mixing tank containing a non-Newtonian fluid is an important process in many industrial applications, such as chemical and biochemical processing. The design and optimization of an aerated mixing tank with such characteristics is a challenging task. Most of these challenges are due to the non-Newtonian behavior of the fluid, which can lead to compartmentalization of the mixing tank and the formation of oxygen-segregated zones. These issues become more pronounced at larger scales. Therefore, the primary objective of this study was to identify the mixing dead zones and determine their impact on the overall mixing process in a coaxial mixing system at two different scales. This research focused on the evaluation of the hydrodynamics attained by a coaxial gas–liquid mixing tank through numerical and experimental methods. The study was conducted using computational fluid dynamics (CFD) and the electrical resistance tomography (ERT) method. The effects of the aeration rate, inner impeller speed, and rotating mode on the creation of dead zones were investigated.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.959
Threshold uncertainty score1.000

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 head0.006
GPT teacher head0.194
Teacher spread0.187 · 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.

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

Citations1
Published2023
Admission routes2
Has abstractyes

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