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Record W2128849888 · doi:10.1002/aic.14235

Tomography images to analyze the deformation of the cavern in the continuous‐flow mixing of non‐Newtonian fluids

2013· article· en· W2128849888 on OpenAlexaff
Dineshkumar Patel, Farhad Ein‐Mozaffari, Mehrab Mehrvar

Bibliographic record

VenueAIChE Journal · 2013
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMixing (physics)ImpellerRheologyFlow (mathematics)MechanicsFlow visualizationNewtonian fluidVolumetric flow rateNon-Newtonian fluidMaterials scienceViscosityVolume (thermodynamics)Mechanical engineeringThermodynamicsEngineeringComposite materialPhysics

Abstract

fetched live from OpenAlex

Tomography, an efficient nonintrusive technique, was employed to visualize the flow in continuous‐flow mixing and to measure the cavern volume (V c ) in batch mixing. This study has demonstrated an efficient method for flow visualization in the continuous‐flow mixing of opaque fluids using two‐dimensional (2‐D) and 3‐D tomograms. The main objective of this study was to explore the effects of four inlet‐outlet configurations, fluid rheology (0.5–1.5% xanthan gum concentration), high‐velocity jet (0.317–1.660 m s −1 ), and feed flow rate (5.3 × 10 −5 −2.36 × 10 −4 m 3 s −1 ) on the deformation of the cavern. Dynamic tests were also performed to estimate the fully mixed volume (V fully mixed ) for the RT, A310, and 3AM impellers in a continuous‐flow mixing system, and it was found that V fully mixed was greater than V c . Incorporating the findings of this study into the design criteria will minimize the extent of nonideal flows in the continuous‐flow mixing of complex fluids and eventually improve the quality of end‐products. © 2013 American Institute of Chemical Engineers AIChE J , 60: 315–331, 2014

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.550
Threshold uncertainty score0.185

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.003
GPT teacher head0.185
Teacher spread0.182 · 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

Citations20
Published2013
Admission routes1
Has abstractyes

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