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Record W3092504663 · doi:10.1080/00986445.2020.1823841

Comparison of mixing performance between stationary-baffle and moving-baffle batch oscillatory baffled columns via numerical modeling

2020· article· en· W3092504663 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueChemical Engineering Communications · 2020
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsLakehead University
Fundersnot available
KeywordsBaffleMechanicsMixing (physics)Computational fluid dynamicsFlow (mathematics)Oscillation (cell signaling)AmplitudeMaterials scienceChemistryPhysicsThermodynamicsOptics

Abstract

fetched live from OpenAlex

This paper presents a numerical comparison of the mixing performance between two configurations of batch oscillatory baffled column: the stationary-baffle column and the moving-baffle column. Computational fluid dynamic (CFD) software was used to investigate the effect of various oscillation conditions on fluid flow and mixing for both configurations. Population balance modeling was used to validate each column using droplet size distribution data for multiphase flow simulations. Column performance was evaluated in terms of power requirement, axial and radial flow dominance, and mixing time. The results suggest that better convective mixing occurs in stationary-baffle columns due to the increased axial velocity magnitudes produced for this configuration of column with respect to moving-baffle columns operating at the same oscillation frequency and amplitude.

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.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.252
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 head0.027
GPT teacher head0.251
Teacher spread0.223 · 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