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Record W1968083627 · doi:10.1002/aic.13913

In‐line jet mixing of liquid‐pulp‐fiber suspensions: Effect of fiber properties, flow regime, and jet penetration

2012· article· en· W1968083627 on OpenAlexafffund
Wisarn Yenjaichon, John R. Grace, Choon Jim Lim, Chad P. J. Bennington

Bibliographic record

VenueAIChE Journal · 2012
Typearticle
Languageen
FieldChemical Engineering
TopicRheology and Fluid Dynamics Studies
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPenetration (warfare)Mixing (physics)TurbulenceMechanicsMaterials scienceComposite materialFiberPhysicsEngineering

Abstract

fetched live from OpenAlex

Mixing effectiveness was determined experimentally for side jet injection into pipe flow for water and pulp suspensions for a range of fiber mass concentrations (0–3.0%), mainstream velocities (0.5–5.0 m/s), and side‐stream velocities (1.0–12.7 m/s). The mixing quality was measured in cross‐sectional planes along the pipe using electrical resistance tomography and quantified by a modified mixing index, derived from the coefficient of variation of conductivity. Mixing depended strongly on the flow regime and jet penetration. For turbulent flow, the criteria for in‐line jet mixing in water are applicable to the mixing in suspensions, with small differences likely due to differences in fiber network strength and influences of fiber‐turbulence interactions in modifying turbulent structures in the bulk. When a suspension flows as a plug, however, the mixing differs greatly from that in water, depending on the fiber network strength in the core of the pipe. © 2012 American Institute of Chemical Engineers AIChE J, 59: 1420–1430, 2013

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.148
Threshold uncertainty score0.390

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 head0.013
GPT teacher head0.237
Teacher spread0.225 · 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 designBench or experimental
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

Citations9
Published2012
Admission routes2
Has abstractyes

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