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Record W2156879890 · doi:10.1021/ie100442e

Potential of Microchannel Flow for Agglomerate Breakage

2010· article· en· W2156879890 on OpenAlex
J.J. Derksen, Dmitry Eskin

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

VenueIndustrial & Engineering Chemistry Research · 2010
Typearticle
Languageen
FieldEngineering
TopicLattice Boltzmann Simulation Studies
Canadian institutionsSchlumberger (Canada)University of Alberta
Fundersnot available
KeywordsAgglomerateBreakageMicrochannelLaminar flowLattice Boltzmann methodsMechanicsMaterials scienceFlow (mathematics)ChemistryComposite materialNanotechnologyPhysics

Abstract

fetched live from OpenAlex

Direct simulations of laminar solid−liquid flow in microchannels with full resolution of the solid−liquid interfaces have been performed. The solids phase consists of simple agglomerates, assembled of monosized, spherical particles. The flow of the interstitial liquid is solved with the lattice-Boltzmann method. Solids and fluid dynamics are two-way coupled. The simulations keep track of the flow-induced forces in the agglomerates. The effects of agglomerate type (doublets, triplets, and quadruplets), solids loading, and channel geometry on (the statistics of the) flow and collision-induced forces has been investigated. By comparing these forces with agglomerate strength, we would be able to assess the potential of microchannels as agglomerate breakage devices.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.064
Threshold uncertainty score0.897

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.054
GPT teacher head0.312
Teacher spread0.258 · 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