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Record W2064884282 · doi:10.1002/cjce.5450780516

Simulation of fine particle formation by precipitation using computational fluid dynamics

2000· article· en· W2064884282 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2000
Typearticle
Languageen
FieldMaterials Science
TopicCrystallization and Solubility Studies
Canadian institutionsnot available
FundersNational Science Foundation
KeywordsComputational fluid dynamicsMicromixingTurbulenceMechanicsFluentMixing (physics)Turbulence kinetic energyPrecipitationDissipationFluid dynamicsMaterials scienceThermodynamicsPhysicsMeteorologyViscosity

Abstract

fetched live from OpenAlex

Abstract The 4‐environment generalized micromixing (4‐EGM) model is applied to describe turbulent mixing and precipitation of barium sulfate in a tubular reactor. The model is implemented in the commercial computational fluid dynamics (CFD) software Fluent. The CFD code is first used to solve for the hydrodynamic fields (velocity, turbulence kinetic energy, turbulent energy dissipation). The species concentrations and moments of the crystal size distribution (CSD) are then computed using user‐defined transport equations. CFD simulations are performed for the tubular reactor used in an earlier experimental study of barium sulfate precipitation. The 4‐EGM CFD results are shown to compare favourably to CFD results found using the presumed beta PDF model. The latter has previously been shown to yield good agreement with experimental data for the mean crystal size at the outlet of the tubular reactor.

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 categoriesnone
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 score0.083
Threshold uncertainty score0.192

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 head0.013
GPT teacher head0.226
Teacher spread0.213 · 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