MétaCan
Menu
Back to cohort
Record W3040032959 · doi:10.1002/aic.16653

Solid hold‐up measurement in a jet‐impactor assisted fluidized bed using gamma‐ray densitometry

2020· article· en· W3040032959 on OpenAlexafffund
Hamed Nasri Lari, Majid Rasouli⃰, Jamal Chaouki, Jason R. Tavares

Bibliographic record

VenueAIChE Journal · 2020
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for InnovationSigma Xia
KeywordsFluidized bedFluidizationAgglomerateJet (fluid)MechanicsMaterials scienceRange (aeronautics)Work (physics)Momentum (technical analysis)Thermal diffusivityComposite materialPhysicsThermodynamics

Abstract

fetched live from OpenAlex

Abstract A jet‐impactor assisted‐fluidized bed (JIAFB) for continuous de‐agglomeration of nanopowder agglomerates was presented in previous work. Therein, a jet caused agglomerates to impinge up an impactor, where they would break. However, efficient impactor positioning will be dictated by particle momentum: the product of solid concentration and velocity must be highest. Herein, the variation of solid hold‐up was measured in a fluidized bed of Fe 2 O 3 nanoparticles using gamma‐ray densitometry. Behaviour was compared under minimum fluidization and bubbling regimes, over a wide range of jet velocities (0–200 m s −1 ). A new line‐decomposition approach allowed mapping local solid distribution across seven axial and five radial positions, tangibly demonstrating how increasing the gas velocity enhanced the fluidization quality by increasing axial solid diffusivity. Conversely, increasing jet velocity locally decreased solid hold‐up in the jet‐affected zone, and brought about inhomogeneities. With this information in hand, jet‐to‐impactor distance was optimized and validated experimentally.

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 categoriesMeta-epidemiology (narrow)
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 score0.122
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.071
GPT teacher head0.283
Teacher spread0.212 · 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.

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

Citations3
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueAIChE JournalSame topicGranular flow and fluidized bedsFrench-language works237,207