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Record W3037572068 · doi:10.1002/cjce.23833

Simulation of binary particle segregation for bubbling fluidized beds using polydispersed structure‐based drag model extended from a monodispersed model

2020· article· en· W3037572068 on OpenAlexvenueno aff
Jibin Jia, Hongzhong Li, Zheng Zou, Wenming Liu, Qingshan Zhu

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsDragMechanicsParticle (ecology)BubbleFluidizationDrag coefficientFluidized bedMaterials scienceBinary numberTwo-fluid modelThermodynamicsPhysicsMathematicsGeology

Abstract

fetched live from OpenAlex

Abstract A bubble structure‐based drag model developed for the monodispersed system has been extended to simulate bi‐dispersed bubbling fluidized beds. Both dense phase and dilute phase are considered to be comprised of two types of particles with different sizes. The derivation of the structure‐based drag coefficient for individual particle based on the force equilibrium principle is proved to be independent of the volume fractions ( ε si ) of particle i. The multi‐fluid model in the commercial software Ansys Fluent is employed to evaluate the polydispersed drag model for the segregation of binary gas‐particle flows in a bubbling fluidized bed. It is shown that the simulation results predicted by the new structure‐based drag model are in reasonable agreement with experimental data with a 6.34% root mean square error (RMSE). The new structure‐based model can capture the particle distribution at the top region of the fluidized bed well. The bubble behaviour can also be captured by the new model well.

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)0.000
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 score0.297
Threshold uncertainty score0.703

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 head0.028
GPT teacher head0.223
Teacher spread0.195 · 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 designSimulation or modeling
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

Citations10
Published2020
Admission routes1
Has abstractyes

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