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Record W2903548012 · doi:10.1002/cjce.23412

Numerical simulation of bubbling fluidization using a local bubble‐structure‐dependent drag model

2018· article· en· W2903548012 on OpenAlexvenueno aff
Shaohua Du, Lijun Liu

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsDragFluidizationMechanicsDrag coefficientBubbleTwo-fluid modelFlow (mathematics)InterphaseLocal BubbleThermodynamicsPhysicsMaterials scienceFluidized bed

Abstract

fetched live from OpenAlex

Abstract The meso‐scale flow structure in gas‐solid bubbling fluidization has significant effects on hydrodynamics and interphase heat and mass transfer. However, traditional homogeneous drag models neglect the meso‐scale structure and fail to yield a reasonable prediction of the hydrodynamics in bubbling fluidization. In this study, the gas‐solid system within a grid cell combines three subsystems in order to take the meso‐scale structure into account. Based on this resolution, a local bubble‐structure‐dependent drag model is established. In addition, a homogeneous interphase drag correlation based on direct numerical simulation is coupled with the novel drag model to describe the interphase interaction when the flow structure is homogeneous. Then, the proposed drag model is incorporated into two fluid models to simulate the hydrodynamics in bubbling fluidization with Geldart A and A/B particles. The predicted profiles of the time‐averaged solid volume fraction are compared with experimental data to validate the effectiveness of the novel drag model. The results show that the average relative error of the solid volume fraction is reduced from over 30 to ∼10 % by replacing homogeneous drag models with the novel drag model.

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: none
Teacher disagreement score0.536
Threshold uncertainty score0.493

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 head0.009
GPT teacher head0.197
Teacher spread0.189 · 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

Citations6
Published2018
Admission routes1
Has abstractyes

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