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Record W4407606054 · doi:10.1016/j.powtec.2025.120763

Meso-scale numerical analysis of the role of Van der Waals adhesion and static friction in fluidized beds of fine solids

2025· article· en· W4407606054 on OpenAlex
Youssef Badran, Dorian Dupuy, Bruno Blais, Vincent Moureau, Renaud Ansart, Jamal Chaouki, Olivier Simonin

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuePowder Technology · 2025
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsPolytechnique Montréal
FundersGrand Équipement National De Calcul IntensifPolytechnique MontréalMinistère de l'Enseignement supérieur, de la Recherche et de l'Innovation
Keywordsvan der Waals forceAdhesionScale (ratio)Materials scienceFluidizationMechanicsFluidized bedComposite materialChemistryThermodynamicsPhysicsMolecule

Abstract

fetched live from OpenAlex

This article explores the effect of Van der Waals force and static friction on the fluidization of fine solids using CFD-DEM simulations. The results show that both Van der Waals adhesion and static friction contribute to the pressure-drop hysteresis phenomenon. These results also demonstrate that to predict the homogeneous expansion of the bed across the range of velocities from the minimum required for fluidization to the minimum for bubbling, it is necessary to take into account the Van der Waals adhesion. The generated CFD-DEM dataset can guide the development of solid stress closures for two-fluid models to incorporate the effects of Van der Waals adhesion and static friction on fluidization hydrodynamics, allowing for the prediction of hysteresis in bed pressure drop at the macro-scale.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.075
Threshold uncertainty score0.321

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.003
GPT teacher head0.208
Teacher spread0.205 · 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