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Record W2560800663 · doi:10.1021/acs.iecr.6b03627

Controlling the Quality of Two-Way Euler/Lagrange Numerical Modeling of Bubbling and Spouted Fluidized Beds Dynamics

2016· article· en· W2560800663 on OpenAlexaff
Manuel Bernard, Éric Climent, Anthony Wachs

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2016
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsNozzleMechanicsFluidizationFluidized bedComputer simulationSPHERESEuler equationsClassical mechanicsPhysicsThermodynamics

Abstract

fetched live from OpenAlex

We numerically simulate three-dimensional fluidized beds of monodisperse spheres using a two-way Euler/Lagrange method. Particles trajectories are tracked in a Lagrangian way, and particles collisions are computed by a soft sphere model. The fluid conservation equations are written in a classical Eulerian fashion and are locally averaged on cells 1 order of magnitude larger than particles. We detail the equations of the model and their numerical implementation. We study the influence of numerical parameters and simulation domain size on computed results in a biperiodic fluidized bed. We then validate the model against theoretical results for a bubbling fluidized bed and against experimental data for a single- and multi-nozzle spouted bed. Finally, we investigate the influence of the Coulomb friction coefficient magnitude on a single-nozzle spouted bed dynamics in order to emphasize the importance of tangential friction in such processes.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.088
GPT teacher head0.325
Teacher spread0.238 · 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 source (direct Gemma or distilled Codex), 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

Citations17
Published2016
Admission routes1
Has abstractyes

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