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Record W2085268729 · doi:10.1021/ie8012083

Simulating the Dynamics of Gas−Solid Flows in a Multichannel Microcirculating Fluidized Bed

2008· article· en· W2085268729 on OpenAlexaff
Yining Wang, Faı̈çal Larachi, Shantanu Roy

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2008
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsComputational fluid dynamicsMonolithFluidized bedWork (physics)Flow (mathematics)ParticulatesMultiphase flowCombustionChemical looping combustionMechanicsProcess engineeringMaterials scienceEnvironmental scienceMechanical engineeringChemistryThermodynamicsPhysicsEngineering

Abstract

fetched live from OpenAlex

The dynamics of gas−solid flows and distribution in monolithic multichannel microcirculating fluidized-bed reactors was analyzed using a computational fluid dynamics (CFD) modeling approach. A 2D Euler−Euler multiphase model with the kinetic theory of granular flow has been solved for the detailed monolithic packing geometry. The assemblage of monolithic structured packings with through-flow gas-particulate flows is globally considered in the simulation to capture the dominant mechanisms contributing to the final overall aero/granular dynamics. Due to the complex nature of the interactions between gas and particulate phases and the stationary monolith backbone, one of the challenges in the design and operation of the monolith reactors is the prevention of flow maldistribution. The work presented in this paper forms the basis for a comprehensive reactor-scale model for exploring the intriguing possibilities that the proposed process intensification concept offers for chemical reactions of energy/environmental relevance such as biomass gasification and combustion.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.026
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.067
GPT teacher head0.295
Teacher spread0.227 · 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

Citations5
Published2008
Admission routes1
Has abstractyes

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