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Record W2106602701 · doi:10.1002/cjce.21952

Comparison between numerical results and PIV experimental data for gas–solid flow in ducts

2013· article· en· W2106602701 on OpenAlexvenueno aff
Rodrigo Koerich Decker, Monica C. Betto, Dirceu Noriler, Henry França Meier

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2013
Typearticle
Languageen
FieldEngineering
TopicParticle Dynamics in Fluid Flows
Canadian institutionsnot available
FundersFundação de Amparo à Pesquisa e Inovação do Estado de Santa Catarina
KeywordsTurbulenceInviscid flowMechanicsTurbulence kinetic energyParticle image velocimetryDragComputational fluid dynamicsFlow (mathematics)Drag coefficientMaterials scienceThermodynamicsPhysics

Abstract

fetched live from OpenAlex

To analyse the behaviour of gas–solid flow, the experimental, nonintrusive technique of particle image velocimetry (PIV) was applied. This technique enables the acquisition of information regarding the microscopic velocity field in a bidimensional plane. The physical experiments were conducted in the vertical and horizontal sections of a test facility. The operating conditions at the inlet were 140 m 3 /h of air and a 40 g/m 3 mass load ratio, which are typical conditions for dilute flows. Solid‐phase catalyst particles with a Sauter mean diameter of 56.7 µm, similar to those applied in the petroleum industry for FCC systems, were used. Experimental radial profiles for the axial velocity data of the solid phase were compared with the respective numerical results obtained by the CFD code in FLUENT 13. Turbulence in the gas phase was modelled with a k − ϵ model, and second‐order versions of this model were used for turbulence in the solid phase. Turbulence was induced by the drag force with direct numerical simulation (inviscid model) and was modelled using the kinetic theory for granular material with the equilibrium model (KTGF equilibrium). The results showed that both the inviscid and KTGF models produced good agreement with the experimental data for dilute gas–solid flow in ducts, particularly in regions of developed flow.

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 score0.287
Threshold uncertainty score0.491

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 head0.032
GPT teacher head0.268
Teacher spread0.236 · 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

Citations7
Published2013
Admission routes1
Has abstractyes

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