MétaCan
Menu
Back to cohort
Record W2049619750 · doi:10.1021/ie4024148

Computational Fluid Dynamics Modeling of Biomass Gasification in Circulating Fluidized-Bed Reactor Using the Eulerian–Eulerian Approach

2013· article· en· W2049619750 on OpenAlex

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

VenueIndustrial & Engineering Chemistry Research · 2013
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsToronto Metropolitan UniversityUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputational fluid dynamicsTurbulenceWood gas generatorMechanicsEulerian pathFluidized bedBiomass (ecology)Fluid dynamicsThermodynamicsTurbulence kinetic energyFluidizationEnvironmental scienceMaterials scienceChemistryPhysicsLagrangianGeology

Abstract

fetched live from OpenAlex

A three-dimensional CFD (computational fluid dynamics) steady-state model was established to simulate biomass gasification in a circulating fluidized-bed (CFB) reactor. The standard k –ε turbulence model was coupled with the kinetic theory of granular flow to simulate the hydrodynamics in the gasifier. The kinetics of homogeneous and heterogeneous reactions were studied and integrated with the equations of continuity, motion, and energy to describe the distributions of velocity, temperature, and concentration. The simulation results were compared to experimental data. The impacts of turbulence models, radiation model, water–gas shift (WGS) reaction, and equivalence ratio (ER) were investigated to present a reliable understanding of biomass gasification in a CFB reactor.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.405
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.091
GPT teacher head0.287
Teacher spread0.196 · 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