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Record W4404412076 · doi:10.1021/acs.iecr.4c02880

Modeling of a Moving Reacting Carbon Char Particle Using Macropore-Resolved and Porous Media Approaches

2024· article· en· W4404412076 on OpenAlexafffund
Andrés Arriagada, Mario Toledo, Robert E. Hayes, Petr A. Nikrityuk

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2024
Typearticle
Languageen
FieldEngineering
TopicElectrohydrodynamics and Fluid Dynamics
Canadian institutionsUniversity of Alberta
FundersFondo de Financiamiento de Centros de Investigación en Áreas PrioritariasFondo Nacional de Desarrollo Científico y TecnológicoNatural Sciences and Engineering Research Council of CanadaAgencia Nacional de Investigación y DesarrolloUniversidad Técnica Federico Santa María
KeywordsCharMacroporePorosityParticle (ecology)Carbon fibersChemical engineeringPorous mediumMaterials scienceChemistryPyrolysisComposite materialOrganic chemistryGeologyCatalysisEngineering

Abstract

fetched live from OpenAlex

This work presents a comparative computational study of the heterogeneous combustion of a single spherical carbon char particle in hot atmospheres with different O 2 concentrations using a porous media model (PMM) and macropore-resolved simulations (PRS). A two-dimensional axisymmetric computational fluid dynamics model (2D CFD) in a pseudo-steady-state approach (PSS) is performed. In both cases, a semiglobal kinetic scheme is implemented, including three heterogeneous and three homogeneous chemical reactions. Several Reynolds numbers (Re in = 10, 50, and 100), inflow gas temperatures ( T in = 1100–2000 K), and inlet mass fractions of oxygen (Y O 2,in = 0.05 and 0.11) are assessed. Results show good agreement between PMM and PRS, especially for T in = 2000 K, where the deviation in the values for the species was not higher than 8.3%. Maximum deviation for CO 2 was 1.54% when evaluating the maximum values for the species concentration. In addition, the effects of the gasifying conditions, oxidative regime, and tortuosity on the partial oxidation process are discussed.

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)0.001
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 score0.402
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 head0.106
GPT teacher head0.282
Teacher spread0.176 · 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.

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

Citations2
Published2024
Admission routes2
Has abstractyes

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