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Record W4285393682 · doi:10.1002/cjce.24542

Numerical simulations of the spread of point‐source liquid spills in inclined and rolling rectangular packed beds

2022· article· en· W4285393682 on OpenAlexafffundvenue
Ion Iliuta, Faı̈çal Larachi, Wei Liu, Yong Luo, Seyed Mohammad Taghavi

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicHeat and Mass Transfer in Porous Media
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaChina Scholarship CouncilCanada Foundation for Innovation
KeywordsCapillary actionPacked bedMechanicsLiquid flowFlow (mathematics)Pressure gradientMaterials sciencePorous mediumCapillary pressurePorosityTwo-phase flowDispersion (optics)ViscosityResidence time (fluid dynamics)AmplitudeGeotechnical engineeringGeologyChemistryComposite materialChromatographyOpticsPhysics

Abstract

fetched live from OpenAlex

Abstract Liquid spreading in thin rectangular vertical/inclined and oscillating porous media was simulated using a two‐fluid dynamic model as a preliminary step in the design of fixed‐bed reactors dedicated to marine applications. The model assessed the influence of capillary pressure and mechanical dispersion forces arising from the spatial inhomogeneity of point‐source liquid injections in packed beds with different particle sizes, liquid and gas flow rates, liquid viscosity, static bed tilts, and rolling amplitudes and periods. In mildly static inclined beds, the lateral gravity force component and the capillary pressure force were the main factors affecting the liquid spreading. However, at considerable bed inclinations, the liquid phase accumulation in the lowermost regions of the packed bed tended to shrink the liquid spreading. Dynamic oscillatory evolutions of the liquid spills in the rolling bed enlarged the liquid spreading length as compared to the static vertical bed due to combined lateral liquid flow and increased liquid residence time.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.427
Threshold uncertainty score0.311

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 integrity0.0000.001
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 head0.006
GPT teacher head0.181
Teacher spread0.175 · 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

Citations0
Published2022
Admission routes3
Has abstractyes

Explore more

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