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Record W3131484092 · doi:10.1002/cjce.24091

Experiments and simulation of liquid drainage in oscillating packed beds under roll and heave motions

2021· article· en· W3131484092 on OpenAlexafffundvenue
Jian Zhang, Faı̈çal Larachi, Ion Iliuta, Seyed Mohammad Taghavi

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicElectrical and Bioimpedance Tomography
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSlosh dynamicsMechanicsDrainageAccelerationGeotechnical engineeringGeologyAmplitudeSubmarine pipelineOscillation (cell signaling)Saturation (graph theory)Porous mediumPorosityPhysicsChemistryClassical mechanics

Abstract

fetched live from OpenAlex

Abstract Replication in the laboratory of offshore sea/swell movements is an essential component in the study and design of unit operations commissioned for seagoing vessels. Whether these perturbations affect the internal fluid flow structure inside reactors on‐board ships is yet to be investigated. This study focuses on understanding the phenomenon of liquid drainage of a porous medium submitted to hexapod‐controlled column oscillations. Liquid saturation transients, drainage rate, and drainage velocity were monitored using wire‐mesh sensors in porous media, under forced tilting (roll) and non‐tilting (heave) oscillations. A 3D/transient Eulerian two‐fluid model was also solved in the robot moving reference frame to reproduce the accelerated liquid drainage measured in oscillating columns. Such acceleration was highlighted by unveiling the relationship between the roll amplitude, the wall‐region greater permeability, and the uneven crosswise liquid distribution. Finally, the moving‐frame fictitious acceleration forces stemming from column oscillations were found to be negligibly small compared with the gravity both under heave and roll excitations.

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 score0.067
Threshold uncertainty score0.251

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 head0.009
GPT teacher head0.207
Teacher spread0.197 · 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

Citations2
Published2021
Admission routes3
Has abstractyes

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