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Record W2404687029 · doi:10.14356/kona.2017010

Changes in Pore Structures of Porous Beds When Subjected to Vertical Vibration

2016· article· en· W2404687029 on OpenAlexaff

Bibliographic record

VenueKONA Powder and Particle Journal · 2016
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsVibrationPorosityTortuosityMaterials scienceIntensity (physics)Discrete element methodComposite materialPorous mediumParticle (ecology)MechanicsAcousticsGeologyPhysicsOptics

Abstract

fetched live from OpenAlex

The objective of this study was to investigate the effect of vibration on critical pore structure parameters related to flow in porous beds. The discrete element method was used to simulate particle packing in porous beds of soybean subjected to vibration. The porous bed was simulated as an assembly of spherical particles with diameters randomly distributed between 5.5 mm and 7.5 mm. The simulated porous bed was subjected to vertical vibration at a fixed frequency of 15 Hz and multiple amplitudes from 0.5 to 4.0 mm, resulting in vibration intensities from 0.45g to 3.62g (g = gravitational acceleration). The location (coordinates) of each particle was tracked during vibration. Based on the simulated spatial arrangement of particles, critical flow-related parameters of the porous bed, including porosity, tortuosity, and pore throat width were calculated. It was found that vibration intensity of 1.81g resulted in the lowest porosity, whereas lower vibration intensity did not have enough energy to densify the bed and higher intensity produced less dense parking due to over-excitement. Local porosity fluctuated markedly during vibration, with a general trend of decrease as vibration progressed. Vibration noticeably affected the shape (tortuousness) of flow path. Tortuosity of the porous bed before vibration was higher (2 % to 9 %) than that after vibration. Vibration reduced the pore throat width by 18 % on average (from 3.3 mm before vibration to an average of 2.7 mm after vibration).

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.177
Threshold uncertainty score0.221

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 head0.011
GPT teacher head0.217
Teacher spread0.206 · 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 designBench or experimental
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

Citations9
Published2016
Admission routes1
Has abstractyes

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