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Record W2595021815 · doi:10.1115/1.4036267

Experimental and Numerical Investigations on Withdrawal of Water-Capped Viscoplastic Fluid

2017· article· en· W2595021815 on OpenAlexaff
Amir H. Azimi, Jianan Cai, David Z. Zhu, N. Rajaratnam

Bibliographic record

VenueJournal of Fluids Engineering · 2017
Typearticle
Languageen
FieldChemical Engineering
TopicRheology and Fluid Dynamics Studies
Canadian institutionsUniversity of AlbertaLakehead University
Fundersnot available
KeywordsViscoplasticitySuspension (topology)Materials scienceMechanicsFlow (mathematics)Volumetric flow rateWater flowComposite materialGeotechnical engineeringGeologyThermodynamicsPhysicsMathematicsConstitutive equation

Abstract

fetched live from OpenAlex

Withdrawal of water-capped viscoplastic fluid was investigated using laboratory experimentation and numerical modeling. The viscoplastic fluid was modeled using a Laponite suspension, which was withdrawn by a vertical pipe intake. Variations of the Laponite–water interface and intake configurations were investigated in this study. The critical submergence, the depth of the intake in the Laponite layer when the upper water begins to withdraw, was studied under different experimental conditions, and the critical depths were measured for different flow rates. An empirical relationship was found between the withdrawal flow rate and the critical submergence. The averaged Laponite velocity was measured at different withdrawal stages to identify the critical stage. A series of numerical simulations were conducted to study the effect of intake structures so that a maximum amount of the Laponite suspension can be withdrawn before the water layer being withdrawn. It was found that a combination of a collar and a cone with an edge length to the intake diameter of 1.5 can increase the pumping duration by 16.7%. The installation of a collar or collar-cone setup can also decrease the disturbance in Laponite layer.

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 score0.394
Threshold uncertainty score0.497

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 head0.232
Teacher spread0.223 · 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

Citations3
Published2017
Admission routes1
Has abstractyes

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