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Record W4404831810 · doi:10.1017/jfm.2024.1022

Exchange flows and plug cementing

2024· article· en· W4404831810 on OpenAlexafffund
Scott Charabin, I.A. Frigaard

Bibliographic record

VenueJournal of Fluid Mechanics · 2024
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaPetroleum Technology Alliance Canada
KeywordsSpark plugMechanicsComputer scienceThermodynamicsPhysics

Abstract

fetched live from OpenAlex

We present the results of an experimental study of buoyancy-driven exchange flows in a vertical pipe, where the lower fluid is Newtonian of low viscosity and the upper fluid has a yield stress. The fluids are initially separated by a gate valve, opened at time $\hat {t}=0$ . The fluids are miscible, but away from the diffusive limit. For a sufficiently large ratio $Y$ , of the yield stress to the buoyancy stress, no sustained fluid motions arise: the flow is stable. For smaller $Y$ numbers an exchange flow results. Commonly, the less dense fluid penetrates upwards in a central finger, displacing the upper fluid downwards around the walls of the pipe. Three regimes are classified: helical finger, disconnected finger and slug flow. The transition between regimes is governed by increasing relevance of inertial to viscous stresses, in balancing buoyancy. The disconnected finger and slug flow regimes are associated with yielded fluid at the interface and early growth of instabilities. Helical fingers are viscous dominated and evolve slowly until late in the experiments. The scenarios studied represent an idealised set-up for the industrial process of plug cementing. The regimes identified are helpful for industrial process design.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.191
Teacher spread0.184 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2024
Admission routes2
Has abstractyes

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