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Record W3003495868 · doi:10.1063/1.5134458

Equilibrium shapes of two-phase rotating fluid drops with surface tension

2020· article· en· W3003495868 on OpenAlexafffund
S. L. Butler

Bibliographic record

VenuePhysics of Fluids · 2020
Typearticle
Languageen
FieldEngineering
TopicSpacecraft and Cryogenic Technologies
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSurface tensionPhysicsDrop (telecommunication)MechanicsAngular momentumHeliumLiquid heliumPhase (matter)ScalingEquation of stateThermodynamicsClassical mechanicsAtomic physicsGeometry

Abstract

fetched live from OpenAlex

While rotating single phase fluid drops have been thoroughly investigated, the determination of the shape of a drop consisting of two immiscible liquid phases has not been previously considered. Recently, experiments using rotating micron-scale droplets of liquid helium have been carried out where the liquid can be in a normal or superfluid state depending on the isotope of helium used. Two phases can be present if the helium is a mixture of He3 and He4. Classical results have been very useful in aiding the analysis of single phase liquid helium drops and are similarly needed for two phase drops. In this contribution, the Navier–Stokes equations with surface tension are solved numerically using the finite-element method with surface tension effects on the inner and outer interfaces. The numerical models are time-dependent but are run to a steady state to determine equilibrium shapes. It is found that with an appropriate scaling of the density and surface tension coefficient, the relationships between the angular velocity and the angular momentum and of the outer surface dimensions with angular momentum become very similar to those of a single phase fluid for a broad range of parameters. However, the shapes of the inner drops vary significantly, particularly when the volume of the inner fluid is significantly less than that of the outer fluid. Increasing the relative magnitude of the interfacial surface tension coefficient or decreasing the relative density of the inner region leads to less deformation of the inner drop relative to the outer one.

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 score0.007

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 science0.0010.000
Research integrity0.0010.000
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 head0.019
GPT teacher head0.244
Teacher spread0.225 · 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

Citations6
Published2020
Admission routes2
Has abstractyes

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