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Record W4320718258 · doi:10.1139/cjp-2022-0093

Numerical study of the effect of heat-flow coupling on interface instability based on front tracking

2023· article· en· W4320718258 on OpenAlexvenueno aff
Yao Liu, Mengjun Yao, Zhaoqing Ke, Kang Chen, Wei Wu

Bibliographic record

VenueCanadian Journal of Physics · 2023
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Thin Films
Canadian institutionsnot available
Fundersnot available
KeywordsInstabilityMechanicsMarangoni effectHeat transferPhysicsHeat fluxSurface tensionThermodynamicsFront (military)Coupling (piping)Materials scienceComposite materialMeteorology

Abstract

fetched live from OpenAlex

The interfacial instability of two-phase immiscible fluids in inclined tubes at constant temperature boundary conditions is numerically investigated by the front-tracking method. By analyzing the effects of inclination angle φ, Marangoni number (Ma), and Rayleigh number (Ra) on the interfacial instability, the interaction law between unstable interface fluctuation and heat transfer is studied. The results show that the larger the inclination angle, the easier the interface is destabilized, and the heat transfer at the interface will also decrease. In the comparison of inclination angles of 0°, 30°, 45°, and 60°, the heat transfer is more stable at 45°. The heat flux between fluids decreases with the increase of Ma number, and Ma number has little effect on the interfacial fluctuation. It mainly affects the interfacial morphology by changing the surface tension gradient at the interface, which is mainly reflected in the end of the convolution interface. The larger the Ma number, the more inward the interface develops. Ra number has an obvious inhibitory effect on the interfacial instability. The effect of Ra number on heat-flow transfer at the interface shows alternating changes. In the initial stage, the heat transfer between fluids in the inclined tube is greater than that in the horizontal tube, but in the later stage, the heat transfer is less than that in the horizontal tube.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.317

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 head0.010
GPT teacher head0.216
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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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