Numerical Modelling Of Liquid Film Deposition from Moving Meniscus in a Capillary Tube
Bibliographic record
Abstract
Meniscus motion in a capillary tube is a very common type of flow in thermal management devices such as oscillating and pulsating heat pipes.It is well known, that the thin film deposited on the wall, which is due to liquid shear force, plays a major role in the heat and mass transfer of those devices.This study focuses on the hydrodynamics of this flow using a CFD axisymmetric model of a 1mm diameter capillary, with the approach of the volume of fluid (VoF).The CFD-VOF approach is tuned to capture the liquid film deposition from a receding meniscus at different constant velocities.Due to the high required grid resolution, the moving overset mesh technique is used.In doing so, a fine-meshed domain consists of meniscus slides over the background domain with a coarse mesh.By using this technique, the number of cells and computational time are reduced considerably in comparison with the regular meshing approach.With water as a working fluid, the numerical results for the liquid film thickness, at different velocities, compare well with experimental data from the literature.The simulations also show that at a higher capillary number, the axial location where the film thickness becomes constant moves away from the meniscus nose.The shear stress distribution indicates higher values near the meniscus compared to uniform film and liquid plug zones which is due to the interface curvature in this zone.Also, a recirculating flow was observed within the liquid film left behind the receding meniscus which could have favorable effects in terms of heat and mass transfer.The present work on hydrodynamics is the first step toward complete modeling of an oscillating meniscus with mass and heat transfer inside the capillaries.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".