Characterization of the thermal and solutal Marangoni flows of opposite directions developing in a cylindrical liquid bridge under zero gravity
Bibliographic record
Abstract
Numerical simulations of the thermo-solutal Marangoni convection developing in a Si–Ge liquid bridge of a floating-zone system have been performed under zero gravity. Half of the liquid bridge was considered as the three-dimensional (3D) computational domain. In this system, the solutal Marangoni convection develops in the direction opposite to the thermal Marangoni convection along the free surface in the bridge, i.e., the thermal Marangoni number, MaT, is negative and the solutal Marangoni number, MaC, is positive. Since the SiGe melt is a low-Prandtl number (Pr = 6.37 × 10−3) and high-Schmidt number (Sc = 14.0) liquid, the temperature field is almost independent of the convective flow and the concentration field determines the transport structures. When MaC is larger than −MaT, the concentration pattern is steady and two-dimensional (2D) axisymmetric. When MaC is smaller than −MaT, we predict two kinds of flow transitions with the increase in |MaT|. If MaC is sufficiently large (MaC ≳ 530), as |MaT| increases, the flow changes from a 2D-steady pattern to a 3D-chaotic behavior at moderate |MaT| (1050 ≲ |MaT| ≲ 2800). We also predict that a second transition and an oscillatory rotating flow occur as |MaT| increases further. The flow becomes 3D-steady at smaller MaC (MaC ≲ 360) with no transition, and the azimuthal wavenumber (m) decreases with increasing |MaT|. Furthermore, the thermo-solutal Marangoni convection in this system can be suppressed almost completely when MaC is approximately equal to −MaT (MaC ≈ −MaT) and the flow becomes periodically stable with weak fluctuations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".