Numerical simulation of thermal-solutal Marangoni convection in a shallow rectangular cavity with mutually perpendicular temperature and concentration gradients
Bibliographic record
Abstract
A series of three-dimensional numerical simulations have been carried out to examine the characteristics of thermal-solutal Marangoni convection in a rectangular cavity that is subjected to mutually perpendicular temperature and concentration gradients. In the simulations, the thermal Marangoni number MaT is selected as 0, 1, 3, and 7 × 104, but the solutal Marangoni number MaC is varied in order to be able to investigate the complex flow patterns and flow transitions. Results show that the flow is steady at relatively small MaC. Then, at this MaC value, we observe three types of steady flows as MaT increases, namely, a longitudinal surface flow, an oblique stripe flow, and a lateral surface flow. When MaC exceeds a critical value, the stability of the Marangoni flow is destroyed, and a three-dimensional oscillatory flow appears. For the oscillatory flow, the wave patterns of temperature and concentration fluctuations are highly dependent on the coupling of the thermal and solutal Marangoni effect. Two different propagation directions of wave patterns coexist on the free surface when the contributions of thermal and solutal flows are in the same order (i.e., MaC is approximately equal to MaT). In addition, a sudden drop in the wave frequency and a backward transition phenomenon from chaotic to oscillatory are also observed. For all the cases of the thermal Marangoni numbers, thermal-solutal Marangoni convection becomes chaotic at higher MaC values. The present study would provide more physical insights into industrial processes such as painting and drying.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".