The Influence of Surface Deformation on Thermocapillary Flow Instabilities in Low Prandtl Melting Pools with Surfactants
Bibliographic record
Abstract
Heat and fluid flow in low-Prandtl number melt pools (Pr = O(10 -1 )) during laser processing of materials are sensitive to the prescribed boundary conditions, and the responses are highly nonlinear. Previous studies have shown that fluid flow in melt pools with surfactants can be unstable at high Marangoni numbers. In numerical simulations of molten metal flow in melt pools, surface deformation and its influence on the energy absorbed by the material are often neglected. However, this simplifying assumption may reduce the level of accuracy of numerical predictions with surface deformations. In the present study, we carry out three-dimensional numerical simulations to realise the effects of surface deformations on thermocapillary flow instabilities in laser melting of a metallic alloy with surfactants. Our computational model is based on the finite-volume method and utilises the volumeof-fluid (VOF) method for gas-metal interface tracking. Additionally, we employ a dynamically adjusted heat source model and discuss its influence on numerical predictions of the melt pool behaviour. Our results demonstrate that including free surface deformations in numerical simulations enhances the predicted flow instabilities and, thus, the predicted solid-liquid interface morphologies.
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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".