Three-Dimensional Modeling of the Rotation Effect on the Growth of Ge₁₋ˣSiˣ, by the Traveling Solvent Method
Bibliographic record
Abstract
The travelling solvent method known as TSM is a process used to produce pure and homogeneous crystals. The TSM process has been tested on many alloys producing uniform and uncontaminated crystal products. A three-dimensional numerical simulation for the growth of Ge1-xSix by the travelling solvent method under axial rotation has been modelled. In this model a mesh sensitivity analysis has been carried out to find an optimum mesh which provides accurate results while saving computational time, The full Navier-Stokes equations together with the energy, mass transport and continuity equations were solved numerically using the finite element technique. The application of crucible rotation to the travelling solvent method is an attempt to control the buoyancy induced convection throughout the melt and to suppress the three-dimensional characteristics of unsteady heat flow. The application of different rotational speeds on the solvent has also been investigated. These different speeds of rotation were shown to have a considerable effect on the buoyancy induced flow. The solute distribution throughout the melt was also affected substantially. Taking these two factors into account plays a crucial role in the crystal growth process. The speed of rotation was found to have a significant effect on the intensity of the convective flow in the melt and an optimal rotational speed was encountered.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".