COMPLEX HEAT TRANSFER AT DIRECTED CRYSTALLIZATION OF SEMITRANSPARENT MATERIALS
Bibliographic record
Abstract
The sensibility of thermal regimes at crystal-melt system to inner or outer parameters was studied for semitransparent media by the numerical simulation of complex heat transfer. A model of radiation-convective and radiation-conductive heat transfer was developed. Advanced features of the model, such as dynamic evolution of interface, were realized by implementation of user-defined functions. The 2D axisymmetric model is limited geometrically to the cylindrical crystal-melt system since heat regimes and temperature gradients in the area near crystallization front are the most important. Combined effect of radiation, convective and conductive heat transfer mechanisms on the formation of temperature fields and heat flows, position and shape of the crystallization front and distribution of the temperature gradients in the crystal-melt system have been examined for the oxide and alkali-halide classes of semitransparent materials at different growth conditions, considering selectivity of their absorption. Analysis of the results allowed developing the recommendations for approximation of the effects of radiation and convection heat transfer and their interaction. This allows justification of several possible simplifying approaches at development of the numerical models of crystal growth furnaces, including on-line models for operative control of the growth process.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".