Software for Simulation of Diffusion Process in Polycrystalline Material
Bibliographic record
Abstract
Understanding of diffusion processes in polycrystalline solids is of importance for studying processes like oxidation, precipitation, creep, superplastic forming, annealing and many other processes. In this paper, we will introduce our latest software that is able to simulate the diffusion process in poly and nano-crystalline solids. The diffusion process is simulated based on Random Walk theory. The diffusion matrix can be computer generated or obtained from the experimental measurement using Orientation Imaging Microscopy. The software describes microstructure and incorporates the effects of the material’s texture, grain size and shape, grain boundary character distribution, statistical information on CSL boundary distributions, contribution from triple junctions and interfaces, the trapping of diffusing atoms and interaction of atoms with second phases and voids. A built-in database of diffusivities of various diffusing species and the user friendly interface make the software easy to use. The software is also applicable to thin films and multilayer structures. The output of simulation can be presented as a normalized concentration profile, a two-dimensional contour map of diffusing species, and also using many other statistical representations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.006 |
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".