Modelling of Particle Pinning in Dual Scale Using Phase Field Method
Bibliographic record
Abstract
Modelling the evolution of structures in polycrystalline materials with distributions of fine particles requires integration of multiple length scales. Grain boundaries interact with particles on the scale of the particle diameters. The particle pinning force controls the kinetics of grain growth. Grain diameters can be several orders of magnitude larger than particles. In this work, a methodology is proposed to combine two sets of phase field models at different length scales. At the smaller scale, the effect of particles on movement of a single grain boundary is modelled in a small domain with a high grid resolution. The interface moves in an array of particles with specified shape and size distributions. The average pinning force exerted by the particles, is calculated from the interface velocity. Then, an effective driving force model is developed to incorporate the obtained pinning force into the large scale where grain growth simulations are preformed. In this model, the particle pinning force is subtracted from the driving force in the phase field formulation. In this effective formulation, particles are not resolved in the calculation grid. Therefore, with the larger numerical mesh, modelling of larger systems is possible. Kinetics of grain growth was studied with 2 dimensional simulations. Keywords: Phase field modelling, particle pinning, grain growth
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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".